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# docstyle-ignore
INSTALL_CONTENT = """
# Datasets installation
! pip install datasets transformers
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/datasets.git
"""
notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]
default_branch_name = "main"
version_prefix = ""
| datasets/docs/source/_config.py/0 | {
"file_path": "datasets/docs/source/_config.py",
"repo_id": "datasets",
"token_count": 118
} | 55 |
# Create a dataset
Sometimes, you may need to create a dataset if you're working with your own data. Creating a dataset with 🤗 Datasets confers all the advantages of the library to your dataset: fast loading and processing, [stream enormous datasets](stream), [memory-mapping](https://huggingface.co/course/chapter5/4?fw=pt#the-magic-of-memory-mapping), and more. You can easily and rapidly create a dataset with 🤗 Datasets low-code approaches, reducing the time it takes to start training a model. In many cases, it is as easy as [dragging and dropping](upload_dataset#upload-with-the-hub-ui) your data files into a dataset repository on the Hub.
In this tutorial, you'll learn how to use 🤗 Datasets low-code methods for creating all types of datasets:
* Folder-based builders for quickly creating an image or audio dataset
* `from_` methods for creating datasets from local files
## Folder-based builders
There are two folder-based builders, [`ImageFolder`] and [`AudioFolder`]. These are low-code methods for quickly creating an image or speech and audio dataset with several thousand examples. They are great for rapidly prototyping computer vision and speech models before scaling to a larger dataset. Folder-based builders takes your data and automatically generates the dataset's features, splits, and labels. Under the hood:
* [`ImageFolder`] uses the [`~datasets.Image`] feature to decode an image file. Many image extension formats are supported, such as jpg and png, but other formats are also supported. You can check the complete [list](https://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/packaged_modules/imagefolder/imagefolder.py#L39) of supported image extensions.
* [`AudioFolder`] uses the [`~datasets.Audio`] feature to decode an audio file. Audio extensions such as wav and mp3 are supported, and you can check the complete [list](https://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/packaged_modules/audiofolder/audiofolder.py#L39) of supported audio extensions.
The dataset splits are generated from the repository structure, and the label names are automatically inferred from the directory name.
For example, if your image dataset (it is the same for an audio dataset) is stored like this:
```
pokemon/train/grass/bulbasaur.png
pokemon/train/fire/charmander.png
pokemon/train/water/squirtle.png
pokemon/test/grass/ivysaur.png
pokemon/test/fire/charmeleon.png
pokemon/test/water/wartortle.png
```
Then this is how the folder-based builder generates an example:
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/folder-based-builder.png"/>
</div>
Create the image dataset by specifying `imagefolder` in [`load_dataset`]:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon")
```
An audio dataset is created in the same way, except you specify `audiofolder` in [`load_dataset`] instead:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("audiofolder", data_dir="/path/to/folder")
```
Any additional information about your dataset, such as text captions or transcriptions, can be included with a `metadata.csv` file in the folder containing your dataset. The metadata file needs to have a `file_name` column that links the image or audio file to its corresponding metadata:
```
file_name, text
bulbasaur.png, There is a plant seed on its back right from the day this Pokémon is born.
charmander.png, It has a preference for hot things.
squirtle.png, When it retracts its long neck into its shell, it squirts out water with vigorous force.
```
To learn more about each of these folder-based builders, check out the and <a href="https://huggingface.co/docs/datasets/image_dataset#imagefolder"><span class="underline decoration-yellow-400 decoration-2 font-semibold">ImageFolder</span></a> or <a href="https://huggingface.co/docs/datasets/audio_dataset#audiofolder"><span class="underline decoration-pink-400 decoration-2 font-semibold">AudioFolder</span></a> guides.
## From local files
You can also create a dataset from local files by specifying the path to the data files. There are two ways you can create a dataset using the `from_` methods:
* The [`~Dataset.from_generator`] method is the most memory-efficient way to create a dataset from a [generator](https://wiki.python.org/moin/Generators) due to a generators iterative behavior. This is especially useful when you're working with a really large dataset that may not fit in memory, since the dataset is generated on disk progressively and then memory-mapped.
```py
>>> from datasets import Dataset
>>> def gen():
... yield {"pokemon": "bulbasaur", "type": "grass"}
... yield {"pokemon": "squirtle", "type": "water"}
>>> ds = Dataset.from_generator(gen)
>>> ds[0]
{"pokemon": "bulbasaur", "type": "grass"}
```
A generator-based [`IterableDataset`] needs to be iterated over with a `for` loop for example:
```py
>>> from datasets import IterableDataset
>>> ds = IterableDataset.from_generator(gen)
>>> for example in ds:
... print(example)
{"pokemon": "bulbasaur", "type": "grass"}
{"pokemon": "squirtle", "type": "water"}
```
* The [`~Dataset.from_dict`] method is a straightforward way to create a dataset from a dictionary:
```py
>>> from datasets import Dataset
>>> ds = Dataset.from_dict({"pokemon": ["bulbasaur", "squirtle"], "type": ["grass", "water"]})
>>> ds[0]
{"pokemon": "bulbasaur", "type": "grass"}
```
To create an image or audio dataset, chain the [`~Dataset.cast_column`] method with [`~Dataset.from_dict`] and specify the column and feature type. For example, to create an audio dataset:
```py
>>> audio_dataset = Dataset.from_dict({"audio": ["path/to/audio_1", ..., "path/to/audio_n"]}).cast_column("audio", Audio())
```
## Next steps
We didn't mention this in the tutorial, but you can also create a dataset with a loading script. A loading script is a more manual and code-intensive method for creating a dataset, but it also gives you the most flexibility and control over how a dataset is generated. It lets you configure additional options such as creating multiple configurations within a dataset, or enabling your dataset to be streamed.
To learn more about how to write loading scripts, take a look at the <a href="https://huggingface.co/docs/datasets/main/en/image_dataset#loading-script"><span class="underline decoration-yellow-400 decoration-2 font-semibold">image loading script</span></a>, <a href="https://huggingface.co/docs/datasets/main/en/audio_dataset"><span class="underline decoration-pink-400 decoration-2 font-semibold">audio loading script</span></a>, and <a href="https://huggingface.co/docs/datasets/main/en/dataset_script"><span class="underline decoration-green-400 decoration-2 font-semibold">text loading script</span></a> guides.
Now that you know how to create a dataset, consider sharing it on the Hub so the community can also benefit from your work! Go on to the next section to learn how to share your dataset.
| datasets/docs/source/create_dataset.mdx/0 | {
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"repo_id": "datasets",
"token_count": 2167
} | 56 |
# Load a dataset from the Hub
Finding high-quality datasets that are reproducible and accessible can be difficult. One of 🤗 Datasets main goals is to provide a simple way to load a dataset of any format or type. The easiest way to get started is to discover an existing dataset on the [Hugging Face Hub](https://huggingface.co/datasets) - a community-driven collection of datasets for tasks in NLP, computer vision, and audio - and use 🤗 Datasets to download and generate the dataset.
This tutorial uses the [rotten_tomatoes](https://huggingface.co/datasets/rotten_tomatoes) and [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) datasets, but feel free to load any dataset you want and follow along. Head over to the Hub now and find a dataset for your task!
## Load a dataset
Before you take the time to download a dataset, it's often helpful to quickly get some general information about a dataset. A dataset's information is stored inside [`DatasetInfo`] and can include information such as the dataset description, features, and dataset size.
Use the [`load_dataset_builder`] function to load a dataset builder and inspect a dataset's attributes without committing to downloading it:
```py
>>> from datasets import load_dataset_builder
>>> ds_builder = load_dataset_builder("rotten_tomatoes")
# Inspect dataset description
>>> ds_builder.info.description
Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005.
# Inspect dataset features
>>> ds_builder.info.features
{'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None),
'text': Value(dtype='string', id=None)}
```
If you're happy with the dataset, then load it with [`load_dataset`]:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("rotten_tomatoes", split="train")
```
## Splits
A split is a specific subset of a dataset like `train` and `test`. List a dataset's split names with the [`get_dataset_split_names`] function:
```py
>>> from datasets import get_dataset_split_names
>>> get_dataset_split_names("rotten_tomatoes")
['train', 'validation', 'test']
```
Then you can load a specific split with the `split` parameter. Loading a dataset `split` returns a [`Dataset`] object:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("rotten_tomatoes", split="train")
>>> dataset
Dataset({
features: ['text', 'label'],
num_rows: 8530
})
```
If you don't specify a `split`, 🤗 Datasets returns a [`DatasetDict`] object instead:
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("rotten_tomatoes")
DatasetDict({
train: Dataset({
features: ['text', 'label'],
num_rows: 8530
})
validation: Dataset({
features: ['text', 'label'],
num_rows: 1066
})
test: Dataset({
features: ['text', 'label'],
num_rows: 1066
})
})
```
## Configurations
Some datasets contain several sub-datasets. For example, the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset has several sub-datasets, each one containing audio data in a different language. These sub-datasets are known as *configurations*, and you must explicitly select one when loading the dataset. If you don't provide a configuration name, 🤗 Datasets will raise a `ValueError` and remind you to choose a configuration.
Use the [`get_dataset_config_names`] function to retrieve a list of all the possible configurations available to your dataset:
```py
>>> from datasets import get_dataset_config_names
>>> configs = get_dataset_config_names("PolyAI/minds14")
>>> print(configs)
['cs-CZ', 'de-DE', 'en-AU', 'en-GB', 'en-US', 'es-ES', 'fr-FR', 'it-IT', 'ko-KR', 'nl-NL', 'pl-PL', 'pt-PT', 'ru-RU', 'zh-CN', 'all']
```
Then load the configuration you want:
```py
>>> from datasets import load_dataset
>>> mindsFR = load_dataset("PolyAI/minds14", "fr-FR", split="train")
```
## Remote code
Certain datasets repositories contain a loading script with the Python code used to generate the dataset.
Those datasets are generally exported to Parquet by Hugging Face, so that 🤗 Datasets can load the dataset fast and without running a loading script.
Even if a Parquet export is not available, you can still use any dataset with Python code in its repository with `load_dataset`.
All files and code uploaded to the Hub are scanned for malware (refer to the Hub security documentation for more information), but you should still review the dataset loading scripts and authors to avoid executing malicious code on your machine. You should set `trust_remote_code=True` to use a dataset with a loading script, or you will get a warning:
```py
>>> from datasets import get_dataset_config_names, get_dataset_split_names, load_dataset
>>> c4 = load_dataset("c4", "en", split="train", trust_remote_code=True)
>>> get_dataset_config_names("c4", trust_remote_code=True)
['en', 'realnewslike', 'en.noblocklist', 'en.noclean']
>>> get_dataset_split_names("c4", "en", trust_remote_code=True)
['train', 'validation']
```
<Tip warning=true>
In the next major release, the new safety features of 🤗 Datasets will disable running dataset loading scripts by default, and you will have to pass `trust_remote_code=True` to load datasets that require running a dataset script.
</Tip>
| datasets/docs/source/load_hub.mdx/0 | {
"file_path": "datasets/docs/source/load_hub.mdx",
"repo_id": "datasets",
"token_count": 1685
} | 57 |
# Share a dataset using the CLI
At Hugging Face, we are on a mission to democratize good Machine Learning and we believe in the value of open source. That's why we designed 🤗 Datasets so that anyone can share a dataset with the greater ML community. There are currently thousands of datasets in over 100 languages in the Hugging Face Hub, and the Hugging Face team always welcomes new contributions!
Dataset repositories offer features such as:
- Free dataset hosting
- Dataset versioning
- Commit history and diffs
- Metadata for discoverability
- Dataset cards for documentation, licensing, limitations, etc.
This guide will show you how to share a dataset that can be easily accessed by anyone.
<a id='upload_dataset_repo'></a>
## Add a dataset
You can share your dataset with the community with a dataset repository on the Hugging Face Hub.
It can also be a private dataset if you want to control who has access to it.
In a dataset repository, you can host all your data files and [configure your dataset](./repository_structure#define-your-splits-in-yaml) to define which file goes to which split.
The following formats are supported: CSV, TSV, JSON, JSON lines, text, Parquet, Arrow, SQLite.
Many kinds of compressed file types are also supported: GZ, BZ2, LZ4, LZMA or ZSTD.
For example, your dataset can be made of `.json.gz` files.
On the other hand, if your dataset is not in a supported format or if you want more control over how your dataset is loaded, you can write your own dataset script.
When loading a dataset from the Hub, all the files in the supported formats are loaded, following the [repository structure](./repository_structure).
However if there's a dataset script, it is downloaded and executed to download and prepare the dataset instead.
For more information on how to load a dataset from the Hub, take a look at the [load a dataset from the Hub](./load_hub) tutorial.
### Create the repository
Sharing a community dataset will require you to create an account on [hf.co](https://huggingface.co/join) if you don't have one yet.
You can directly create a [new dataset repository](https://huggingface.co/login?next=%2Fnew-dataset) from your account on the Hugging Face Hub, but this guide will show you how to upload a dataset from the terminal.
1. Make sure you are in the virtual environment where you installed Datasets, and run the following command:
```
huggingface-cli login
```
2. Login using your Hugging Face Hub credentials, and create a new dataset repository:
```
huggingface-cli repo create your_dataset_name --type dataset
```
Add the `-organization` flag to create a repository under a specific organization:
```
huggingface-cli repo create your_dataset_name --type dataset --organization your-org-name
```
### Clone the repository
3. Install [Git LFS](https://git-lfs.github.com/) and clone your repository (refer to the [Git over SSH docs](https://huggingface.co/docs/hub/security-git-ssh) if you prefer cloning through SSH):
```
# Make sure you have git-lfs installed
# (https://git-lfs.github.com/)
git lfs install
git clone https://huggingface.co/datasets/namespace/your_dataset_name
```
Here the `namespace` is either your username or your organization name.
### Prepare your files
4. Now is a good time to check your directory to ensure the only files you're uploading are:
- The data files of the dataset
- The dataset card `README.md`
- (optional) `your_dataset_name.py` is your dataset loading script (optional if your data files are already in the supported formats csv/jsonl/json/parquet/txt). To create a dataset script, see the [dataset script](dataset_script) page.
### Upload your files
You can directly upload your files to your repository on the Hugging Face Hub, but this guide will show you how to upload the files from the terminal.
5. It is important to add the large data files first with `git lfs track` or else you will encounter an error later when you push your files:
```
cp /somewhere/data/*.json .
git lfs track *.json
git add .gitattributes
git add *.json
git commit -m "add json files"
```
6. (Optional) Add the dataset loading script:
```
cp /somewhere/data/load_script.py .
git add --all
```
7. Verify the files have been correctly staged. Then you can commit and push your files:
```
git status
git commit -m "First version of the your_dataset_name dataset."
git push
```
Congratulations, your dataset has now been uploaded to the Hugging Face Hub where anyone can load it in a single line of code! 🥳
```
dataset = load_dataset("namespace/your_dataset_name")
```
Finally, don't forget to enrich the dataset card to document your dataset and make it discoverable! Check out the [Create a dataset card](dataset_card) guide to learn more.
## Datasets on GitHub (legacy)
Datasets used to be hosted on our GitHub repository, but all datasets have now been migrated to the Hugging Face Hub.
The legacy GitHub datasets were added originally on our GitHub repository and therefore don't have a namespace on the Hub: "squad", "glue", etc. unlike the other datasets that are named "username/dataset_name" or "org/dataset_name".
<Tip>
The distinction between a Hub dataset within or without a namespace only comes from the legacy sharing workflow. It does not involve any ranking, decisioning, or opinion regarding the contents of the dataset itself.
</Tip>
Those datasets are now maintained on the Hub: if you think a fix is needed, please use their "Community" tab to open a discussion or create a Pull Request.
The code of these datasets is reviewed by the Hugging Face team.
| datasets/docs/source/share.mdx/0 | {
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"repo_id": "datasets",
"token_count": 1511
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# Metric Card for BLEU
## Metric Description
BLEU (Bilingual Evaluation Understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and remains one of the most popular automated and inexpensive metrics.
Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations. Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Neither intelligibility nor grammatical correctness are not taken into account.
## Intended Uses
BLEU and BLEU-derived metrics are most often used for machine translation.
## How to Use
This metric takes as input lists of predicted sentences and reference sentences:
```python
>>> predictions = [
... ["hello", "there", "general", "kenobi"],
... ["foo", "bar", "foobar"]
... ]
>>> references = [
... [["hello", "there", "general", "kenobi"]],
... [["foo", "bar", "foobar"]]
... ]
>>> bleu = datasets.load_metric("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results)
{'bleu': 1.0, 'precisions': [1.0, 1.0, 1.0, 1.0], 'brevity_penalty': 1.0, 'length_ratio': 1.0, 'translation_length': 7, 'reference_length': 7}
```
### Inputs
- **predictions** (`list`): Translations to score. Each translation should be tokenized into a list of tokens.
- **references** (`list` of `list`s): references for each translation. Each reference should be tokenized into a list of tokens.
- **max_order** (`int`): Maximum n-gram order to use when computing BLEU score. Defaults to `4`.
- **smooth** (`boolean`): Whether or not to apply Lin et al. 2004 smoothing. Defaults to `False`.
### Output Values
- **bleu** (`float`): bleu score
- **precisions** (`list` of `float`s): geometric mean of n-gram precisions,
- **brevity_penalty** (`float`): brevity penalty,
- **length_ratio** (`float`): ratio of lengths,
- **translation_length** (`int`): translation_length,
- **reference_length** (`int`): reference_length
Output Example:
```python
{'bleu': 1.0, 'precisions': [1.0, 1.0, 1.0, 1.0], 'brevity_penalty': 1.0, 'length_ratio': 1.167, 'translation_length': 7, 'reference_length': 6}
```
BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1 representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional reference translations will increase the BLEU score.
#### Values from Popular Papers
The [original BLEU paper](https://aclanthology.org/P02-1040/) (Papineni et al. 2002) compares BLEU scores of five different models on the same 500-sentence corpus. These scores ranged from 0.0527 to 0.2571.
The [Attention is All you Need paper](https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) (Vaswani et al. 2017) got a BLEU score of 0.284 on the WMT 2014 English-to-German translation task, and 0.41 on the WMT 2014 English-to-French translation task.
### Examples
Example where each sample has 1 reference:
```python
>>> predictions = [
... ["hello", "there", "general", "kenobi"],
... ["foo", "bar", "foobar"]
... ]
>>> references = [
... [["hello", "there", "general", "kenobi"]],
... [["foo", "bar", "foobar"]]
... ]
>>> bleu = datasets.load_metric("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results)
{'bleu': 1.0, 'precisions': [1.0, 1.0, 1.0, 1.0], 'brevity_penalty': 1.0, 'length_ratio': 1.0, 'translation_length': 7, 'reference_length': 7}
```
Example where the first sample has 2 references:
```python
>>> predictions = [
... ["hello", "there", "general", "kenobi"],
... ["foo", "bar", "foobar"]
... ]
>>> references = [
... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],
... [["foo", "bar", "foobar"]]
... ]
>>> bleu = datasets.load_metric("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results)
{'bleu': 1.0, 'precisions': [1.0, 1.0, 1.0, 1.0], 'brevity_penalty': 1.0, 'length_ratio': 1.1666666666666667, 'translation_length': 7, 'reference_length': 6}
```
## Limitations and Bias
This metric hase multiple known limitations and biases:
- BLEU compares overlap in tokens from the predictions and references, instead of comparing meaning. This can lead to discrepencies between BLEU scores and human ratings.
- BLEU scores are not comparable across different datasets, nor are they comparable across different languages.
- BLEU scores can vary greatly depending on which parameters are used to generate the scores, especially when different tokenization and normalization techniques are used. It is therefore not possible to compare BLEU scores generated using different parameters, or when these parameters are unknown.
- Shorter predicted translations achieve higher scores than longer ones, simply due to how the score is calculated. A brevity penalty is introduced to attempt to counteract this.
## Citation
```bibtex
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
booktitle = {},
year = {2002},
pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
author = "Lin, Chin-Yew and
Och, Franz Josef",
booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
month = "aug 23{--}aug 27",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://www.aclweb.org/anthology/C04-1072",
pages = "501--507",
}
```
## Further References
- This Hugging Face implementation uses [this Tensorflow implementation](https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py)
| datasets/metrics/bleu/README.md/0 | {
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# Copyright 2020 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" CoVal metric. """
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
"""
_DESCRIPTION = """\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""
_KWARGS_DESCRIPTION = """
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting 'keep_singletons=False', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
'mentions': mentions
'muc': MUC metric [Vilain et al, 1995]
'bcub': B-cubed [Bagga and Baldwin, 1998]
'ceafe': CEAFe [Luo et al., 2005]
'lea': LEA [Moosavi and Strube, 2016]
'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric('coval')
>>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -',
... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)',
... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)',
... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -',
... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -',
... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""
def get_coref_infos(
key_lines, sys_lines, NP_only=False, remove_nested=False, keep_singletons=True, min_span=False, doc="dummy_doc"
):
key_doc_lines = {doc: key_lines}
sys_doc_lines = {doc: sys_lines}
doc_coref_infos = {}
key_nested_coref_num = 0
sys_nested_coref_num = 0
key_removed_nested_clusters = 0
sys_removed_nested_clusters = 0
key_singletons_num = 0
sys_singletons_num = 0
key_clusters, singletons_num = reader.get_doc_mentions(doc, key_doc_lines[doc], keep_singletons)
key_singletons_num += singletons_num
if NP_only or min_span:
key_clusters = reader.set_annotated_parse_trees(key_clusters, key_doc_lines[doc], NP_only, min_span)
sys_clusters, singletons_num = reader.get_doc_mentions(doc, sys_doc_lines[doc], keep_singletons)
sys_singletons_num += singletons_num
if NP_only or min_span:
sys_clusters = reader.set_annotated_parse_trees(sys_clusters, key_doc_lines[doc], NP_only, min_span)
if remove_nested:
nested_mentions, removed_clusters = reader.remove_nested_coref_mentions(key_clusters, keep_singletons)
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
nested_mentions, removed_clusters = reader.remove_nested_coref_mentions(sys_clusters, keep_singletons)
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
sys_mention_key_cluster = reader.get_mention_assignments(sys_clusters, key_clusters)
key_mention_sys_cluster = reader.get_mention_assignments(key_clusters, sys_clusters)
doc_coref_infos[doc] = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
"Number of removed nested coreferring mentions in the key "
f"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"
)
logger.info(
"Number of resulting singleton clusters in the key "
f"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"
)
if not keep_singletons:
logger.info(
f"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
"files, respectively"
)
return doc_coref_infos
def evaluate(key_lines, sys_lines, metrics, NP_only, remove_nested, keep_singletons, min_span):
doc_coref_infos = get_coref_infos(key_lines, sys_lines, NP_only, remove_nested, keep_singletons, min_span)
output_scores = {}
conll = 0
conll_subparts_num = 0
for name, metric in metrics:
recall, precision, f1 = evaluator.evaluate_documents(doc_coref_infos, metric, beta=1)
if name in ["muc", "bcub", "ceafe"]:
conll += f1
conll_subparts_num += 1
output_scores.update({f"{name}/recall": recall, f"{name}/precision": precision, f"{name}/f1": f1})
logger.info(
name.ljust(10),
f"Recall: {recall * 100:.2f}",
f" Precision: {precision * 100:.2f}",
f" F1: {f1 * 100:.2f}",
)
if conll_subparts_num == 3:
conll = (conll / 3) * 100
logger.info(f"CoNLL score: {conll:.2f}")
output_scores.update({"conll_score": conll})
return output_scores
def check_gold_parse_annotation(key_lines):
has_gold_parse = False
for line in key_lines:
if not line.startswith("#"):
if len(line.split()) > 6:
parse_col = line.split()[5]
if not parse_col == "-":
has_gold_parse = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Coval(datasets.Metric):
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string")),
"references": datasets.Sequence(datasets.Value("string")),
}
),
codebase_urls=["https://github.com/ns-moosavi/coval"],
reference_urls=[
"https://github.com/ns-moosavi/coval",
"https://www.aclweb.org/anthology/P16-1060",
"http://www.conll.cemantix.org/2012/data.html",
],
)
def _compute(
self, predictions, references, keep_singletons=True, NP_only=False, min_span=False, remove_nested=False
):
allmetrics = [
("mentions", evaluator.mentions),
("muc", evaluator.muc),
("bcub", evaluator.b_cubed),
("ceafe", evaluator.ceafe),
("lea", evaluator.lea),
]
if min_span:
has_gold_parse = util.check_gold_parse_annotation(references)
if not has_gold_parse:
raise NotImplementedError("References should have gold parse annotation to use 'min_span'.")
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
score = evaluate(
key_lines=references,
sys_lines=predictions,
metrics=allmetrics,
NP_only=NP_only,
remove_nested=remove_nested,
keep_singletons=keep_singletons,
min_span=min_span,
)
return score
| datasets/metrics/coval/coval.py/0 | {
"file_path": "datasets/metrics/coval/coval.py",
"repo_id": "datasets",
"token_count": 5730
} | 60 |
# Metric Card for MAE
## Metric Description
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual numeric values:
## How to Use
At minimum, this metric requires predictions and references as inputs.
```python
>>> mae_metric = datasets.load_metric("mae")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mae_metric.compute(predictions=predictions, references=references)
```
### Inputs
Mandatory inputs:
- `predictions`: numeric array-like of shape (`n_samples,`) or (`n_samples`, `n_outputs`), representing the estimated target values.
- `references`: numeric array-like of shape (`n_samples,`) or (`n_samples`, `n_outputs`), representing the ground truth (correct) target values.
Optional arguments:
- `sample_weight`: numeric array-like of shape (`n_samples,`) representing sample weights. The default is `None`.
- `multioutput`: `raw_values`, `uniform_average` or numeric array-like of shape (`n_outputs,`), which defines the aggregation of multiple output values. The default value is `uniform_average`.
- `raw_values` returns a full set of errors in case of multioutput input.
- `uniform_average` means that the errors of all outputs are averaged with uniform weight.
- the array-like value defines weights used to average errors.
### Output Values
This metric outputs a dictionary, containing the mean absolute error score, which is of type:
- `float`: if multioutput is `uniform_average` or an ndarray of weights, then the weighted average of all output errors is returned.
- numeric array-like of shape (`n_outputs,`): if multioutput is `raw_values`, then the score is returned for each output separately.
Each MAE `float` value ranges from `0.0` to `1.0`, with the best value being 0.0.
Output Example(s):
```python
{'mae': 0.5}
```
If `multioutput="raw_values"`:
```python
{'mae': array([0.5, 1. ])}
```
#### Values from Popular Papers
### Examples
Example with the `uniform_average` config:
```python
>>> from datasets import load_metric
>>> mae_metric = load_metric("mae")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mae_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mae': 0.5}
```
Example with multi-dimensional lists, and the `raw_values` config:
```python
>>> from datasets import load_metric
>>> mae_metric = datasets.load_metric("mae", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mae_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mae': 0.75}
>>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results)
{'mae': array([0.5, 1. ])}
```
## Limitations and Bias
One limitation of MAE is that the relative size of the error is not always obvious, meaning that it can be difficult to tell a big error from a smaller one -- metrics such as Mean Absolute Percentage Error (MAPE) have been proposed to calculate MAE in percentage terms.
Also, since it calculates the mean, MAE may underestimate the impact of big, but infrequent, errors -- metrics such as the Root Mean Square Error (RMSE) compensate for this by taking the root of error values.
## Citation(s)
```bibtex
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
```
```bibtex
@article{willmott2005advantages,
title={Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance},
author={Willmott, Cort J and Matsuura, Kenji},
journal={Climate research},
volume={30},
number={1},
pages={79--82},
year={2005}
}
```
## Further References
- [Mean Absolute Error - Wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_error)
| datasets/metrics/mae/README.md/0 | {
"file_path": "datasets/metrics/mae/README.md",
"repo_id": "datasets",
"token_count": 1421
} | 61 |
# Metric Card for Perplexity
## Metric Description
Given a model and an input text sequence, perplexity measures how likely the model is to generate the input text sequence. This can be used in two main ways:
1. to evaluate how well the model has learned the distribution of the text it was trained on
- In this case, the model input should be the trained model to be evaluated, and the input texts should be the text that the model was trained on.
2. to evaluate how well a selection of text matches the distribution of text that the input model was trained on
- In this case, the model input should be a trained model, and the input texts should be the text to be evaluated.
## Intended Uses
Any language generation task.
## How to Use
The metric takes a list of text as input, as well as the name of the model used to compute the metric:
```python
from datasets import load_metric
perplexity = load_metric("perplexity")
results = perplexity.compute(input_texts=input_texts, model_id='gpt2')
```
### Inputs
- **model_id** (str): model used for calculating Perplexity. NOTE: Perplexity can only be calculated for causal language models.
- This includes models such as gpt2, causal variations of bert, causal versions of t5, and more (the full list can be found in the AutoModelForCausalLM documentation here: https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
- **input_texts** (list of str): input text, each separate text snippet is one list entry.
- **batch_size** (int): the batch size to run texts through the model. Defaults to 16.
- **add_start_token** (bool): whether to add the start token to the texts, so the perplexity can include the probability of the first word. Defaults to True.
- **device** (str): device to run on, defaults to 'cuda' when available
### Output Values
This metric outputs a dictionary with the perplexity scores for the text input in the list, and the average perplexity.
If one of the input texts is longer than the max input length of the model, then it is truncated to the max length for the perplexity computation.
```
{'perplexities': [8.182524681091309, 33.42122268676758, 27.012239456176758], 'mean_perplexity': 22.871995608011883}
```
This metric's range is 0 and up. A lower score is better.
#### Values from Popular Papers
### Examples
Calculating perplexity on input_texts defined here:
```python
perplexity = datasets.load_metric("perplexity")
input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
results = perplexity.compute(model_id='gpt2',
add_start_token=False,
input_texts=input_texts)
print(list(results.keys()))
>>>['perplexities', 'mean_perplexity']
print(round(results["mean_perplexity"], 2))
>>>78.22
print(round(results["perplexities"][0], 2))
>>>11.11
```
Calculating perplexity on input_texts loaded in from a dataset:
```python
perplexity = datasets.load_metric("perplexity")
input_texts = datasets.load_dataset("wikitext",
"wikitext-2-raw-v1",
split="test")["text"][:50]
input_texts = [s for s in input_texts if s!='']
results = perplexity.compute(model_id='gpt2',
input_texts=input_texts)
print(list(results.keys()))
>>>['perplexities', 'mean_perplexity']
print(round(results["mean_perplexity"], 2))
>>>60.35
print(round(results["perplexities"][0], 2))
>>>81.12
```
## Limitations and Bias
Note that the output value is based heavily on what text the model was trained on. This means that perplexity scores are not comparable between models or datasets.
## Citation
```bibtex
@article{jelinek1977perplexity,
title={Perplexity—a measure of the difficulty of speech recognition tasks},
author={Jelinek, Fred and Mercer, Robert L and Bahl, Lalit R and Baker, James K},
journal={The Journal of the Acoustical Society of America},
volume={62},
number={S1},
pages={S63--S63},
year={1977},
publisher={Acoustical Society of America}
}
```
## Further References
- [Hugging Face Perplexity Blog Post](https://huggingface.co/docs/transformers/perplexity)
| datasets/metrics/perplexity/README.md/0 | {
"file_path": "datasets/metrics/perplexity/README.md",
"repo_id": "datasets",
"token_count": 1345
} | 62 |
# Metric Card for Spearman Correlation Coefficient Metric (spearmanr)
## Metric Description
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
## How to Use
At minimum, this metric only requires a `list` of predictions and a `list` of references:
```python
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
```
### Inputs
- **`predictions`** (`list` of `float`): Predicted labels, as returned by a model.
- **`references`** (`list` of `float`): Ground truth labels.
- **`return_pvalue`** (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
### Output Values
- **`spearmanr`** (`float`): Spearman correlation coefficient.
- **`p-value`** (`float`): p-value. **Note**: is only returned
if `return_pvalue=True` is input.
If `return_pvalue=False`, the output is a `dict` with one value, as below:
```python
{'spearmanr': -0.7}
```
Otherwise, if `return_pvalue=True`, the output is a `dict` containing a the `spearmanr` value as well as the corresponding `pvalue`:
```python
{'spearmanr': -0.7, 'spearmanr_pvalue': 0.1881204043741873}
```
Spearman rank-order correlations can take on any value from `-1` to `1`, inclusive.
The p-values can take on any value from `0` to `1`, inclusive.
#### Values from Popular Papers
### Examples
A basic example:
```python
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
```
The same example, but that also returns the pvalue:
```python
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4], return_pvalue=True)
>>> print(results)
{'spearmanr': -0.7, 'spearmanr_pvalue': 0.1881204043741873
>>> print(results['spearmanr'])
-0.7
>>> print(results['spearmanr_pvalue'])
0.1881204043741873
```
## Limitations and Bias
## Citation
```bibtex
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
```
## Further References
*Add any useful further references.*
| datasets/metrics/spearmanr/README.md/0 | {
"file_path": "datasets/metrics/spearmanr/README.md",
"repo_id": "datasets",
"token_count": 1585
} | 63 |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" WIKI_SPLIT metric."""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_CITATION = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
"""
_DESCRIPTION = """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""
_KWARGS_DESCRIPTION = """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
return re.sub(regex, " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_em(predictions, references):
scores = [any(compute_exact(ref, pred) for ref in refs) for pred, refs in zip(predictions, references)]
return (sum(scores) / len(scores)) * 100
def SARIngram(sgrams, cgrams, rgramslist, numref):
rgramsall = [rgram for rgrams in rgramslist for rgram in rgrams]
rgramcounter = Counter(rgramsall)
sgramcounter = Counter(sgrams)
sgramcounter_rep = Counter()
for sgram, scount in sgramcounter.items():
sgramcounter_rep[sgram] = scount * numref
cgramcounter = Counter(cgrams)
cgramcounter_rep = Counter()
for cgram, ccount in cgramcounter.items():
cgramcounter_rep[cgram] = ccount * numref
# KEEP
keepgramcounter_rep = sgramcounter_rep & cgramcounter_rep
keepgramcountergood_rep = keepgramcounter_rep & rgramcounter
keepgramcounterall_rep = sgramcounter_rep & rgramcounter
keeptmpscore1 = 0
keeptmpscore2 = 0
for keepgram in keepgramcountergood_rep:
keeptmpscore1 += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscore2 += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
keepscore_precision = 1
keepscore_recall = 1
if len(keepgramcounter_rep) > 0:
keepscore_precision = keeptmpscore1 / len(keepgramcounter_rep)
if len(keepgramcounterall_rep) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
keepscore_recall = keeptmpscore2 / sum(keepgramcounterall_rep.values())
keepscore = 0
if keepscore_precision > 0 or keepscore_recall > 0:
keepscore = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
delgramcounter_rep = sgramcounter_rep - cgramcounter_rep
delgramcountergood_rep = delgramcounter_rep - rgramcounter
delgramcounterall_rep = sgramcounter_rep - rgramcounter
deltmpscore1 = 0
deltmpscore2 = 0
for delgram in delgramcountergood_rep:
deltmpscore1 += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscore2 += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
delscore_precision = 1
if len(delgramcounter_rep) > 0:
delscore_precision = deltmpscore1 / len(delgramcounter_rep)
# ADDITION
addgramcounter = set(cgramcounter) - set(sgramcounter)
addgramcountergood = set(addgramcounter) & set(rgramcounter)
addgramcounterall = set(rgramcounter) - set(sgramcounter)
addtmpscore = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
addscore_precision = 1
addscore_recall = 1
if len(addgramcounter) > 0:
addscore_precision = addtmpscore / len(addgramcounter)
if len(addgramcounterall) > 0:
addscore_recall = addtmpscore / len(addgramcounterall)
addscore = 0
if addscore_precision > 0 or addscore_recall > 0:
addscore = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def SARIsent(ssent, csent, rsents):
numref = len(rsents)
s1grams = ssent.split(" ")
c1grams = csent.split(" ")
s2grams = []
c2grams = []
s3grams = []
c3grams = []
s4grams = []
c4grams = []
r1gramslist = []
r2gramslist = []
r3gramslist = []
r4gramslist = []
for rsent in rsents:
r1grams = rsent.split(" ")
r2grams = []
r3grams = []
r4grams = []
r1gramslist.append(r1grams)
for i in range(0, len(r1grams) - 1):
if i < len(r1grams) - 1:
r2gram = r1grams[i] + " " + r1grams[i + 1]
r2grams.append(r2gram)
if i < len(r1grams) - 2:
r3gram = r1grams[i] + " " + r1grams[i + 1] + " " + r1grams[i + 2]
r3grams.append(r3gram)
if i < len(r1grams) - 3:
r4gram = r1grams[i] + " " + r1grams[i + 1] + " " + r1grams[i + 2] + " " + r1grams[i + 3]
r4grams.append(r4gram)
r2gramslist.append(r2grams)
r3gramslist.append(r3grams)
r4gramslist.append(r4grams)
for i in range(0, len(s1grams) - 1):
if i < len(s1grams) - 1:
s2gram = s1grams[i] + " " + s1grams[i + 1]
s2grams.append(s2gram)
if i < len(s1grams) - 2:
s3gram = s1grams[i] + " " + s1grams[i + 1] + " " + s1grams[i + 2]
s3grams.append(s3gram)
if i < len(s1grams) - 3:
s4gram = s1grams[i] + " " + s1grams[i + 1] + " " + s1grams[i + 2] + " " + s1grams[i + 3]
s4grams.append(s4gram)
for i in range(0, len(c1grams) - 1):
if i < len(c1grams) - 1:
c2gram = c1grams[i] + " " + c1grams[i + 1]
c2grams.append(c2gram)
if i < len(c1grams) - 2:
c3gram = c1grams[i] + " " + c1grams[i + 1] + " " + c1grams[i + 2]
c3grams.append(c3gram)
if i < len(c1grams) - 3:
c4gram = c1grams[i] + " " + c1grams[i + 1] + " " + c1grams[i + 2] + " " + c1grams[i + 3]
c4grams.append(c4gram)
(keep1score, del1score, add1score) = SARIngram(s1grams, c1grams, r1gramslist, numref)
(keep2score, del2score, add2score) = SARIngram(s2grams, c2grams, r2gramslist, numref)
(keep3score, del3score, add3score) = SARIngram(s3grams, c3grams, r3gramslist, numref)
(keep4score, del4score, add4score) = SARIngram(s4grams, c4grams, r4gramslist, numref)
avgkeepscore = sum([keep1score, keep2score, keep3score, keep4score]) / 4
avgdelscore = sum([del1score, del2score, del3score, del4score]) / 4
avgaddscore = sum([add1score, add2score, add3score, add4score]) / 4
finalscore = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def normalize(sentence, lowercase: bool = True, tokenizer: str = "13a", return_str: bool = True):
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
sentence = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__).major >= 2:
normalized_sent = sacrebleu.metrics.bleu._get_tokenizer(tokenizer)()(sentence)
else:
normalized_sent = sacrebleu.TOKENIZERS[tokenizer]()(sentence)
elif tokenizer == "moses":
normalized_sent = sacremoses.MosesTokenizer().tokenize(sentence, return_str=True, escape=False)
elif tokenizer == "penn":
normalized_sent = sacremoses.MosesTokenizer().penn_tokenize(sentence, return_str=True)
else:
normalized_sent = sentence
if not return_str:
normalized_sent = normalized_sent.split()
return normalized_sent
def compute_sari(sources, predictions, references):
if not (len(sources) == len(predictions) == len(references)):
raise ValueError("Sources length must match predictions and references lengths.")
sari_score = 0
for src, pred, refs in zip(sources, predictions, references):
sari_score += SARIsent(normalize(src), normalize(pred), [normalize(sent) for sent in refs])
sari_score = sari_score / len(predictions)
return 100 * sari_score
def compute_sacrebleu(
predictions,
references,
smooth_method="exp",
smooth_value=None,
force=False,
lowercase=False,
use_effective_order=False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
output = sacrebleu.corpus_bleu(
predictions,
transformed_references,
smooth_method=smooth_method,
smooth_value=smooth_value,
force=force,
lowercase=lowercase,
use_effective_order=use_effective_order,
)
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class WikiSplit(datasets.Metric):
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Value("string", id="sequence"),
"references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
}
),
codebase_urls=[
"https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
"https://github.com/cocoxu/simplification/blob/master/SARI.py",
"https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
"https://github.com/mjpost/sacreBLEU",
],
reference_urls=[
"https://www.aclweb.org/anthology/Q16-1029.pdf",
"https://github.com/mjpost/sacreBLEU",
"https://en.wikipedia.org/wiki/BLEU",
"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
],
)
def _compute(self, sources, predictions, references):
result = {}
result.update({"sari": compute_sari(sources=sources, predictions=predictions, references=references)})
result.update({"sacrebleu": compute_sacrebleu(predictions=predictions, references=references)})
result.update({"exact": compute_em(predictions=predictions, references=references)})
return result
| datasets/metrics/wiki_split/wiki_split.py/0 | {
"file_path": "datasets/metrics/wiki_split/wiki_split.py",
"repo_id": "datasets",
"token_count": 5827
} | 64 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
HIGHLIGHT_MESSAGE_PRE = """<<<<<<< This should probably be modified because it mentions: """
HIGHLIGHT_MESSAGE_POST = """=======
>>>>>>>
"""
TO_HIGHLIGHT = [
"TextEncoderConfig",
"ByteTextEncoder",
"SubwordTextEncoder",
"encoder_config",
"maybe_build_from_corpus",
"manual_dir",
]
TO_CONVERT = [
# (pattern, replacement)
# Order is important here for some replacements
(r"tfds\.core", r"datasets"),
(r"tf\.io\.gfile\.GFile", r"open"),
(r"tf\.([\w\d]+)", r"datasets.Value('\1')"),
(r"tfds\.features\.Text\(\)", r"datasets.Value('string')"),
(r"tfds\.features\.Text\(", r"datasets.Value('string'),"),
(r"features\s*=\s*tfds.features.FeaturesDict\(", r"features=datasets.Features("),
(r"tfds\.features\.FeaturesDict\(", r"dict("),
(r"The TensorFlow Datasets Authors", r"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
(r"tfds\.", r"datasets."),
(r"dl_manager\.manual_dir", r"self.config.data_dir"),
(r"self\.builder_config", r"self.config"),
]
def convert_command_factory(args: Namespace):
"""
Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint.
Returns: ConvertCommand
"""
return ConvertCommand(args.tfds_path, args.datasets_directory)
class ConvertCommand(BaseDatasetsCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the datasets-cli
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser(
"convert",
help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",
)
train_parser.add_argument(
"--tfds_path",
type=str,
required=True,
help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",
)
train_parser.add_argument(
"--datasets_directory", type=str, required=True, help="Path to the HuggingFace Datasets folder."
)
train_parser.set_defaults(func=convert_command_factory)
def __init__(self, tfds_path: str, datasets_directory: str, *args):
self._logger = get_logger("datasets-cli/converting")
self._tfds_path = tfds_path
self._datasets_directory = datasets_directory
def run(self):
if os.path.isdir(self._tfds_path):
abs_tfds_path = os.path.abspath(self._tfds_path)
elif os.path.isfile(self._tfds_path):
abs_tfds_path = os.path.dirname(self._tfds_path)
else:
raise ValueError("--tfds_path is neither a directory nor a file. Please check path.")
abs_datasets_path = os.path.abspath(self._datasets_directory)
self._logger.info(f"Converting datasets from {abs_tfds_path} to {abs_datasets_path}")
utils_files = []
with_manual_update = []
imports_to_builder_map = {}
if os.path.isdir(self._tfds_path):
file_names = os.listdir(abs_tfds_path)
else:
file_names = [os.path.basename(self._tfds_path)]
for f_name in file_names:
self._logger.info(f"Looking at file {f_name}")
input_file = os.path.join(abs_tfds_path, f_name)
output_file = os.path.join(abs_datasets_path, f_name)
if not os.path.isfile(input_file) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("Skipping file")
continue
with open(input_file, encoding="utf-8") as f:
lines = f.readlines()
out_lines = []
is_builder = False
needs_manual_update = False
tfds_imports = []
for line in lines:
out_line = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
out_line = "import datasets\n"
elif "import tensorflow" in out_line:
# order is important here
out_line = ""
continue
elif "from absl import logging" in out_line:
out_line = "from datasets import logging\n"
elif "getLogger" in out_line:
out_line = out_line.replace("getLogger", "get_logger")
elif any(expression in out_line for expression in TO_HIGHLIGHT):
needs_manual_update = True
to_remove = list(filter(lambda e: e in out_line, TO_HIGHLIGHT))
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(to_remove) + "\n")
out_lines.append(out_line)
out_lines.append(HIGHLIGHT_MESSAGE_POST)
continue
else:
for pattern, replacement in TO_CONVERT:
out_line = re.sub(pattern, replacement, out_line)
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
match = re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)", out_line)
tfds_imports.extend(imp.strip() for imp in match.group(1).split(","))
out_line = "from . import " + match.group(1)
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f"Error converting {out_line.strip()}")
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
is_builder = True
out_lines.append(out_line)
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
dir_name = f_name.replace(".py", "")
output_dir = os.path.join(abs_datasets_path, dir_name)
output_file = os.path.join(output_dir, f_name)
os.makedirs(output_dir, exist_ok=True)
self._logger.info(f"Adding directory {output_dir}")
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
else:
# Utilities will be moved at the end
utils_files.append(output_file)
if needs_manual_update:
with_manual_update.append(output_file)
with open(output_file, "w", encoding="utf-8") as f:
f.writelines(out_lines)
self._logger.info(f"Converted in {output_file}")
for utils_file in utils_files:
try:
f_name = os.path.basename(utils_file)
dest_folder = imports_to_builder_map[f_name.replace(".py", "")]
self._logger.info(f"Moving {dest_folder} to {utils_file}")
shutil.copy(utils_file, dest_folder)
except KeyError:
self._logger.error(f"Cannot find destination folder for {utils_file}. Please copy manually.")
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."
)
| datasets/src/datasets/commands/convert.py/0 | {
"file_path": "datasets/src/datasets/commands/convert.py",
"repo_id": "datasets",
"token_count": 3822
} | 65 |
# ruff: noqa
__all__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| datasets/src/datasets/features/__init__.py/0 | {
"file_path": "datasets/src/datasets/features/__init__.py",
"repo_id": "datasets",
"token_count": 165
} | 66 |
# Copyright 2020 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
""" List and inspect datasets."""
import inspect
import os
import shutil
import warnings
from pathlib import Path, PurePath
from typing import Dict, List, Mapping, Optional, Sequence, Union
import huggingface_hub
from . import config
from .download.download_config import DownloadConfig
from .download.download_manager import DownloadMode
from .download.streaming_download_manager import StreamingDownloadManager
from .info import DatasetInfo
from .load import (
dataset_module_factory,
get_dataset_builder_class,
import_main_class,
load_dataset_builder,
metric_module_factory,
)
from .utils.deprecation_utils import deprecated
from .utils.file_utils import relative_to_absolute_path
from .utils.logging import get_logger
from .utils.version import Version
logger = get_logger(__name__)
class SplitsNotFoundError(ValueError):
pass
@deprecated("Use 'huggingface_hub.list_datasets' instead.")
def list_datasets(with_community_datasets=True, with_details=False):
"""List all the datasets scripts available on the Hugging Face Hub.
Args:
with_community_datasets (`bool`, *optional*, defaults to `True`):
Include the community provided datasets.
with_details (`bool`, *optional*, defaults to `False`):
Return the full details on the datasets instead of only the short name.
Example:
```py
>>> from datasets import list_datasets
>>> list_datasets()
['acronym_identification',
'ade_corpus_v2',
'adversarial_qa',
'aeslc',
'afrikaans_ner_corpus',
'ag_news',
...
]
```
"""
datasets = huggingface_hub.list_datasets(full=with_details)
if not with_community_datasets:
datasets = [dataset for dataset in datasets if "/" not in dataset.id]
if not with_details:
datasets = [dataset.id for dataset in datasets]
return list(datasets)
@deprecated(
"Use 'evaluate.list_evaluation_modules' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate"
)
def list_metrics(with_community_metrics=True, with_details=False):
"""List all the metrics script available on the Hugging Face Hub.
<Deprecated version="2.5.0">
Use `evaluate.list_evaluation_modules` instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate
</Deprecated>
Args:
with_community_metrics (:obj:`bool`, optional, default ``True``): Include the community provided metrics.
with_details (:obj:`bool`, optional, default ``False``): Return the full details on the metrics instead of only the short name.
Example:
```py
>>> from datasets import list_metrics
>>> list_metrics()
['accuracy',
'bertscore',
'bleu',
'bleurt',
'cer',
'chrf',
...
]
```
"""
metrics = huggingface_hub.list_metrics()
if not with_community_metrics:
metrics = [metric for metric in metrics if "/" not in metric.id]
if not with_details:
metrics = [metric.id for metric in metrics]
return metrics
@deprecated("Clone the dataset repository from the Hugging Face Hub instead.")
def inspect_dataset(path: str, local_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs):
"""
Allow inspection/modification of a dataset script by copying on local drive at local_path.
Args:
path (`str`): Path to the dataset processing script with the dataset builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name
as the directory),
e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`.
- a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`list_datasets`])
e.g. `'squad'`, `'glue'` or `'openai/webtext'`.
local_path (`str`):
Path to the local folder to copy the dataset script to.
download_config ([`DownloadConfig`], *optional*):
Specific download configuration parameters.
**download_kwargs (additional keyword arguments):
Optional arguments for [`DownloadConfig`] which will override
the attributes of `download_config` if supplied.
"""
if download_config is None:
download_config = DownloadConfig(**download_kwargs)
if os.path.isfile(path):
path = str(Path(path).parent)
if os.path.isdir(path):
shutil.copytree(path, local_path, dirs_exist_ok=True)
else:
huggingface_hub.HfApi(endpoint=config.HF_ENDPOINT, token=download_config.token).snapshot_download(
repo_id=path, repo_type="dataset", local_dir=local_path, force_download=download_config.force_download
)
print(
f"The dataset {path} can be inspected at {local_path}. "
f'You can modify this loading script if it has one and use it with `datasets.load_dataset("{PurePath(local_path).as_posix()}")`.'
)
@deprecated(
"Use 'evaluate.inspect_evaluation_module' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate"
)
def inspect_metric(path: str, local_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs):
r"""
Allow inspection/modification of a metric script by copying it on local drive at local_path.
<Deprecated version="2.5.0">
Use `evaluate.inspect_evaluation_module` instead, from the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate
</Deprecated>
Args:
path (``str``): path to the dataset processing script with the dataset builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name as the directory),
e.g. ``'./dataset/squad'`` or ``'./dataset/squad/squad.py'``
- a dataset identifier on the Hugging Face Hub (list all available datasets and ids with ``datasets.list_datasets()``)
e.g. ``'squad'``, ``'glue'`` or ``'openai/webtext'``
local_path (``str``): path to the local folder to copy the datset script to.
download_config (Optional ``datasets.DownloadConfig``): specific download configuration parameters.
**download_kwargs (additional keyword arguments): optional attributes for DownloadConfig() which will override the attributes in download_config if supplied.
"""
metric_module = metric_module_factory(path, download_config=download_config, **download_kwargs)
metric_cls = import_main_class(metric_module.module_path, dataset=False)
module_source_path = inspect.getsourcefile(metric_cls)
module_source_dirpath = os.path.dirname(module_source_path)
for dirpath, dirnames, filenames in os.walk(module_source_dirpath):
dst_dirpath = os.path.join(local_path, os.path.relpath(dirpath, module_source_dirpath))
os.makedirs(dst_dirpath, exist_ok=True)
# skipping hidden directories; prune the search
dirnames[:] = [dirname for dirname in dirnames if not dirname.startswith((".", "__"))]
for filename in filenames:
shutil.copy2(os.path.join(dirpath, filename), os.path.join(dst_dirpath, filename))
shutil.copystat(dirpath, dst_dirpath)
local_path = relative_to_absolute_path(local_path)
print(
f"The processing scripts for metric {path} can be inspected at {local_path}. "
f"The main class is in {module_source_dirpath}. "
f'You can modify this processing scripts and use it with `datasets.load_metric("{PurePath(local_path).as_posix()}")`.'
)
def get_dataset_infos(
path: str,
data_files: Optional[Union[Dict, List, str]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
revision: Optional[Union[str, Version]] = None,
token: Optional[Union[bool, str]] = None,
use_auth_token="deprecated",
**config_kwargs,
):
"""Get the meta information about a dataset, returned as a dict mapping config name to DatasetInfoDict.
Args:
path (`str`): path to the dataset processing script with the dataset builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name as the directory),
e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
- a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
e.g. `'squad'`, `'glue'` or``'openai/webtext'`
revision (`Union[str, datasets.Version]`, *optional*):
If specified, the dataset module will be loaded from the datasets repository at this version.
By default:
- it is set to the local version of the lib.
- it will also try to load it from the main branch if it's not available at the local version of the lib.
Specifying a version that is different from your local version of the lib might cause compatibility issues.
download_config ([`DownloadConfig`], *optional*):
Specific download configuration parameters.
download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
Download/generate mode.
data_files (`Union[Dict, List, str]`, *optional*):
Defining the data_files of the dataset configuration.
token (`str` or `bool`, *optional*):
Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If `True`, or not specified, will get token from `"~/.huggingface"`.
use_auth_token (`str` or `bool`, *optional*):
Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If `True`, or not specified, will get token from `"~/.huggingface"`.
<Deprecated version="2.14.0">
`use_auth_token` was deprecated in favor of `token` in version 2.14.0 and will be removed in 3.0.0.
</Deprecated>
**config_kwargs (additional keyword arguments):
Optional attributes for builder class which will override the attributes if supplied.
Example:
```py
>>> from datasets import get_dataset_infos
>>> get_dataset_infos('rotten_tomatoes')
{'default': DatasetInfo(description="Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews...), ...}
```
"""
if use_auth_token != "deprecated":
warnings.warn(
"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n"
"You can remove this warning by passing 'token=<use_auth_token>' instead.",
FutureWarning,
)
token = use_auth_token
config_names = get_dataset_config_names(
path=path,
revision=revision,
download_config=download_config,
download_mode=download_mode,
data_files=data_files,
token=token,
)
return {
config_name: get_dataset_config_info(
path=path,
config_name=config_name,
data_files=data_files,
download_config=download_config,
download_mode=download_mode,
revision=revision,
token=token,
**config_kwargs,
)
for config_name in config_names
}
def get_dataset_config_names(
path: str,
revision: Optional[Union[str, Version]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
dynamic_modules_path: Optional[str] = None,
data_files: Optional[Union[Dict, List, str]] = None,
**download_kwargs,
):
"""Get the list of available config names for a particular dataset.
Args:
path (`str`): path to the dataset processing script with the dataset builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name as the directory),
e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
- a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
e.g. `'squad'`, `'glue'` or `'openai/webtext'`
revision (`Union[str, datasets.Version]`, *optional*):
If specified, the dataset module will be loaded from the datasets repository at this version.
By default:
- it is set to the local version of the lib.
- it will also try to load it from the main branch if it's not available at the local version of the lib.
Specifying a version that is different from your local version of the lib might cause compatibility issues.
download_config ([`DownloadConfig`], *optional*):
Specific download configuration parameters.
download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
Download/generate mode.
dynamic_modules_path (`str`, defaults to `~/.cache/huggingface/modules/datasets_modules`):
Optional path to the directory in which the dynamic modules are saved. It must have been initialized with `init_dynamic_modules`.
By default the datasets and metrics are stored inside the `datasets_modules` module.
data_files (`Union[Dict, List, str]`, *optional*):
Defining the data_files of the dataset configuration.
**download_kwargs (additional keyword arguments):
Optional attributes for [`DownloadConfig`] which will override the attributes in `download_config` if supplied,
for example `token`.
Example:
```py
>>> from datasets import get_dataset_config_names
>>> get_dataset_config_names("glue")
['cola',
'sst2',
'mrpc',
'qqp',
'stsb',
'mnli',
'mnli_mismatched',
'mnli_matched',
'qnli',
'rte',
'wnli',
'ax']
```
"""
dataset_module = dataset_module_factory(
path,
revision=revision,
download_config=download_config,
download_mode=download_mode,
dynamic_modules_path=dynamic_modules_path,
data_files=data_files,
**download_kwargs,
)
builder_cls = get_dataset_builder_class(dataset_module, dataset_name=os.path.basename(path))
return list(builder_cls.builder_configs.keys()) or [
dataset_module.builder_kwargs.get("config_name", builder_cls.DEFAULT_CONFIG_NAME or "default")
]
def get_dataset_default_config_name(
path: str,
revision: Optional[Union[str, Version]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
dynamic_modules_path: Optional[str] = None,
data_files: Optional[Union[Dict, List, str]] = None,
**download_kwargs,
) -> Optional[str]:
"""Get the default config name for a particular dataset.
Args:
path (`str`): path to the dataset processing script with the dataset builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name as the directory),
e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
- a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
e.g. `'squad'`, `'glue'` or `'openai/webtext'`
revision (`Union[str, datasets.Version]`, *optional*):
If specified, the dataset module will be loaded from the datasets repository at this version.
By default:
- it is set to the local version of the lib.
- it will also try to load it from the main branch if it's not available at the local version of the lib.
Specifying a version that is different from your local version of the lib might cause compatibility issues.
download_config ([`DownloadConfig`], *optional*):
Specific download configuration parameters.
download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
Download/generate mode.
dynamic_modules_path (`str`, defaults to `~/.cache/huggingface/modules/datasets_modules`):
Optional path to the directory in which the dynamic modules are saved. It must have been initialized with `init_dynamic_modules`.
By default the datasets and metrics are stored inside the `datasets_modules` module.
data_files (`Union[Dict, List, str]`, *optional*):
Defining the data_files of the dataset configuration.
**download_kwargs (additional keyword arguments):
Optional attributes for [`DownloadConfig`] which will override the attributes in `download_config` if supplied,
for example `token`.
Returns:
Optional[str]
Example:
```py
>>> from datasets import get_dataset_default_config_name
>>> get_dataset_default_config_name("openbookqa")
'main'
```
"""
dataset_module = dataset_module_factory(
path,
revision=revision,
download_config=download_config,
download_mode=download_mode,
dynamic_modules_path=dynamic_modules_path,
data_files=data_files,
**download_kwargs,
)
builder_cls = get_dataset_builder_class(dataset_module, dataset_name=os.path.basename(path))
builder_configs = list(builder_cls.builder_configs.keys())
if builder_configs:
default_config_name = builder_configs[0] if len(builder_configs) == 1 else None
else:
default_config_name = "default"
return builder_cls.DEFAULT_CONFIG_NAME or default_config_name
def get_dataset_config_info(
path: str,
config_name: Optional[str] = None,
data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
revision: Optional[Union[str, Version]] = None,
token: Optional[Union[bool, str]] = None,
use_auth_token="deprecated",
**config_kwargs,
) -> DatasetInfo:
"""Get the meta information (DatasetInfo) about a dataset for a particular config
Args:
path (``str``): path to the dataset processing script with the dataset builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name as the directory),
e.g. ``'./dataset/squad'`` or ``'./dataset/squad/squad.py'``
- a dataset identifier on the Hugging Face Hub (list all available datasets and ids with ``datasets.list_datasets()``)
e.g. ``'squad'``, ``'glue'`` or ``'openai/webtext'``
config_name (:obj:`str`, optional): Defining the name of the dataset configuration.
data_files (:obj:`str` or :obj:`Sequence` or :obj:`Mapping`, optional): Path(s) to source data file(s).
download_config (:class:`~download.DownloadConfig`, optional): Specific download configuration parameters.
download_mode (:class:`DownloadMode` or :obj:`str`, default ``REUSE_DATASET_IF_EXISTS``): Download/generate mode.
revision (:class:`~utils.Version` or :obj:`str`, optional): Version of the dataset script to load.
As datasets have their own git repository on the Datasets Hub, the default version "main" corresponds to their "main" branch.
You can specify a different version than the default "main" by using a commit SHA or a git tag of the dataset repository.
token (``str`` or :obj:`bool`, optional): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If True, or not specified, will get token from `"~/.huggingface"`.
use_auth_token (``str`` or :obj:`bool`, optional): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If True, or not specified, will get token from `"~/.huggingface"`.
<Deprecated version="2.14.0">
`use_auth_token` was deprecated in favor of `token` in version 2.14.0 and will be removed in 3.0.0.
</Deprecated>
**config_kwargs (additional keyword arguments): optional attributes for builder class which will override the attributes if supplied.
"""
if use_auth_token != "deprecated":
warnings.warn(
"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n"
"You can remove this warning by passing 'token=<use_auth_token>' instead.",
FutureWarning,
)
token = use_auth_token
builder = load_dataset_builder(
path,
name=config_name,
data_files=data_files,
download_config=download_config,
download_mode=download_mode,
revision=revision,
token=token,
**config_kwargs,
)
info = builder.info
if info.splits is None:
download_config = download_config.copy() if download_config else DownloadConfig()
if token is not None:
download_config.token = token
builder._check_manual_download(
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
)
try:
info.splits = {
split_generator.name: {"name": split_generator.name, "dataset_name": path}
for split_generator in builder._split_generators(
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
)
}
except Exception as err:
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
return info
def get_dataset_split_names(
path: str,
config_name: Optional[str] = None,
data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
revision: Optional[Union[str, Version]] = None,
token: Optional[Union[bool, str]] = None,
use_auth_token="deprecated",
**config_kwargs,
):
"""Get the list of available splits for a particular config and dataset.
Args:
path (`str`): path to the dataset processing script with the dataset builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name as the directory),
e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
- a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
e.g. `'squad'`, `'glue'` or `'openai/webtext'`
config_name (`str`, *optional*):
Defining the name of the dataset configuration.
data_files (`str` or `Sequence` or `Mapping`, *optional*):
Path(s) to source data file(s).
download_config ([`DownloadConfig`], *optional*):
Specific download configuration parameters.
download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
Download/generate mode.
revision ([`Version`] or `str`, *optional*):
Version of the dataset script to load.
As datasets have their own git repository on the Datasets Hub, the default version "main" corresponds to their "main" branch.
You can specify a different version than the default "main" by using a commit SHA or a git tag of the dataset repository.
token (`str` or `bool`, *optional*):
Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If `True`, or not specified, will get token from `"~/.huggingface"`.
use_auth_token (`str` or `bool`, *optional*):
Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
If `True`, or not specified, will get token from `"~/.huggingface"`.
<Deprecated version="2.14.0">
`use_auth_token` was deprecated in favor of `token` in version 2.14.0 and will be removed in 3.0.0.
</Deprecated>
**config_kwargs (additional keyword arguments):
Optional attributes for builder class which will override the attributes if supplied.
Example:
```py
>>> from datasets import get_dataset_split_names
>>> get_dataset_split_names('rotten_tomatoes')
['train', 'validation', 'test']
```
"""
if use_auth_token != "deprecated":
warnings.warn(
"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n"
"You can remove this warning by passing 'token=<use_auth_token>' instead.",
FutureWarning,
)
token = use_auth_token
info = get_dataset_config_info(
path,
config_name=config_name,
data_files=data_files,
download_config=download_config,
download_mode=download_mode,
revision=revision,
token=token,
**config_kwargs,
)
return list(info.splits.keys())
| datasets/src/datasets/inspect.py/0 | {
"file_path": "datasets/src/datasets/inspect.py",
"repo_id": "datasets",
"token_count": 9910
} | 67 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Splits related API."""
import abc
import collections
import copy
import dataclasses
import re
from dataclasses import dataclass
from typing import Dict, List, Optional, Union
from .arrow_reader import FileInstructions, make_file_instructions
from .naming import _split_re
from .utils.py_utils import NonMutableDict, asdict
@dataclass
class SplitInfo:
name: str = dataclasses.field(default="", metadata={"include_in_asdict_even_if_is_default": True})
num_bytes: int = dataclasses.field(default=0, metadata={"include_in_asdict_even_if_is_default": True})
num_examples: int = dataclasses.field(default=0, metadata={"include_in_asdict_even_if_is_default": True})
shard_lengths: Optional[List[int]] = None
# Deprecated
# For backward compatibility, this field needs to always be included in files like
# dataset_infos.json and dataset_info.json files
# To do so, we always include it in the output of datasets.utils.py_utils.asdict(split_info)
dataset_name: Optional[str] = dataclasses.field(
default=None, metadata={"include_in_asdict_even_if_is_default": True}
)
@property
def file_instructions(self):
"""Returns the list of dict(filename, take, skip)."""
# `self.dataset_name` is assigned in `SplitDict.add()`.
instructions = make_file_instructions(
name=self.dataset_name,
split_infos=[self],
instruction=str(self.name),
)
return instructions.file_instructions
@dataclass
class SubSplitInfo:
"""Wrapper around a sub split info.
This class expose info on the subsplit:
```
ds, info = datasets.load_dataset(..., split='train[75%:]', with_info=True)
info.splits['train[75%:]'].num_examples
```
"""
instructions: FileInstructions
@property
def num_examples(self):
"""Returns the number of example in the subsplit."""
return self.instructions.num_examples
@property
def file_instructions(self):
"""Returns the list of dict(filename, take, skip)."""
return self.instructions.file_instructions
class SplitBase(metaclass=abc.ABCMeta):
# pylint: disable=line-too-long
"""Abstract base class for Split compositionality.
See the
[guide on splits](../loading#slice-splits)
for more information.
There are three parts to the composition:
1) The splits are composed (defined, merged, split,...) together before
calling the `.as_dataset()` function. This is done with the `__add__`,
`__getitem__`, which return a tree of `SplitBase` (whose leaf
are the `NamedSplit` objects)
```
split = datasets.Split.TRAIN + datasets.Split.TEST.subsplit(datasets.percent[:50])
```
2) The `SplitBase` is forwarded to the `.as_dataset()` function
to be resolved into actual read instruction. This is done by the
`.get_read_instruction()` method which takes the real dataset splits
(name, number of shards,...) and parse the tree to return a
`SplitReadInstruction()` object
```
read_instruction = split.get_read_instruction(self.info.splits)
```
3) The `SplitReadInstruction` is then used in the `tf.data.Dataset` pipeline
to define which files to read and how to skip examples within file.
"""
# pylint: enable=line-too-long
@abc.abstractmethod
def get_read_instruction(self, split_dict):
"""Parse the descriptor tree and compile all read instructions together.
Args:
split_dict: `dict`, The `dict[split_name, SplitInfo]` of the dataset
Returns:
split_read_instruction: `SplitReadInstruction`
"""
raise NotImplementedError("Abstract method")
def __eq__(self, other):
"""Equality: datasets.Split.TRAIN == 'train'."""
if isinstance(other, (NamedSplit, str)):
return False
raise NotImplementedError("Equality is not implemented between merged/sub splits.")
def __ne__(self, other):
"""InEquality: datasets.Split.TRAIN != 'test'."""
return not self.__eq__(other)
def __add__(self, other):
"""Merging: datasets.Split.TRAIN + datasets.Split.TEST."""
return _SplitMerged(self, other)
def subsplit(self, arg=None, k=None, percent=None, weighted=None): # pylint: disable=redefined-outer-name
"""Divides this split into subsplits.
There are 3 ways to define subsplits, which correspond to the 3
arguments `k` (get `k` even subsplits), `percent` (get a slice of the
dataset with `datasets.percent`), and `weighted` (get subsplits with proportions
specified by `weighted`).
Example::
```
# 50% train, 50% test
train, test = split.subsplit(k=2)
# 50% train, 25% test, 25% validation
train, test, validation = split.subsplit(weighted=[2, 1, 1])
# Extract last 20%
subsplit = split.subsplit(datasets.percent[-20:])
```
Warning: k and weighted will be converted into percent which mean that
values below the percent will be rounded up or down. The final split may be
bigger to deal with remainders. For instance:
```
train, test, valid = split.subsplit(k=3) # 33%, 33%, 34%
s1, s2, s3, s4 = split.subsplit(weighted=[2, 2, 1, 1]) # 33%, 33%, 16%, 18%
```
Args:
arg: If no kwargs are given, `arg` will be interpreted as one of
`k`, `percent`, or `weighted` depending on the type.
For example:
```
split.subsplit(10) # Equivalent to split.subsplit(k=10)
split.subsplit(datasets.percent[:-20]) # percent=datasets.percent[:-20]
split.subsplit([1, 1, 2]) # weighted=[1, 1, 2]
```
k: `int` If set, subdivide the split into `k` equal parts.
percent: `datasets.percent slice`, return a single subsplit corresponding to
a slice of the original split. For example:
`split.subsplit(datasets.percent[-20:]) # Last 20% of the dataset`.
weighted: `list[int]`, return a list of subsplits whose proportions match
the normalized sum of the list. For example:
`split.subsplit(weighted=[1, 1, 2]) # 25%, 25%, 50%`.
Returns:
A subsplit or list of subsplits extracted from this split object.
"""
# Note that the percent kwargs redefine the outer name datasets.percent. This
# is done for consistency (.subsplit(percent=datasets.percent[:40]))
if sum(bool(x) for x in (arg, k, percent, weighted)) != 1:
raise ValueError("Only one argument of subsplit should be set.")
# Auto deduce k
if isinstance(arg, int):
k = arg
elif isinstance(arg, slice):
percent = arg
elif isinstance(arg, list):
weighted = arg
if not (k or percent or weighted):
raise ValueError(
f"Invalid split argument {arg}. Only list, slice and int supported. "
"One of k, weighted or percent should be set to a non empty value."
)
def assert_slices_coverage(slices):
# Ensure that the expended slices cover all percents.
assert sum((list(range(*s.indices(100))) for s in slices), []) == list(range(100))
if k:
if not 0 < k <= 100:
raise ValueError(f"Subsplit k should be between 0 and 100, got {k}")
shift = 100 // k
slices = [slice(i * shift, (i + 1) * shift) for i in range(k)]
# Round up last element to ensure all elements are taken
slices[-1] = slice(slices[-1].start, 100)
# Internal check to ensure full coverage
assert_slices_coverage(slices)
return tuple(_SubSplit(self, s) for s in slices)
elif percent:
return _SubSplit(self, percent)
elif weighted:
# Normalize the weighted sum
total = sum(weighted)
weighted = [100 * x // total for x in weighted]
# Create the slice for each of the elements
start = 0
stop = 0
slices = []
for v in weighted:
stop += v
slices.append(slice(start, stop))
start = stop
# Round up last element to ensure all elements are taken
slices[-1] = slice(slices[-1].start, 100)
# Internal check to ensure full coverage
assert_slices_coverage(slices)
return tuple(_SubSplit(self, s) for s in slices)
else:
# Should not be possible
raise ValueError("Could not determine the split")
# 2 requirements:
# 1. datasets.percent be sliceable
# 2. datasets.percent be documented
#
# Instances are not documented, so we want datasets.percent to be a class, but to
# have it be sliceable, we need this metaclass.
class PercentSliceMeta(type):
def __getitem__(cls, slice_value):
if not isinstance(slice_value, slice):
raise ValueError(f"datasets.percent should only be called with slice, not {slice_value}")
return slice_value
class PercentSlice(metaclass=PercentSliceMeta):
# pylint: disable=line-too-long
"""Syntactic sugar for defining slice subsplits: `datasets.percent[75:-5]`.
See the
[guide on splits](../loading#slice-splits)
for more information.
"""
# pylint: enable=line-too-long
pass
percent = PercentSlice # pylint: disable=invalid-name
class _SplitMerged(SplitBase):
"""Represent two split descriptors merged together."""
def __init__(self, split1, split2):
self._split1 = split1
self._split2 = split2
def get_read_instruction(self, split_dict):
read_instruction1 = self._split1.get_read_instruction(split_dict)
read_instruction2 = self._split2.get_read_instruction(split_dict)
return read_instruction1 + read_instruction2
def __repr__(self):
return f"({repr(self._split1)} + {repr(self._split2)})"
class _SubSplit(SplitBase):
"""Represent a sub split of a split descriptor."""
def __init__(self, split, slice_value):
self._split = split
self._slice_value = slice_value
def get_read_instruction(self, split_dict):
return self._split.get_read_instruction(split_dict)[self._slice_value]
def __repr__(self):
slice_str = "{start}:{stop}"
if self._slice_value.step is not None:
slice_str += ":{step}"
slice_str = slice_str.format(
start="" if self._slice_value.start is None else self._slice_value.start,
stop="" if self._slice_value.stop is None else self._slice_value.stop,
step=self._slice_value.step,
)
return f"{repr(self._split)}(datasets.percent[{slice_str}])"
class NamedSplit(SplitBase):
"""Descriptor corresponding to a named split (train, test, ...).
Example:
Each descriptor can be composed with other using addition or slice:
```py
split = datasets.Split.TRAIN.subsplit(datasets.percent[0:25]) + datasets.Split.TEST
```
The resulting split will correspond to 25% of the train split merged with
100% of the test split.
A split cannot be added twice, so the following will fail:
```py
split = (
datasets.Split.TRAIN.subsplit(datasets.percent[:25]) +
datasets.Split.TRAIN.subsplit(datasets.percent[75:])
) # Error
split = datasets.Split.TEST + datasets.Split.ALL # Error
```
The slices can be applied only one time. So the following are valid:
```py
split = (
datasets.Split.TRAIN.subsplit(datasets.percent[:25]) +
datasets.Split.TEST.subsplit(datasets.percent[:50])
)
split = (datasets.Split.TRAIN + datasets.Split.TEST).subsplit(datasets.percent[:50])
```
But this is not valid:
```py
train = datasets.Split.TRAIN
test = datasets.Split.TEST
split = train.subsplit(datasets.percent[:25]).subsplit(datasets.percent[:25])
split = (train.subsplit(datasets.percent[:25]) + test).subsplit(datasets.percent[:50])
```
"""
def __init__(self, name):
self._name = name
split_names_from_instruction = [split_instruction.split("[")[0] for split_instruction in name.split("+")]
for split_name in split_names_from_instruction:
if not re.match(_split_re, split_name):
raise ValueError(f"Split name should match '{_split_re}' but got '{split_name}'.")
def __str__(self):
return self._name
def __repr__(self):
return f"NamedSplit({self._name!r})"
def __eq__(self, other):
"""Equality: datasets.Split.TRAIN == 'train'."""
if isinstance(other, NamedSplit):
return self._name == other._name # pylint: disable=protected-access
elif isinstance(other, SplitBase):
return False
elif isinstance(other, str): # Other should be string
return self._name == other
else:
raise ValueError(f"Equality not supported between split {self} and {other}")
def __lt__(self, other):
return self._name < other._name # pylint: disable=protected-access
def __hash__(self):
return hash(self._name)
def get_read_instruction(self, split_dict):
return SplitReadInstruction(split_dict[self._name])
class NamedSplitAll(NamedSplit):
"""Split corresponding to the union of all defined dataset splits."""
def __init__(self):
super().__init__("all")
def __repr__(self):
return "NamedSplitAll()"
def get_read_instruction(self, split_dict):
# Merge all dataset split together
read_instructions = [SplitReadInstruction(s) for s in split_dict.values()]
return sum(read_instructions, SplitReadInstruction())
class Split:
# pylint: disable=line-too-long
"""`Enum` for dataset splits.
Datasets are typically split into different subsets to be used at various
stages of training and evaluation.
- `TRAIN`: the training data.
- `VALIDATION`: the validation data. If present, this is typically used as
evaluation data while iterating on a model (e.g. changing hyperparameters,
model architecture, etc.).
- `TEST`: the testing data. This is the data to report metrics on. Typically
you do not want to use this during model iteration as you may overfit to it.
- `ALL`: the union of all defined dataset splits.
All splits, including compositions inherit from `datasets.SplitBase`.
See the [guide](../load_hub#splits) on splits for more information.
Example:
```py
>>> datasets.SplitGenerator(
... name=datasets.Split.TRAIN,
... gen_kwargs={"split_key": "train", "files": dl_manager.download_and extract(url)},
... ),
... datasets.SplitGenerator(
... name=datasets.Split.VALIDATION,
... gen_kwargs={"split_key": "validation", "files": dl_manager.download_and extract(url)},
... ),
... datasets.SplitGenerator(
... name=datasets.Split.TEST,
... gen_kwargs={"split_key": "test", "files": dl_manager.download_and extract(url)},
... )
```
"""
# pylint: enable=line-too-long
TRAIN = NamedSplit("train")
TEST = NamedSplit("test")
VALIDATION = NamedSplit("validation")
ALL = NamedSplitAll()
def __new__(cls, name):
"""Create a custom split with datasets.Split('custom_name')."""
return NamedSplitAll() if name == "all" else NamedSplit(name)
# Similar to SplitInfo, but contain an additional slice info
SlicedSplitInfo = collections.namedtuple(
"SlicedSplitInfo",
[
"split_info",
"slice_value",
],
) # noqa: E231
class SplitReadInstruction:
"""Object containing the reading instruction for the dataset.
Similarly to `SplitDescriptor` nodes, this object can be composed with itself,
but the resolution happens instantaneously, instead of keeping track of the
tree, such as all instructions are compiled and flattened in a single
SplitReadInstruction object containing the list of files and slice to use.
Once resolved, the instructions can be accessed with:
```
read_instructions.get_list_sliced_split_info() # List of splits to use
```
"""
def __init__(self, split_info=None):
self._splits = NonMutableDict(error_msg="Overlap between splits. Split {key} has been added with " "itself.")
if split_info:
self.add(SlicedSplitInfo(split_info=split_info, slice_value=None))
def add(self, sliced_split):
"""Add a SlicedSplitInfo the read instructions."""
# TODO(epot): Check that the number of examples per shard % 100 == 0
# Otherwise the slices value may be unbalanced and not exactly reflect the
# requested slice.
self._splits[sliced_split.split_info.name] = sliced_split
def __add__(self, other):
"""Merging split together."""
# Will raise error if a split has already be added (NonMutableDict)
# TODO(epot): If a split is already added but there is no overlap between
# the slices, should merge the slices (ex: [:10] + [80:])
split_instruction = SplitReadInstruction()
split_instruction._splits.update(self._splits) # pylint: disable=protected-access
split_instruction._splits.update(other._splits) # pylint: disable=protected-access
return split_instruction
def __getitem__(self, slice_value):
"""Sub-splits."""
# Will raise an error if a split has already been sliced
split_instruction = SplitReadInstruction()
for v in self._splits.values():
if v.slice_value is not None:
raise ValueError(f"Trying to slice Split {v.split_info.name} which has already been sliced")
v = v._asdict()
v["slice_value"] = slice_value
split_instruction.add(SlicedSplitInfo(**v))
return split_instruction
def get_list_sliced_split_info(self):
return list(self._splits.values())
class SplitDict(dict):
"""Split info object."""
def __init__(self, *args, dataset_name=None, **kwargs):
super().__init__(*args, **kwargs)
self.dataset_name = dataset_name
def __getitem__(self, key: Union[SplitBase, str]):
# 1st case: The key exists: `info.splits['train']`
if str(key) in self:
return super().__getitem__(str(key))
# 2nd case: Uses instructions: `info.splits['train[50%]']`
else:
instructions = make_file_instructions(
name=self.dataset_name,
split_infos=self.values(),
instruction=key,
)
return SubSplitInfo(instructions)
def __setitem__(self, key: Union[SplitBase, str], value: SplitInfo):
if key != value.name:
raise ValueError(f"Cannot add elem. (key mismatch: '{key}' != '{value.name}')")
if key in self:
raise ValueError(f"Split {key} already present")
super().__setitem__(key, value)
def add(self, split_info: SplitInfo):
"""Add the split info."""
if split_info.name in self:
raise ValueError(f"Split {split_info.name} already present")
split_info.dataset_name = self.dataset_name
super().__setitem__(split_info.name, split_info)
@property
def total_num_examples(self):
"""Return the total number of examples."""
return sum(s.num_examples for s in self.values())
@classmethod
def from_split_dict(cls, split_infos: Union[List, Dict], dataset_name: Optional[str] = None):
"""Returns a new SplitDict initialized from a Dict or List of `split_infos`."""
if isinstance(split_infos, dict):
split_infos = list(split_infos.values())
if dataset_name is None:
dataset_name = split_infos[0].get("dataset_name") if split_infos else None
split_dict = cls(dataset_name=dataset_name)
for split_info in split_infos:
if isinstance(split_info, dict):
split_info = SplitInfo(**split_info)
split_dict.add(split_info)
return split_dict
def to_split_dict(self):
"""Returns a list of SplitInfo protos that we have."""
out = []
for split_name, split_info in self.items():
split_info = copy.deepcopy(split_info)
split_info.name = split_name
out.append(split_info)
return out
def copy(self):
return SplitDict.from_split_dict(self.to_split_dict(), self.dataset_name)
def _to_yaml_list(self) -> list:
out = [asdict(s) for s in self.to_split_dict()]
# we don't need the shard lengths in YAML, since it depends on max_shard_size and num_proc
for split_info_dict in out:
split_info_dict.pop("shard_lengths", None)
# we don't need the dataset_name attribute that is deprecated
for split_info_dict in out:
split_info_dict.pop("dataset_name", None)
return out
@classmethod
def _from_yaml_list(cls, yaml_data: list) -> "SplitDict":
return cls.from_split_dict(yaml_data)
@dataclass
class SplitGenerator:
"""Defines the split information for the generator.
This should be used as returned value of
`GeneratorBasedBuilder._split_generators`.
See `GeneratorBasedBuilder._split_generators` for more info and example
of usage.
Args:
name (`str`):
Name of the `Split` for which the generator will
create the examples.
**gen_kwargs (additional keyword arguments):
Keyword arguments to forward to the `DatasetBuilder._generate_examples` method
of the builder.
Example:
```py
>>> datasets.SplitGenerator(
... name=datasets.Split.TRAIN,
... gen_kwargs={"split_key": "train", "files": dl_manager.download_and_extract(url)},
... )
```
"""
name: str
gen_kwargs: Dict = dataclasses.field(default_factory=dict)
split_info: SplitInfo = dataclasses.field(init=False)
def __post_init__(self):
self.name = str(self.name) # Make sure we convert NamedSplits in strings
NamedSplit(self.name) # check that it's a valid split name
self.split_info = SplitInfo(name=self.name)
| datasets/src/datasets/splits.py/0 | {
"file_path": "datasets/src/datasets/splits.py",
"repo_id": "datasets",
"token_count": 9636
} | 68 |
import os
from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline
from .logging import get_logger
CHUNK_SIZE = 2 << 20 # 2mb
logger = get_logger(__name__)
class BeamPipeline(Pipeline):
"""Wrapper over `apache_beam.pipeline.Pipeline` for convenience"""
def is_local(self):
runner = self._options.get_all_options().get("runner")
return runner in [None, "DirectRunner", "PortableRunner"]
def upload_local_to_remote(local_file_path, remote_file_path, force_upload=False):
"""Use the Beam Filesystems to upload to a remote directory on gcs/s3/hdfs..."""
fs = FileSystems
if fs.exists(remote_file_path):
if force_upload:
logger.info(f"Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.")
else:
logger.info(f"Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.")
return
with fs.create(remote_file_path) as remote_file:
with open(local_file_path, "rb") as local_file:
chunk = local_file.read(CHUNK_SIZE)
while chunk:
remote_file.write(chunk)
chunk = local_file.read(CHUNK_SIZE)
def download_remote_to_local(remote_file_path, local_file_path, force_download=False):
"""Use the Beam Filesystems to download from a remote directory on gcs/s3/hdfs..."""
fs = FileSystems
if os.path.exists(local_file_path):
if force_download:
logger.info(f"Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.")
else:
logger.info(f"Local path already exist: {remote_file_path}. Skipping it as force_upload=False.")
return
with fs.open(remote_file_path) as remote_file:
with open(local_file_path, "wb") as local_file:
chunk = remote_file.read(CHUNK_SIZE)
while chunk:
local_file.write(chunk)
chunk = remote_file.read(CHUNK_SIZE)
| datasets/src/datasets/utils/beam_utils.py/0 | {
"file_path": "datasets/src/datasets/utils/beam_utils.py",
"repo_id": "datasets",
"token_count": 847
} | 69 |
{
"language": [
"found",
"crowdsourced",
"expert-generated",
"machine-generated",
"other"
],
"annotations": [
"found",
"crowdsourced",
"expert-generated",
"machine-generated",
"no-annotation",
"other"
]
}
| datasets/src/datasets/utils/resources/creators.json/0 | {
"file_path": "datasets/src/datasets/utils/resources/creators.json",
"repo_id": "datasets",
"token_count": 119
} | 70 |
## Add Dummy data test
**Important** In order to pass the `load_dataset_<dataset_name>` test, dummy data is required for all possible config names.
First we distinguish between datasets scripts that
- A) have no config class and
- B) have a config class
For A) the dummy data folder structure, will always look as follows:
- ``dummy/<version>/dummy_data.zip``, *e.g.* ``cosmos_qa/dummy/0.1.0/dummy_data.zip``.
For B) the dummy data folder structure, will always look as follows:
- ``dummy/<config_name>/<version>/dummy_data.zip``, *e.g.* ``squad/dummy/plain-text/1.0.0/dummy_data.zip``.
Now the difficult part is to create the correct `dummy_data.zip` file.
**Important** When checking the dummy folder structure of already added datasets, always unzip ``dummy_data.zip``. If a folder ``dummy_data`` is found next to ``dummy_data.zip``, it is probably an old version and should be deleted. The tests only take the ``dummy_data.zip`` file into account.
Here we have to pay close attention to the ``_split_generators(self, dl_manager)`` function of the dataset script in question.
There are three general possibilties:
1) The ``dl_manager.download_and_extract()`` is given a **single path variable** of type `str` as its argument. In this case the file `dummy_data.zip` should unzip to the following structure:
``os.path.join("dummy_data", <additional-paths-as-defined-in-split-generations>)`` *e.g.* for ``sentiment140``, the unzipped ``dummy_data.zip`` has the following dir structure ``dummy_data/testdata.manual.2009.06.14.csv`` and ``dummy_data/training.1600000.processed.noemoticon.csv``.
**Note** if there are no ``<additional-paths-as-defined-in-split-generations>``, then ``dummy_data`` should be the name of the single file. An example for this is the ``crime-and-punishment`` dataset script.
2) The ``dl_manager.download_and_extract()`` is given a **dictionary of paths** of type `str` as its argument. In this case the file `dummy_data.zip` should unzip to the following structure:
``os.path.join("dummy_data", <value_of_dict>.split('/')[-1], <additional-paths-as-defined-in-split-generations>)`` *e.g.* for ``squad``, the unzipped ``dummy_data.zip`` has the following dir structure ``dummy_data/dev-v1.1.json``, etc...
**Note** if ``<value_of_dict>`` is a zipped file then the dummy data folder structure should contain the exact name of the zipped file and the following extracted folder structure. The file `dummy_data.zip` should **never** itself contain a zipped file since the dummy data is not unzipped by the ``MockDownloadManager`` during testing. *E.g.* check the dummy folder structure of ``hansards`` where the folders have to be named ``*.tar`` or the structure of ``wiki_split`` where the folders have to be named ``*.zip``.
3) The ``dl_manager.download_and_extract()`` is given a **dictionary of lists of paths** of type `str` as its argument. This is a very special case and has been seen only for the dataset ``ensli``. In this case the values are simply flattened and the dummy folder structure is the same as in 2).
| datasets/tests/README.md/0 | {
"file_path": "datasets/tests/README.md",
"repo_id": "datasets",
"token_count": 928
} | 71 |
import os
import random
import tempfile
import unittest
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from absl.testing import parameterized
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import Array2D, Array3D, Array4D, Array5D, Value
from datasets.features.features import Array3DExtensionType, PandasArrayExtensionDtype, _ArrayXD
from datasets.formatting.formatting import NumpyArrowExtractor, SimpleArrowExtractor
SHAPE_TEST_1 = (30, 487)
SHAPE_TEST_2 = (36, 1024)
SHAPE_TEST_3 = (None, 100)
SPEED_TEST_SHAPE = (100, 100)
SPEED_TEST_N_EXAMPLES = 100
DEFAULT_FEATURES = datasets.Features(
{
"text": Array2D(SHAPE_TEST_1, dtype="float32"),
"image": Array2D(SHAPE_TEST_2, dtype="float32"),
"dynamic": Array2D(SHAPE_TEST_3, dtype="float32"),
}
)
def generate_examples(features: dict, num_examples=100, seq_shapes=None):
dummy_data = []
seq_shapes = seq_shapes or {}
for i in range(num_examples):
example = {}
for col_id, (k, v) in enumerate(features.items()):
if isinstance(v, _ArrayXD):
if k == "dynamic":
first_dim = random.randint(1, 3)
data = np.random.rand(first_dim, *v.shape[1:]).astype(v.dtype)
else:
data = np.random.rand(*v.shape).astype(v.dtype)
elif isinstance(v, datasets.Value):
data = "foo"
elif isinstance(v, datasets.Sequence):
while isinstance(v, datasets.Sequence):
v = v.feature
shape = seq_shapes[k]
data = np.random.rand(*shape).astype(v.dtype)
example[k] = data
dummy_data.append((i, example))
return dummy_data
class ExtensionTypeCompatibilityTest(unittest.TestCase):
def test_array2d_nonspecific_shape(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(
features=my_features,
num_examples=1,
):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
dataset.set_format("numpy")
row = dataset[0]
first_shape = row["image"].shape
second_shape = row["text"].shape
self.assertTrue(first_shape is not None and second_shape is not None, "need atleast 2 different shapes")
self.assertEqual(len(first_shape), len(second_shape), "both shapes are supposed to be equal length")
self.assertNotEqual(first_shape, second_shape, "shapes must not be the same")
del dataset
def test_multiple_extensions_same_row(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(features=my_features, num_examples=1):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
dataset.set_format("numpy")
row = dataset[0]
first_len = len(row["image"].shape)
second_len = len(row["text"].shape)
third_len = len(row["dynamic"].shape)
self.assertEqual(first_len, 2, "use a sequence type if dim is < 2")
self.assertEqual(second_len, 2, "use a sequence type if dim is < 2")
self.assertEqual(third_len, 2, "use a sequence type if dim is < 2")
del dataset
def test_compatability_with_string_values(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
my_features["image_id"] = datasets.Value("string")
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(features=my_features, num_examples=1):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
self.assertIsInstance(dataset[0]["image_id"], str, "image id must be of type string")
del dataset
def test_extension_indexing(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
my_features["explicit_ext"] = Array2D((3, 3), dtype="float32")
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(features=my_features, num_examples=1):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
dataset.set_format("numpy")
data = dataset[0]["explicit_ext"]
self.assertIsInstance(data, np.ndarray, "indexed extension must return numpy.ndarray")
del dataset
def get_array_feature_types():
shape_1 = [3] * 5
shape_2 = [3, 4, 5, 6, 7]
return [
{
"testcase_name": f"{d}d",
"array_feature": array_feature,
"shape_1": tuple(shape_1[:d]),
"shape_2": tuple(shape_2[:d]),
}
for d, array_feature in zip(range(2, 6), [Array2D, Array3D, Array4D, Array5D])
]
@parameterized.named_parameters(get_array_feature_types())
class ArrayXDTest(unittest.TestCase):
def get_features(self, array_feature, shape_1, shape_2):
return datasets.Features(
{
"image": array_feature(shape_1, dtype="float32"),
"source": Value("string"),
"matrix": array_feature(shape_2, dtype="float32"),
}
)
def get_dict_example_0(self, shape_1, shape_2):
return {
"image": np.random.rand(*shape_1).astype("float32"),
"source": "foo",
"matrix": np.random.rand(*shape_2).astype("float32"),
}
def get_dict_example_1(self, shape_1, shape_2):
return {
"image": np.random.rand(*shape_1).astype("float32"),
"matrix": np.random.rand(*shape_2).astype("float32"),
"source": "bar",
}
def get_dict_examples(self, shape_1, shape_2):
return {
"image": np.random.rand(2, *shape_1).astype("float32").tolist(),
"source": ["foo", "bar"],
"matrix": np.random.rand(2, *shape_2).astype("float32").tolist(),
}
def _check_getitem_output_type(self, dataset, shape_1, shape_2, first_matrix):
matrix_column = dataset["matrix"]
self.assertIsInstance(matrix_column, list)
self.assertIsInstance(matrix_column[0], list)
self.assertIsInstance(matrix_column[0][0], list)
self.assertTupleEqual(np.array(matrix_column).shape, (2, *shape_2))
matrix_field_of_first_example = dataset[0]["matrix"]
self.assertIsInstance(matrix_field_of_first_example, list)
self.assertIsInstance(matrix_field_of_first_example, list)
self.assertEqual(np.array(matrix_field_of_first_example).shape, shape_2)
np.testing.assert_array_equal(np.array(matrix_field_of_first_example), np.array(first_matrix))
matrix_field_of_first_two_examples = dataset[:2]["matrix"]
self.assertIsInstance(matrix_field_of_first_two_examples, list)
self.assertIsInstance(matrix_field_of_first_two_examples[0], list)
self.assertIsInstance(matrix_field_of_first_two_examples[0][0], list)
self.assertTupleEqual(np.array(matrix_field_of_first_two_examples).shape, (2, *shape_2))
with dataset.formatted_as("numpy"):
self.assertTupleEqual(dataset["matrix"].shape, (2, *shape_2))
self.assertEqual(dataset[0]["matrix"].shape, shape_2)
self.assertTupleEqual(dataset[:2]["matrix"].shape, (2, *shape_2))
with dataset.formatted_as("pandas"):
self.assertIsInstance(dataset["matrix"], pd.Series)
self.assertIsInstance(dataset[0]["matrix"], pd.Series)
self.assertIsInstance(dataset[:2]["matrix"], pd.Series)
self.assertTupleEqual(dataset["matrix"].to_numpy().shape, (2, *shape_2))
self.assertTupleEqual(dataset[0]["matrix"].to_numpy().shape, (1, *shape_2))
self.assertTupleEqual(dataset[:2]["matrix"].to_numpy().shape, (2, *shape_2))
def test_write(self, array_feature, shape_1, shape_2):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = self.get_features(array_feature, shape_1, shape_2)
my_examples = [
(0, self.get_dict_example_0(shape_1, shape_2)),
(1, self.get_dict_example_1(shape_1, shape_2)),
]
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in my_examples:
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
self._check_getitem_output_type(dataset, shape_1, shape_2, my_examples[0][1]["matrix"])
del dataset
def test_write_batch(self, array_feature, shape_1, shape_2):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = self.get_features(array_feature, shape_1, shape_2)
dict_examples = self.get_dict_examples(shape_1, shape_2)
dict_examples = my_features.encode_batch(dict_examples)
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
writer.write_batch(dict_examples)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
self._check_getitem_output_type(dataset, shape_1, shape_2, dict_examples["matrix"][0])
del dataset
def test_from_dict(self, array_feature, shape_1, shape_2):
dict_examples = self.get_dict_examples(shape_1, shape_2)
dataset = datasets.Dataset.from_dict(
dict_examples, features=self.get_features(array_feature, shape_1, shape_2)
)
self._check_getitem_output_type(dataset, shape_1, shape_2, dict_examples["matrix"][0])
del dataset
class ArrayXDDynamicTest(unittest.TestCase):
def get_one_col_dataset(self, first_dim_list, fixed_shape):
features = datasets.Features({"image": Array3D(shape=(None, *fixed_shape), dtype="float32")})
dict_values = {"image": [np.random.rand(fdim, *fixed_shape).astype("float32") for fdim in first_dim_list]}
dataset = datasets.Dataset.from_dict(dict_values, features=features)
return dataset
def get_two_col_datasset(self, first_dim_list, fixed_shape):
features = datasets.Features(
{"image": Array3D(shape=(None, *fixed_shape), dtype="float32"), "text": Value("string")}
)
dict_values = {
"image": [np.random.rand(fdim, *fixed_shape).astype("float32") for fdim in first_dim_list],
"text": ["text" for _ in first_dim_list],
}
dataset = datasets.Dataset.from_dict(dict_values, features=features)
return dataset
def test_to_pylist(self):
fixed_shape = (2, 2)
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
arr_xd = SimpleArrowExtractor().extract_column(dataset._data)
self.assertIsInstance(arr_xd.type, Array3DExtensionType)
pylist = arr_xd.to_pylist()
for first_dim, single_arr in zip(first_dim_list, pylist):
self.assertIsInstance(single_arr, list)
self.assertTupleEqual(np.array(single_arr).shape, (first_dim, *fixed_shape))
def test_to_numpy(self):
fixed_shape = (2, 2)
# ragged
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
arr_xd = SimpleArrowExtractor().extract_column(dataset._data)
self.assertIsInstance(arr_xd.type, Array3DExtensionType)
# replace with arr_xd = arr_xd.combine_chunks() when 12.0.0 will be the minimal required PyArrow version
arr_xd = arr_xd.type.wrap_array(pa.concat_arrays([chunk.storage for chunk in arr_xd.chunks]))
numpy_arr = arr_xd.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
# non-ragged
first_dim_list = [4, 4, 4]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
arr_xd = SimpleArrowExtractor().extract_column(dataset._data)
self.assertIsInstance(arr_xd.type, Array3DExtensionType)
# replace with arr_xd = arr_xd.combine_chunks() when 12.0.0 will be the minimal required PyArrow version
arr_xd = arr_xd.type.wrap_array(pa.concat_arrays([chunk.storage for chunk in arr_xd.chunks]))
numpy_arr = arr_xd.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertNotEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
def test_iter_dataset(self):
fixed_shape = (2, 2)
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
for first_dim, ds_row in zip(first_dim_list, dataset):
single_arr = ds_row["image"]
self.assertIsInstance(single_arr, list)
self.assertTupleEqual(np.array(single_arr).shape, (first_dim, *fixed_shape))
def test_to_pandas(self):
fixed_shape = (2, 2)
# ragged
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
df = dataset.to_pandas()
self.assertEqual(type(df.image.dtype), PandasArrayExtensionDtype)
numpy_arr = df.image.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
# non-ragged
first_dim_list = [4, 4, 4]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
df = dataset.to_pandas()
self.assertEqual(type(df.image.dtype), PandasArrayExtensionDtype)
numpy_arr = df.image.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertNotEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
def test_map_dataset(self):
fixed_shape = (2, 2)
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
dataset = dataset.map(lambda a: {"image": np.concatenate([a] * 2)}, input_columns="image")
# check also if above function resulted with 2x bigger first dim
for first_dim, ds_row in zip(first_dim_list, dataset):
single_arr = ds_row["image"]
self.assertIsInstance(single_arr, list)
self.assertTupleEqual(np.array(single_arr).shape, (first_dim * 2, *fixed_shape))
@pytest.mark.parametrize("dtype, dummy_value", [("int32", 1), ("bool", True), ("float64", 1)])
def test_table_to_pandas(dtype, dummy_value):
features = datasets.Features({"foo": datasets.Array2D(dtype=dtype, shape=(2, 2))})
dataset = datasets.Dataset.from_dict({"foo": [[[dummy_value] * 2] * 2]}, features=features)
df = dataset._data.to_pandas()
assert type(df.foo.dtype) == PandasArrayExtensionDtype
arr = df.foo.to_numpy()
np.testing.assert_equal(arr, np.array([[[dummy_value] * 2] * 2], dtype=np.dtype(dtype)))
@pytest.mark.parametrize("dtype, dummy_value", [("int32", 1), ("bool", True), ("float64", 1)])
def test_array_xd_numpy_arrow_extractor(dtype, dummy_value):
features = datasets.Features({"foo": datasets.Array2D(dtype=dtype, shape=(2, 2))})
dataset = datasets.Dataset.from_dict({"foo": [[[dummy_value] * 2] * 2]}, features=features)
arr = NumpyArrowExtractor().extract_column(dataset._data)
assert isinstance(arr, np.ndarray)
np.testing.assert_equal(arr, np.array([[[dummy_value] * 2] * 2], dtype=np.dtype(dtype)))
def test_array_xd_with_none():
# Fixed shape
features = datasets.Features({"foo": datasets.Array2D(dtype="int32", shape=(2, 2))})
dummy_array = np.array([[1, 2], [3, 4]], dtype="int32")
dataset = datasets.Dataset.from_dict({"foo": [dummy_array, None, dummy_array, None]}, features=features)
arr = NumpyArrowExtractor().extract_column(dataset._data)
assert isinstance(arr, np.ndarray) and arr.dtype == np.float64 and arr.shape == (4, 2, 2)
assert np.allclose(arr[0], dummy_array) and np.allclose(arr[2], dummy_array)
assert np.all(np.isnan(arr[1])) and np.all(np.isnan(arr[3])) # broadcasted np.nan - use np.all
# Dynamic shape
features = datasets.Features({"foo": datasets.Array2D(dtype="int32", shape=(None, 2))})
dummy_array = np.array([[1, 2], [3, 4]], dtype="int32")
dataset = datasets.Dataset.from_dict({"foo": [dummy_array, None, dummy_array, None]}, features=features)
arr = NumpyArrowExtractor().extract_column(dataset._data)
assert isinstance(arr, np.ndarray) and arr.dtype == object and arr.shape == (4,)
np.testing.assert_equal(arr[0], dummy_array)
np.testing.assert_equal(arr[2], dummy_array)
assert np.isnan(arr[1]) and np.isnan(arr[3]) # a single np.nan value - np.all not needed
@pytest.mark.parametrize("seq_type", ["no_sequence", "sequence", "sequence_of_sequence"])
@pytest.mark.parametrize(
"dtype",
[
"bool",
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float16",
"float32",
"float64",
],
)
@pytest.mark.parametrize("shape, feature_class", [((2, 3), datasets.Array2D), ((2, 3, 4), datasets.Array3D)])
def test_array_xd_with_np(seq_type, dtype, shape, feature_class):
feature = feature_class(dtype=dtype, shape=shape)
data = np.zeros(shape, dtype=dtype)
expected = data.tolist()
if seq_type == "sequence":
feature = datasets.Sequence(feature)
data = [data]
expected = [expected]
elif seq_type == "sequence_of_sequence":
feature = datasets.Sequence(datasets.Sequence(feature))
data = [[data]]
expected = [[expected]]
ds = datasets.Dataset.from_dict({"col": [data]}, features=datasets.Features({"col": feature}))
assert ds[0]["col"] == expected
@pytest.mark.parametrize("with_none", [False, True])
def test_dataset_map(with_none):
ds = datasets.Dataset.from_dict({"path": ["path1", "path2"]})
def process_data(batch):
batch = {
"image": [
np.array(
[
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
[[10, 20, 30], [40, 50, 60], [70, 80, 90]],
[[100, 200, 300], [400, 500, 600], [700, 800, 900]],
]
)
for _ in batch["path"]
]
}
if with_none:
batch["image"][0] = None
return batch
features = datasets.Features({"image": Array3D(dtype="int32", shape=(3, 3, 3))})
processed_ds = ds.map(process_data, batched=True, remove_columns=ds.column_names, features=features)
assert processed_ds.shape == (2, 1)
with processed_ds.with_format("numpy") as pds:
for i, example in enumerate(pds):
assert "image" in example
assert isinstance(example["image"], np.ndarray)
assert example["image"].shape == (3, 3, 3)
if with_none and i == 0:
assert np.all(np.isnan(example["image"]))
| datasets/tests/features/test_array_xd.py/0 | {
"file_path": "datasets/tests/features/test_array_xd.py",
"repo_id": "datasets",
"token_count": 9826
} | 72 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _check_text_dataset(dataset, expected_features):
assert isinstance(dataset, Dataset)
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_dataset_from_text_keep_in_memory(keep_in_memory, text_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = TextDatasetReader(text_path, cache_dir=cache_dir, keep_in_memory=keep_in_memory).read()
_check_text_dataset(dataset, expected_features)
@pytest.mark.parametrize(
"features",
[
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
],
)
def test_dataset_from_text_features(features, text_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {"text": "string"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = TextDatasetReader(text_path, features=features, cache_dir=cache_dir).read()
_check_text_dataset(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_dataset_from_text_split(split, text_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
dataset = TextDatasetReader(text_path, cache_dir=cache_dir, split=split).read()
_check_text_dataset(dataset, expected_features)
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type", [str, list])
def test_dataset_from_text_path_type(path_type, text_path, tmp_path):
if issubclass(path_type, str):
path = text_path
elif issubclass(path_type, list):
path = [text_path]
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
dataset = TextDatasetReader(path, cache_dir=cache_dir).read()
_check_text_dataset(dataset, expected_features)
def _check_text_datasetdict(dataset_dict, expected_features, splits=("train",)):
assert isinstance(dataset_dict, DatasetDict)
for split in splits:
dataset = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_datasetdict_from_text_keep_in_memory(keep_in_memory, text_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = TextDatasetReader({"train": text_path}, cache_dir=cache_dir, keep_in_memory=keep_in_memory).read()
_check_text_datasetdict(dataset, expected_features)
@pytest.mark.parametrize(
"features",
[
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
],
)
def test_datasetdict_from_text_features(features, text_path, tmp_path):
cache_dir = tmp_path / "cache"
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
default_expected_features = {"text": "string"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = TextDatasetReader({"train": text_path}, features=features, cache_dir=cache_dir).read()
_check_text_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_datasetdict_from_text_split(split, text_path, tmp_path):
if split:
path = {split: text_path}
else:
split = "train"
path = {"train": text_path, "test": text_path}
cache_dir = tmp_path / "cache"
expected_features = {"text": "string"}
dataset = TextDatasetReader(path, cache_dir=cache_dir).read()
_check_text_datasetdict(dataset, expected_features, splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
| datasets/tests/io/test_text.py/0 | {
"file_path": "datasets/tests/io/test_text.py",
"repo_id": "datasets",
"token_count": 1833
} | 73 |
import copy
import os
from pathlib import Path
from typing import List
from unittest.mock import patch
import fsspec
import pytest
from fsspec.registry import _registry as _fsspec_registry
from fsspec.spec import AbstractFileSystem
from datasets.data_files import (
DataFilesDict,
DataFilesList,
DataFilesPatternsDict,
DataFilesPatternsList,
_get_data_files_patterns,
_get_metadata_files_patterns,
_is_inside_unrequested_special_dir,
_is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir,
get_data_patterns,
resolve_pattern,
)
from datasets.fingerprint import Hasher
_TEST_PATTERNS = ["*", "**", "**/*", "*.txt", "data/*", "**/*.txt", "**/train.txt"]
_FILES_TO_IGNORE = {".dummy", "README.md", "dummy_data.zip", "dataset_infos.json"}
_DIRS_TO_IGNORE = {"data/.dummy_subdir", "__pycache__"}
_TEST_PATTERNS_SIZES = {
"*": 0,
"**": 4,
"**/*": 4,
"*.txt": 0,
"data/*": 2,
"data/**": 4,
"**/*.txt": 4,
"**/train.txt": 2,
}
_TEST_URL = "https://raw.githubusercontent.com/huggingface/datasets/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d/setup.py"
@pytest.fixture
def complex_data_dir(tmp_path):
data_dir = tmp_path / "complex_data_dir"
data_dir.mkdir()
(data_dir / "data").mkdir()
with open(data_dir / "data" / "train.txt", "w") as f:
f.write("foo\n" * 10)
with open(data_dir / "data" / "test.txt", "w") as f:
f.write("bar\n" * 10)
with open(data_dir / "README.md", "w") as f:
f.write("This is a readme")
with open(data_dir / ".dummy", "w") as f:
f.write("this is a dummy file that is not a data file")
(data_dir / "data" / "subdir").mkdir()
with open(data_dir / "data" / "subdir" / "train.txt", "w") as f:
f.write("foo\n" * 10)
with open(data_dir / "data" / "subdir" / "test.txt", "w") as f:
f.write("bar\n" * 10)
(data_dir / "data" / ".dummy_subdir").mkdir()
with open(data_dir / "data" / ".dummy_subdir" / "train.txt", "w") as f:
f.write("foo\n" * 10)
with open(data_dir / "data" / ".dummy_subdir" / "test.txt", "w") as f:
f.write("bar\n" * 10)
(data_dir / "__pycache__").mkdir()
with open(data_dir / "__pycache__" / "script.py", "w") as f:
f.write("foo\n" * 10)
return str(data_dir)
def is_relative_to(path, *other):
# A built-in method in Python 3.9+
try:
path.relative_to(*other)
return True
except ValueError:
return False
@pytest.fixture
def pattern_results(complex_data_dir):
# We use fsspec glob as a reference for data files resolution from patterns.
# This is the same as dask for example.
#
# /!\ Here are some behaviors specific to fsspec glob that are different from glob.glob, Path.glob, Path.match or fnmatch:
# - '*' matches only first level items
# - '**' matches all items
# - '**/*' matches all at least second level items
#
# More generally:
# - '*' matches any character except a forward-slash (to match just the file or directory name)
# - '**' matches any character including a forward-slash /
return {
pattern: sorted(
Path(os.path.abspath(path)).as_posix()
for path in fsspec.filesystem("file").glob(os.path.join(complex_data_dir, pattern))
if Path(path).name not in _FILES_TO_IGNORE
and not any(
is_relative_to(Path(path), os.path.join(complex_data_dir, dir_path)) for dir_path in _DIRS_TO_IGNORE
)
and Path(path).is_file()
)
for pattern in _TEST_PATTERNS
}
@pytest.fixture
def hub_dataset_repo_path(tmpfs, complex_data_dir):
for path in Path(complex_data_dir).rglob("*"):
if path.is_file():
with tmpfs.open(path.relative_to(complex_data_dir).as_posix(), "wb") as f:
f.write(path.read_bytes())
yield "tmp://"
@pytest.fixture
def hub_dataset_repo_patterns_results(hub_dataset_repo_path, complex_data_dir, pattern_results):
return {
pattern: [
hub_dataset_repo_path + Path(path).relative_to(complex_data_dir).as_posix()
for path in pattern_results[pattern]
]
for pattern in pattern_results
}
def test_is_inside_unrequested_special_dir(complex_data_dir, pattern_results):
# usual patterns outside special dir work fine
for pattern, result in pattern_results.items():
if result:
matched_rel_path = str(Path(result[0]).relative_to(complex_data_dir))
assert _is_inside_unrequested_special_dir(matched_rel_path, pattern) is False
# check behavior for special dir
f = _is_inside_unrequested_special_dir
assert f("__pycache__/b.txt", "**") is True
assert f("__pycache__/b.txt", "*/b.txt") is True
assert f("__pycache__/b.txt", "__pycache__/*") is False
assert f("__pycache__/__b.txt", "__pycache__/*") is False
assert f("__pycache__/__b.txt", "__*/*") is False
assert f("__b.txt", "*") is False
def test_is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(complex_data_dir, pattern_results):
# usual patterns outside hidden dir work fine
for pattern, result in pattern_results.items():
if result:
matched_rel_path = str(Path(result[0]).relative_to(complex_data_dir))
assert _is_inside_unrequested_special_dir(matched_rel_path, pattern) is False
# check behavior for hidden dir and file
f = _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir
assert f(".hidden_file.txt", "**") is True
assert f(".hidden_file.txt", ".*") is False
assert f(".hidden_dir/a.txt", "**") is True
assert f(".hidden_dir/a.txt", ".*/*") is False
assert f(".hidden_dir/a.txt", ".hidden_dir/*") is False
assert f(".hidden_dir/.hidden_file.txt", "**") is True
assert f(".hidden_dir/.hidden_file.txt", ".*/*") is True
assert f(".hidden_dir/.hidden_file.txt", ".*/.*") is False
assert f(".hidden_dir/.hidden_file.txt", ".hidden_dir/*") is True
assert f(".hidden_dir/.hidden_file.txt", ".hidden_dir/.*") is False
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_pattern_results_fixture(pattern_results, pattern):
assert len(pattern_results[pattern]) == _TEST_PATTERNS_SIZES[pattern]
assert all(Path(path).is_file() for path in pattern_results[pattern])
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_resolve_pattern_locally(complex_data_dir, pattern, pattern_results):
try:
resolved_data_files = resolve_pattern(pattern, complex_data_dir)
assert sorted(str(f) for f in resolved_data_files) == pattern_results[pattern]
except FileNotFoundError:
assert len(pattern_results[pattern]) == 0
def test_resolve_pattern_locally_with_dot_in_base_path(complex_data_dir):
base_path_with_dot = os.path.join(complex_data_dir, "data", ".dummy_subdir")
resolved_data_files = resolve_pattern(os.path.join(base_path_with_dot, "train.txt"), base_path_with_dot)
assert len(resolved_data_files) == 1
def test_resolve_pattern_locally_with_absolute_path(tmp_path, complex_data_dir):
abs_path = os.path.join(complex_data_dir, "data", "train.txt")
resolved_data_files = resolve_pattern(abs_path, str(tmp_path / "blabla"))
assert len(resolved_data_files) == 1
def test_resolve_pattern_locally_with_double_dots(tmp_path, complex_data_dir):
path_with_double_dots = os.path.join(complex_data_dir, "data", "subdir", "..", "train.txt")
resolved_data_files = resolve_pattern(path_with_double_dots, str(tmp_path / "blabla"))
assert len(resolved_data_files) == 1
def test_resolve_pattern_locally_returns_hidden_file_only_if_requested(complex_data_dir):
with pytest.raises(FileNotFoundError):
resolve_pattern("*dummy", complex_data_dir)
resolved_data_files = resolve_pattern(".dummy", complex_data_dir)
assert len(resolved_data_files) == 1
def test_resolve_pattern_locally_hidden_base_path(tmp_path):
hidden = tmp_path / ".test_hidden_base_path"
hidden.mkdir()
(tmp_path / ".test_hidden_base_path" / "a.txt").touch()
resolved_data_files = resolve_pattern("*", str(hidden))
assert len(resolved_data_files) == 1
def test_resolve_pattern_locallyreturns_hidden_dir_only_if_requested(complex_data_dir):
with pytest.raises(FileNotFoundError):
resolve_pattern("data/*dummy_subdir/train.txt", complex_data_dir)
resolved_data_files = resolve_pattern("data/.dummy_subdir/train.txt", complex_data_dir)
assert len(resolved_data_files) == 1
resolved_data_files = resolve_pattern("*/.dummy_subdir/train.txt", complex_data_dir)
assert len(resolved_data_files) == 1
def test_resolve_pattern_locally_returns_special_dir_only_if_requested(complex_data_dir):
with pytest.raises(FileNotFoundError):
resolve_pattern("data/*dummy_subdir/train.txt", complex_data_dir)
resolved_data_files = resolve_pattern("data/.dummy_subdir/train.txt", complex_data_dir)
assert len(resolved_data_files) == 1
resolved_data_files = resolve_pattern("*/.dummy_subdir/train.txt", complex_data_dir)
assert len(resolved_data_files) == 1
def test_resolve_pattern_locally_special_base_path(tmp_path):
special = tmp_path / "__test_special_base_path__"
special.mkdir()
(tmp_path / "__test_special_base_path__" / "a.txt").touch()
resolved_data_files = resolve_pattern("*", str(special))
assert len(resolved_data_files) == 1
@pytest.mark.parametrize("pattern,size,extensions", [("**", 4, [".txt"]), ("**", 4, None), ("**", 0, [".blablabla"])])
def test_resolve_pattern_locally_with_extensions(complex_data_dir, pattern, size, extensions):
if size > 0:
resolved_data_files = resolve_pattern(pattern, complex_data_dir, allowed_extensions=extensions)
assert len(resolved_data_files) == size
else:
with pytest.raises(FileNotFoundError):
resolve_pattern(pattern, complex_data_dir, allowed_extensions=extensions)
def test_fail_resolve_pattern_locally(complex_data_dir):
with pytest.raises(FileNotFoundError):
resolve_pattern(complex_data_dir, ["blablabla"])
@pytest.mark.skipif(os.name == "nt", reason="Windows does not support symlinks in the default mode")
def test_resolve_pattern_locally_does_not_resolve_symbolic_links(tmp_path, complex_data_dir):
(tmp_path / "train_data_symlink.txt").symlink_to(os.path.join(complex_data_dir, "data", "train.txt"))
resolved_data_files = resolve_pattern("train_data_symlink.txt", str(tmp_path))
assert len(resolved_data_files) == 1
assert Path(resolved_data_files[0]) == tmp_path / "train_data_symlink.txt"
def test_resolve_pattern_locally_sorted_files(tmp_path_factory):
path = str(tmp_path_factory.mktemp("unsorted_text_files"))
unsorted_names = ["0.txt", "2.txt", "3.txt"]
for name in unsorted_names:
with open(os.path.join(path, name), "w"):
pass
resolved_data_files = resolve_pattern("*", path)
resolved_names = [os.path.basename(data_file) for data_file in resolved_data_files]
assert resolved_names == sorted(unsorted_names)
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_resolve_pattern_in_dataset_repository(hub_dataset_repo_path, pattern, hub_dataset_repo_patterns_results):
try:
resolved_data_files = resolve_pattern(pattern, hub_dataset_repo_path)
assert sorted(str(f) for f in resolved_data_files) == hub_dataset_repo_patterns_results[pattern]
except FileNotFoundError:
assert len(hub_dataset_repo_patterns_results[pattern]) == 0
@pytest.mark.parametrize(
"pattern,size,base_path", [("**", 4, None), ("**", 4, "data"), ("**", 2, "data/subdir"), ("**", 0, "data/subdir2")]
)
def test_resolve_pattern_in_dataset_repository_with_base_path(hub_dataset_repo_path, pattern, size, base_path):
base_path = hub_dataset_repo_path + (base_path or "")
if size > 0:
resolved_data_files = resolve_pattern(pattern, base_path)
assert len(resolved_data_files) == size
else:
with pytest.raises(FileNotFoundError):
resolve_pattern(pattern, base_path)
@pytest.mark.parametrize("pattern,size,extensions", [("**", 4, [".txt"]), ("**", 4, None), ("**", 0, [".blablabla"])])
def test_resolve_pattern_in_dataset_repository_with_extensions(hub_dataset_repo_path, pattern, size, extensions):
if size > 0:
resolved_data_files = resolve_pattern(pattern, hub_dataset_repo_path, allowed_extensions=extensions)
assert len(resolved_data_files) == size
else:
with pytest.raises(FileNotFoundError):
resolved_data_files = resolve_pattern(pattern, hub_dataset_repo_path, allowed_extensions=extensions)
def test_fail_resolve_pattern_in_dataset_repository(hub_dataset_repo_path):
with pytest.raises(FileNotFoundError):
resolve_pattern("blablabla", hub_dataset_repo_path)
def test_resolve_pattern_in_dataset_repository_returns_hidden_file_only_if_requested(hub_dataset_repo_path):
with pytest.raises(FileNotFoundError):
resolve_pattern("*dummy", hub_dataset_repo_path)
resolved_data_files = resolve_pattern(".dummy", hub_dataset_repo_path)
assert len(resolved_data_files) == 1
def test_resolve_pattern_in_dataset_repository_hidden_base_path(tmpfs):
tmpfs.touch(".hidden/a.txt")
resolved_data_files = resolve_pattern("*", base_path="tmp://.hidden")
assert len(resolved_data_files) == 1
def test_resolve_pattern_in_dataset_repository_returns_hidden_dir_only_if_requested(hub_dataset_repo_path):
with pytest.raises(FileNotFoundError):
resolve_pattern("data/*dummy_subdir/train.txt", hub_dataset_repo_path)
resolved_data_files = resolve_pattern("data/.dummy_subdir/train.txt", hub_dataset_repo_path)
assert len(resolved_data_files) == 1
resolved_data_files = resolve_pattern("*/.dummy_subdir/train.txt", hub_dataset_repo_path)
assert len(resolved_data_files) == 1
def test_resolve_pattern_in_dataset_repository_returns_special_dir_only_if_requested(hub_dataset_repo_path):
with pytest.raises(FileNotFoundError):
resolve_pattern("data/*dummy_subdir/train.txt", hub_dataset_repo_path)
resolved_data_files = resolve_pattern("data/.dummy_subdir/train.txt", hub_dataset_repo_path)
assert len(resolved_data_files) == 1
resolved_data_files = resolve_pattern("*/.dummy_subdir/train.txt", hub_dataset_repo_path)
assert len(resolved_data_files) == 1
def test_resolve_pattern_in_dataset_repository_special_base_path(tmpfs):
tmpfs.touch("__special__/a.txt")
resolved_data_files = resolve_pattern("*", base_path="tmp://__special__")
assert len(resolved_data_files) == 1
@pytest.fixture
def dummy_fs():
DummyTestFS = mock_fs(["train.txt", "test.txt"])
_fsspec_registry["mock"] = DummyTestFS
_fsspec_registry["dummy"] = DummyTestFS
yield
del _fsspec_registry["mock"]
del _fsspec_registry["dummy"]
def test_resolve_pattern_fs(dummy_fs):
resolved_data_files = resolve_pattern("mock://train.txt", base_path="")
assert resolved_data_files == ["mock://train.txt"]
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_DataFilesList_from_patterns_in_dataset_repository_(
hub_dataset_repo_path, hub_dataset_repo_patterns_results, pattern
):
try:
data_files_list = DataFilesList.from_patterns([pattern], hub_dataset_repo_path)
assert sorted(data_files_list) == hub_dataset_repo_patterns_results[pattern]
assert len(data_files_list.origin_metadata) == len(data_files_list)
except FileNotFoundError:
assert len(hub_dataset_repo_patterns_results[pattern]) == 0
def test_DataFilesList_from_patterns_locally_with_extra_files(complex_data_dir, text_file):
data_files_list = DataFilesList.from_patterns([_TEST_URL, text_file.as_posix()], complex_data_dir)
assert list(data_files_list) == [_TEST_URL, text_file.as_posix()]
assert len(data_files_list.origin_metadata) == 2
def test_DataFilesList_from_patterns_raises_FileNotFoundError(complex_data_dir):
with pytest.raises(FileNotFoundError):
DataFilesList.from_patterns(["file_that_doesnt_exist.txt"], complex_data_dir)
class TestDataFilesDict:
def test_key_order_after_copy(self):
data_files = DataFilesDict({"train": "train.csv", "test": "test.csv"})
copied_data_files = copy.deepcopy(data_files)
assert list(copied_data_files.keys()) == list(data_files.keys()) # test split order with list()
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_DataFilesDict_from_patterns_in_dataset_repository(
hub_dataset_repo_path, hub_dataset_repo_patterns_results, pattern
):
split_name = "train"
try:
data_files = DataFilesDict.from_patterns({split_name: [pattern]}, hub_dataset_repo_path)
assert all(isinstance(data_files_list, DataFilesList) for data_files_list in data_files.values())
assert sorted(data_files[split_name]) == hub_dataset_repo_patterns_results[pattern]
except FileNotFoundError:
assert len(hub_dataset_repo_patterns_results[pattern]) == 0
@pytest.mark.parametrize(
"pattern,size,base_path,split_name",
[
("**", 4, None, "train"),
("**", 4, "data", "train"),
("**", 2, "data/subdir", "train"),
("**train*", 1, "data/subdir", "train"),
("**test*", 1, "data/subdir", "test"),
("**", 0, "data/subdir2", "train"),
],
)
def test_DataFilesDict_from_patterns_in_dataset_repository_with_base_path(
hub_dataset_repo_path, pattern, size, base_path, split_name
):
base_path = hub_dataset_repo_path + (base_path or "")
if size > 0:
data_files = DataFilesDict.from_patterns({split_name: [pattern]}, base_path=base_path)
assert len(data_files[split_name]) == size
else:
with pytest.raises(FileNotFoundError):
resolve_pattern(pattern, base_path)
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_DataFilesDict_from_patterns_locally(complex_data_dir, pattern_results, pattern):
split_name = "train"
try:
data_files = DataFilesDict.from_patterns({split_name: [pattern]}, complex_data_dir)
assert all(isinstance(data_files_list, DataFilesList) for data_files_list in data_files.values())
assert sorted(data_files[split_name]) == pattern_results[pattern]
except FileNotFoundError:
assert len(pattern_results[pattern]) == 0
def test_DataFilesDict_from_patterns_in_dataset_repository_hashing(hub_dataset_repo_path):
patterns = {"train": ["**/train.txt"], "test": ["**/test.txt"]}
data_files1 = DataFilesDict.from_patterns(patterns, hub_dataset_repo_path)
data_files2 = DataFilesDict.from_patterns(patterns, hub_dataset_repo_path)
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
data_files2 = DataFilesDict(sorted(data_files1.items(), reverse=True))
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
patterns2 = {"train": ["data/**train.txt"], "test": ["data/**test.txt"]}
data_files2 = DataFilesDict.from_patterns(patterns2, hub_dataset_repo_path)
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
patterns2 = {"train": ["data/**train.txt"], "test": ["data/**train.txt"]}
data_files2 = DataFilesDict.from_patterns(patterns2, hub_dataset_repo_path)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
# the tmpfs used to mock the hub repo is based on a local directory
# therefore os.stat is used to get the mtime of the data files
with patch("os.stat", return_value=os.stat(__file__)):
data_files2 = DataFilesDict.from_patterns(patterns, hub_dataset_repo_path)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
def test_DataFilesDict_from_patterns_locally_or_remote_hashing(text_file):
patterns = {"train": [_TEST_URL], "test": [str(text_file)]}
data_files1 = DataFilesDict.from_patterns(patterns)
data_files2 = DataFilesDict.from_patterns(patterns)
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
data_files2 = DataFilesDict(sorted(data_files1.items(), reverse=True))
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
patterns2 = {"train": [_TEST_URL], "test": [_TEST_URL]}
data_files2 = DataFilesDict.from_patterns(patterns2)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
with patch("fsspec.implementations.http._file_info", return_value={}):
data_files2 = DataFilesDict.from_patterns(patterns)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
with patch("os.stat", return_value=os.stat(__file__)):
data_files2 = DataFilesDict.from_patterns(patterns)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
def test_DataFilesPatternsList(text_file):
data_files_patterns = DataFilesPatternsList([str(text_file)], allowed_extensions=[None])
data_files = data_files_patterns.resolve(base_path="")
assert data_files == [text_file.as_posix()]
assert isinstance(data_files, DataFilesList)
data_files_patterns = DataFilesPatternsList([str(text_file)], allowed_extensions=[[".txt"]])
data_files = data_files_patterns.resolve(base_path="")
assert data_files == [text_file.as_posix()]
assert isinstance(data_files, DataFilesList)
data_files_patterns = DataFilesPatternsList([str(text_file).replace(".txt", ".tx*")], allowed_extensions=[None])
data_files = data_files_patterns.resolve(base_path="")
assert data_files == [text_file.as_posix()]
assert isinstance(data_files, DataFilesList)
data_files_patterns = DataFilesPatternsList([Path(text_file).name], allowed_extensions=[None])
data_files = data_files_patterns.resolve(base_path=str(Path(text_file).parent))
assert data_files == [text_file.as_posix()]
data_files_patterns = DataFilesPatternsList([str(text_file)], allowed_extensions=[[".zip"]])
with pytest.raises(FileNotFoundError):
data_files_patterns.resolve(base_path="")
def test_DataFilesPatternsDict(text_file):
data_files_patterns_dict = DataFilesPatternsDict(
{"train": DataFilesPatternsList([str(text_file)], allowed_extensions=[None])}
)
data_files_dict = data_files_patterns_dict.resolve(base_path="")
assert data_files_dict == {"train": [text_file.as_posix()]}
assert isinstance(data_files_dict, DataFilesDict)
assert isinstance(data_files_dict["train"], DataFilesList)
def mock_fs(file_paths: List[str]):
"""
Set up a mock filesystem for fsspec containing the provided files
Example:
```py
>>> DummyTestFS = mock_fs(["data/train.txt", "data.test.txt"])
>>> fs = DummyTestFS()
>>> assert fsspec.get_filesystem_class("mock").__name__ == "DummyTestFS"
>>> assert type(fs).__name__ == "DummyTestFS"
>>> print(fs.glob("**"))
["data", "data/train.txt", "data.test.txt"]
```
"""
file_paths = [file_path.split("://")[-1] for file_path in file_paths]
dir_paths = {
"/".join(file_path.split("/")[: i + 1]) for file_path in file_paths for i in range(file_path.count("/"))
}
fs_contents = [{"name": dir_path, "type": "directory"} for dir_path in dir_paths] + [
{"name": file_path, "type": "file", "size": 10} for file_path in file_paths
]
class DummyTestFS(AbstractFileSystem):
protocol = ("mock", "dummy")
_fs_contents = fs_contents
def ls(self, path, detail=True, refresh=True, **kwargs):
if kwargs.pop("strip_proto", True):
path = self._strip_protocol(path)
files = not refresh and self._ls_from_cache(path)
if not files:
files = [file for file in self._fs_contents if path == self._parent(file["name"])]
files.sort(key=lambda file: file["name"])
self.dircache[path.rstrip("/")] = files
if detail:
return files
return [file["name"] for file in files]
return DummyTestFS
@pytest.mark.parametrize("base_path", ["", "mock://", "my_dir"])
@pytest.mark.parametrize(
"data_file_per_split",
[
# === Main cases ===
# file named after split at the root
{"train": "train.txt", "validation": "valid.txt", "test": "test.txt"},
# file named after split in a directory
{
"train": "data/train.txt",
"validation": "data/valid.txt",
"test": "data/test.txt",
},
# directory named after split
{
"train": "train/split.txt",
"validation": "valid/split.txt",
"test": "test/split.txt",
},
# sharded splits
{
"train": [f"data/train_{i}.txt" for i in range(3)],
"validation": [f"data/validation_{i}.txt" for i in range(3)],
"test": [f"data/test_{i}.txt" for i in range(3)],
},
# sharded splits with standard format (+ custom split name)
{
"train": [f"data/train-0000{i}-of-00003.txt" for i in range(3)],
"validation": [f"data/validation-0000{i}-of-00003.txt" for i in range(3)],
"test": [f"data/test-0000{i}-of-00003.txt" for i in range(3)],
"random": [f"data/random-0000{i}-of-00003.txt" for i in range(3)],
},
# === Secondary cases ===
# Default to train split
{"train": "dataset.txt"},
{"train": "data/dataset.txt"},
{"train": ["data/image.jpg", "metadata.jsonl"]},
{"train": ["data/image.jpg", "metadata.csv"]},
# With prefix or suffix in directory or file names
{"train": "my_train_dir/dataset.txt"},
{"train": "data/my_train_file.txt"},
{"test": "my_test_dir/dataset.txt"},
{"test": "data/my_test_file.txt"},
{"validation": "my_validation_dir/dataset.txt"},
{"validation": "data/my_validation_file.txt"},
# With test<>eval aliases
{"test": "eval.txt"},
{"test": "data/eval.txt"},
{"test": "eval/dataset.txt"},
# With valid<>dev aliases
{"validation": "dev.txt"},
{"validation": "data/dev.txt"},
{"validation": "dev/dataset.txt"},
# With valid<>val aliases
{"validation": "val.txt"},
{"validation": "data/val.txt"},
# With other extensions
{"train": "train.parquet", "validation": "valid.parquet", "test": "test.parquet"},
# With "dev" or "eval" without separators
{"train": "developers_list.txt"},
{"train": "data/seqeval_results.txt"},
{"train": "contest.txt"},
# With supported separators
{"test": "my.test.file.txt"},
{"test": "my-test-file.txt"},
{"test": "my_test_file.txt"},
{"test": "my test file.txt"},
{"test": "test00001.txt"},
],
)
def test_get_data_files_patterns(base_path, data_file_per_split):
data_file_per_split = {k: v if isinstance(v, list) else [v] for k, v in data_file_per_split.items()}
data_file_per_split = {
split: [
base_path + ("/" if base_path and base_path[-1] != "/" else "") + file_path
for file_path in data_file_per_split[split]
]
for split in data_file_per_split
}
file_paths = sum(data_file_per_split.values(), [])
DummyTestFS = mock_fs(file_paths)
fs = DummyTestFS()
def resolver(pattern):
pattern = base_path + ("/" if base_path and base_path[-1] != "/" else "") + pattern
return [
file_path[len(fs._strip_protocol(base_path)) :].lstrip("/")
for file_path in fs.glob(pattern)
if fs.isfile(file_path)
]
patterns_per_split = _get_data_files_patterns(resolver)
assert list(patterns_per_split.keys()) == list(data_file_per_split.keys()) # Test split order with list()
for split, patterns in patterns_per_split.items():
matched = [file_path for pattern in patterns for file_path in resolver(pattern)]
expected = [
fs._strip_protocol(file_path)[len(fs._strip_protocol(base_path)) :].lstrip("/")
for file_path in data_file_per_split[split]
]
assert matched == expected
@pytest.mark.parametrize(
"metadata_files",
[
# metadata files at the root
["metadata.jsonl"],
["metadata.csv"],
# nested metadata files
["metadata.jsonl", "data/metadata.jsonl"],
["metadata.csv", "data/metadata.csv"],
],
)
def test_get_metadata_files_patterns(metadata_files):
DummyTestFS = mock_fs(metadata_files)
fs = DummyTestFS()
def resolver(pattern):
return [file_path for file_path in fs.glob(pattern) if fs.isfile(file_path)]
patterns = _get_metadata_files_patterns(resolver)
matched = [file_path for pattern in patterns for file_path in resolver(pattern)]
assert sorted(matched) == sorted(metadata_files)
def test_get_data_patterns_from_directory_with_the_word_data_twice(tmp_path):
repo_dir = tmp_path / "directory-name-ending-with-the-word-data" # parent directory contains the word "data/"
data_dir = repo_dir / "data"
data_dir.mkdir(parents=True)
data_file = data_dir / "train-00001-of-00009.parquet"
data_file.touch()
data_file_patterns = get_data_patterns(repo_dir.as_posix())
assert data_file_patterns == {"train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"]}
| datasets/tests/test_data_files.py/0 | {
"file_path": "datasets/tests/test_data_files.py",
"repo_id": "datasets",
"token_count": 12239
} | 74 |
import os
from pathlib import Path
import pytest
from datasets.inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_default_config_name,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
from datasets.packaged_modules.csv import csv
pytestmark = pytest.mark.integration
@pytest.mark.parametrize("path", ["lhoestq/test", csv.__file__])
def test_inspect_dataset(path, tmp_path):
inspect_dataset(path, tmp_path)
script_name = Path(path).stem + ".py"
assert script_name in os.listdir(tmp_path)
@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning")
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning")
@pytest.mark.parametrize("path", ["accuracy"])
def test_inspect_metric(path, tmp_path):
inspect_metric(path, tmp_path)
script_name = path + ".py"
assert script_name in os.listdir(tmp_path)
assert "__pycache__" not in os.listdir(tmp_path)
@pytest.mark.parametrize(
"path, config_name, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "default", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_config_info(path, config_name, expected_splits):
info = get_dataset_config_info(path, config_name=config_name)
assert info.config_name == config_name
assert list(info.splits.keys()) == expected_splits
def test_get_dataset_config_info_private(hf_token, hf_private_dataset_repo_txt_data):
info = get_dataset_config_info(hf_private_dataset_repo_txt_data, config_name="default", token=hf_token)
assert list(info.splits.keys()) == ["train"]
@pytest.mark.parametrize(
"path, config_name, expected_exception",
[
("paws", None, ValueError),
],
)
def test_get_dataset_config_info_error(path, config_name, expected_exception):
with pytest.raises(expected_exception):
get_dataset_config_info(path, config_name=config_name)
@pytest.mark.parametrize(
"path, expected",
[
("acronym_identification", ["default"]),
("squad", ["plain_text"]),
("hf-internal-testing/dataset_with_script", ["default"]),
("dalle-mini/wit", ["default"]),
("hf-internal-testing/librispeech_asr_dummy", ["clean", "other"]),
("hf-internal-testing/audiofolder_no_configs_in_metadata", ["default"]),
("hf-internal-testing/audiofolder_single_config_in_metadata", ["custom"]),
("hf-internal-testing/audiofolder_two_configs_in_metadata", ["v1", "v2"]),
],
)
def test_get_dataset_config_names(path, expected):
config_names = get_dataset_config_names(path)
assert config_names == expected
@pytest.mark.parametrize(
"path, expected",
[
("acronym_identification", "default"),
("squad", "plain_text"),
("hf-internal-testing/dataset_with_script", "default"),
("dalle-mini/wit", "default"),
("hf-internal-testing/librispeech_asr_dummy", None),
("hf-internal-testing/audiofolder_no_configs_in_metadata", "default"),
("hf-internal-testing/audiofolder_single_config_in_metadata", "custom"),
("hf-internal-testing/audiofolder_two_configs_in_metadata", None),
],
)
def test_get_dataset_default_config_name(path, expected):
default_config_name = get_dataset_default_config_name(path)
if expected:
assert default_config_name == expected
else:
assert default_config_name is None
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config",
[
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["default"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
],
)
def test_get_dataset_info(path, expected_configs, expected_splits_in_first_config):
infos = get_dataset_infos(path)
assert list(infos.keys()) == expected_configs
expected_config = expected_configs[0]
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys()) == expected_splits_in_first_config
@pytest.mark.parametrize(
"path, expected_config, expected_splits",
[
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "default", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
],
)
def test_get_dataset_split_names(path, expected_config, expected_splits):
infos = get_dataset_infos(path)
assert expected_config in infos
info = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys()) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception",
[
("paws", None, ValueError),
],
)
def test_get_dataset_split_names_error(path, config_name, expected_exception):
with pytest.raises(expected_exception):
get_dataset_split_names(path, config_name=config_name)
| datasets/tests/test_inspect.py/0 | {
"file_path": "datasets/tests/test_inspect.py",
"repo_id": "datasets",
"token_count": 2081
} | 75 |
from copy import deepcopy
from unittest.case import TestCase
import pytest
from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
AudioClassification,
AutomaticSpeechRecognition,
ImageClassification,
LanguageModeling,
QuestionAnsweringExtractive,
Summarization,
TextClassification,
task_template_from_dict,
)
from datasets.utils.py_utils import asdict
SAMPLE_QUESTION_ANSWERING_EXTRACTIVE = {
"id": "5733be284776f41900661182",
"title": "University_of_Notre_Dame",
"context": 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
"question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
"answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]},
}
@pytest.mark.parametrize(
"task_cls",
[
AudioClassification,
AutomaticSpeechRecognition,
ImageClassification,
LanguageModeling,
QuestionAnsweringExtractive,
Summarization,
TextClassification,
],
)
def test_reload_task_from_dict(task_cls):
task = task_cls()
task_dict = asdict(task)
reloaded = task_template_from_dict(task_dict)
assert task == reloaded
class TestLanguageModeling:
def test_column_mapping(self):
task = LanguageModeling(text_column="input_text")
assert {"input_text": "text"} == task.column_mapping
def test_from_dict(self):
input_schema = Features({"text": Value("string")})
template_dict = {"text_column": "input_text"}
task = LanguageModeling.from_dict(template_dict)
assert "language-modeling" == task.task
assert input_schema == task.input_schema
class TextClassificationTest(TestCase):
def setUp(self):
self.labels = sorted(["pos", "neg"])
def test_column_mapping(self):
task = TextClassification(text_column="input_text", label_column="input_label")
self.assertDictEqual({"input_text": "text", "input_label": "labels"}, task.column_mapping)
def test_from_dict(self):
input_schema = Features({"text": Value("string")})
# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
label_schema = Features({"labels": ClassLabel})
template_dict = {"text_column": "input_text", "label_column": "input_labels"}
task = TextClassification.from_dict(template_dict)
self.assertEqual("text-classification", task.task)
self.assertEqual(input_schema, task.input_schema)
self.assertEqual(label_schema, task.label_schema)
def test_align_with_features(self):
task = TextClassification(text_column="input_text", label_column="input_label")
self.assertEqual(task.label_schema["labels"], ClassLabel)
task = task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)}))
self.assertEqual(task.label_schema["labels"], ClassLabel(names=self.labels))
class QuestionAnsweringTest(TestCase):
def test_column_mapping(self):
task = QuestionAnsweringExtractive(
context_column="input_context", question_column="input_question", answers_column="input_answers"
)
self.assertDictEqual(
{"input_context": "context", "input_question": "question", "input_answers": "answers"}, task.column_mapping
)
def test_from_dict(self):
input_schema = Features({"question": Value("string"), "context": Value("string")})
label_schema = Features(
{
"answers": Sequence(
{
"text": Value("string"),
"answer_start": Value("int32"),
}
)
}
)
template_dict = {
"context_column": "input_input_context",
"question_column": "input_question",
"answers_column": "input_answers",
}
task = QuestionAnsweringExtractive.from_dict(template_dict)
self.assertEqual("question-answering-extractive", task.task)
self.assertEqual(input_schema, task.input_schema)
self.assertEqual(label_schema, task.label_schema)
class SummarizationTest(TestCase):
def test_column_mapping(self):
task = Summarization(text_column="input_text", summary_column="input_summary")
self.assertDictEqual({"input_text": "text", "input_summary": "summary"}, task.column_mapping)
def test_from_dict(self):
input_schema = Features({"text": Value("string")})
label_schema = Features({"summary": Value("string")})
template_dict = {"text_column": "input_text", "summary_column": "input_summary"}
task = Summarization.from_dict(template_dict)
self.assertEqual("summarization", task.task)
self.assertEqual(input_schema, task.input_schema)
self.assertEqual(label_schema, task.label_schema)
class AutomaticSpeechRecognitionTest(TestCase):
def test_column_mapping(self):
task = AutomaticSpeechRecognition(audio_column="input_audio", transcription_column="input_transcription")
self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}, task.column_mapping)
def test_from_dict(self):
input_schema = Features({"audio": Audio()})
label_schema = Features({"transcription": Value("string")})
template_dict = {
"audio_column": "input_audio",
"transcription_column": "input_transcription",
}
task = AutomaticSpeechRecognition.from_dict(template_dict)
self.assertEqual("automatic-speech-recognition", task.task)
self.assertEqual(input_schema, task.input_schema)
self.assertEqual(label_schema, task.label_schema)
class AudioClassificationTest(TestCase):
def setUp(self):
self.labels = sorted(["pos", "neg"])
def test_column_mapping(self):
task = AudioClassification(audio_column="input_audio", label_column="input_label")
self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}, task.column_mapping)
def test_from_dict(self):
input_schema = Features({"audio": Audio()})
label_schema = Features({"labels": ClassLabel})
template_dict = {
"audio_column": "input_image",
"label_column": "input_label",
}
task = AudioClassification.from_dict(template_dict)
self.assertEqual("audio-classification", task.task)
self.assertEqual(input_schema, task.input_schema)
self.assertEqual(label_schema, task.label_schema)
def test_align_with_features(self):
task = AudioClassification(audio_column="input_audio", label_column="input_label")
self.assertEqual(task.label_schema["labels"], ClassLabel)
task = task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)}))
self.assertEqual(task.label_schema["labels"], ClassLabel(names=self.labels))
class ImageClassificationTest(TestCase):
def setUp(self):
self.labels = sorted(["pos", "neg"])
def test_column_mapping(self):
task = ImageClassification(image_column="input_image", label_column="input_label")
self.assertDictEqual({"input_image": "image", "input_label": "labels"}, task.column_mapping)
def test_from_dict(self):
input_schema = Features({"image": Image()})
label_schema = Features({"labels": ClassLabel})
template_dict = {
"image_column": "input_image",
"label_column": "input_label",
}
task = ImageClassification.from_dict(template_dict)
self.assertEqual("image-classification", task.task)
self.assertEqual(input_schema, task.input_schema)
self.assertEqual(label_schema, task.label_schema)
def test_align_with_features(self):
task = ImageClassification(image_column="input_image", label_column="input_label")
self.assertEqual(task.label_schema["labels"], ClassLabel)
task = task.align_with_features(Features({"input_label": ClassLabel(names=self.labels)}))
self.assertEqual(task.label_schema["labels"], ClassLabel(names=self.labels))
class DatasetWithTaskProcessingTest(TestCase):
def test_map_on_task_template(self):
info = DatasetInfo(task_templates=QuestionAnsweringExtractive())
dataset = Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}, info=info)
assert isinstance(dataset.info.task_templates, list)
assert len(dataset.info.task_templates) == 1
def keep_task(x):
return x
def dont_keep_task(x):
out = deepcopy(SAMPLE_QUESTION_ANSWERING_EXTRACTIVE)
out["answers"]["foobar"] = 0
return out
mapped_dataset = dataset.map(keep_task)
assert mapped_dataset.info.task_templates == dataset.info.task_templates
# reload from cache
mapped_dataset = dataset.map(keep_task)
assert mapped_dataset.info.task_templates == dataset.info.task_templates
mapped_dataset = dataset.map(dont_keep_task)
assert mapped_dataset.info.task_templates == []
# reload from cache
mapped_dataset = dataset.map(dont_keep_task)
assert mapped_dataset.info.task_templates == []
def test_remove_and_map_on_task_template(self):
features = Features({"text": Value("string"), "label": ClassLabel(names=("pos", "neg"))})
task_templates = TextClassification(text_column="text", label_column="label")
info = DatasetInfo(features=features, task_templates=task_templates)
dataset = Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}, info=info)
def process(example):
return example
modified_dataset = dataset.remove_columns("label")
mapped_dataset = modified_dataset.map(process)
assert mapped_dataset.info.task_templates == []
| datasets/tests/test_tasks.py/0 | {
"file_path": "datasets/tests/test_tasks.py",
"repo_id": "datasets",
"token_count": 4249
} | 76 |
<jupyter_start><jupyter_text>Unit 3: Deep Q-Learning with Atari Games 👾 using RL Baselines3 ZooIn this notebook, **you'll train a Deep Q-Learning agent** playing Space Invaders using [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo), a training framework based on [Stable-Baselines3](https://stable-baselines3.readthedocs.io/en/master/) that provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.We're using the [RL-Baselines-3 Zoo integration, a vanilla version of Deep Q-Learning](https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) with no extensions such as Double-DQN, Dueling-DQN, and Prioritized Experience Replay.⬇️ Here is an example of what **you will achieve** ⬇️<jupyter_code>%%html
<video controls autoplay><source src="https://huggingface.co/ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4/resolve/main/replay.mp4" type="video/mp4"></video><jupyter_output><empty_output><jupyter_text>🎮 Environments: - [SpacesInvadersNoFrameskip-v4](https://gymnasium.farama.org/environments/atari/space_invaders/)You can see the difference between Space Invaders versions here 👉 https://gymnasium.farama.org/environments/atari/space_invaders/variants 📚 RL-Library: - [RL-Baselines3-Zoo](https://github.com/DLR-RM/rl-baselines3-zoo) Objectives of this notebook 🏆At the end of the notebook, you will:- Be able to understand deeper **how RL Baselines3 Zoo works**.- Be able to **push your trained agent and the code to the Hub** with a nice video replay and an evaluation score 🔥. This notebook is from Deep Reinforcement Learning Course In this free course, you will:- 📖 Study Deep Reinforcement Learning in **theory and practice**.- 🧑💻 Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0.- 🤖 Train **agents in unique environments** And more check 📚 the syllabus 👉 https://simoninithomas.github.io/deep-rl-courseDon’t forget to **sign up to the course** (we are collecting your email to be able to **send you the links when each Unit is published and give you information about the challenges and updates).**The best way to keep in touch is to join our discord server to exchange with the community and with us 👉🏻 https://discord.gg/ydHrjt3WP5 Prerequisites 🏗️Before diving into the notebook, you need to:🔲 📚 **[Study Deep Q-Learning by reading Unit 3](https://huggingface.co/deep-rl-course/unit3/introduction)** 🤗 We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues). Let's train a Deep Q-Learning agent playing Atari' Space Invaders 👾 and upload it to the Hub.We strongly recommend students **to use Google Colab for the hands-on exercises instead of running them on their personal computers**.By using Google Colab, **you can focus on learning and experimenting without worrying about the technical aspects of setting up your environments**.To validate this hands-on for the certification process, you need to push your trained model to the Hub and **get a result of >= 200**.To find your result, go to the leaderboard and find your model, **the result = mean_reward - std of reward**For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introductioncertification-process An advice 💡It's better to run this colab in a copy on your Google Drive, so that **if it timeouts** you still have the saved notebook on your Google Drive and do not need to fill everything from scratch.To do that you can either do `Ctrl + S` or `File > Save a copy in Google Drive.`Also, we're going to **train it for 90 minutes with 1M timesteps**. By typing `!nvidia-smi` will tell you what GPU you're using.And if you want to train more such 10 million steps, this will take about 9 hours, potentially resulting in Colab timing out. In that case, I recommend running this on your local computer (or somewhere else). Just click on: `File>Download`. Set the GPU 💪- To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type` - `Hardware Accelerator > GPU` Install RL-Baselines3 Zoo and its dependencies 📚If you see `ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.` **this is normal and it's not a critical error** there's a conflict of version. But the packages we need are installed.<jupyter_code># For now we install this update of RL-Baselines3 Zoo
!pip install git+https://github.com/DLR-RM/rl-baselines3-zoo@update/hf<jupyter_output><empty_output><jupyter_text>IF AND ONLY IF THE VERSION ABOVE DOES NOT EXIST ANYMORE. UNCOMMENT AND INSTALL THE ONE BELOW<jupyter_code>#!pip install rl_zoo3==2.0.0a9
!apt-get install swig cmake ffmpeg<jupyter_output><empty_output><jupyter_text>To be able to use Atari games in Gymnasium we need to install atari package. And accept-rom-license to download the rom files (games files).<jupyter_code>!pip install gymnasium[atari]
!pip install gymnasium[accept-rom-license]<jupyter_output><empty_output><jupyter_text>Create a virtual display 🔽During the notebook, we'll need to generate a replay video. To do so, with colab, **we need to have a virtual screen to be able to render the environment** (and thus record the frames). Hence the following cell will install the librairies and create and run a virtual screen 🖥<jupyter_code>%%capture
!apt install python-opengl
!apt install xvfb
!pip3 install pyvirtualdisplay
# Virtual display
from pyvirtualdisplay import Display
virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()<jupyter_output><empty_output><jupyter_text>Train our Deep Q-Learning Agent to Play Space Invaders 👾To train an agent with RL-Baselines3-Zoo, we just need to do two things:1. Create a hyperparameter config file that will contain our training hyperparameters called `dqn.yml`.This is a template example:```SpaceInvadersNoFrameskip-v4: env_wrapper: - stable_baselines3.common.atari_wrappers.AtariWrapper frame_stack: 4 policy: 'CnnPolicy' n_timesteps: !!float 1e6 buffer_size: 100000 learning_rate: !!float 1e-4 batch_size: 32 learning_starts: 100000 target_update_interval: 1000 train_freq: 4 gradient_steps: 1 exploration_fraction: 0.1 exploration_final_eps: 0.01 If True, you need to deactivate handle_timeout_termination in the replay_buffer_kwargs optimize_memory_usage: False``` Here we see that:- We use the `Atari Wrapper` that preprocess the input (Frame reduction ,grayscale, stack 4 frames)- We use `CnnPolicy`, since we use Convolutional layers to process the frames- We train it for 10 million `n_timesteps` - Memory (Experience Replay) size is 100000, aka the amount of experience steps you saved to train again your agent with.💡 My advice is to **reduce the training timesteps to 1M,** which will take about 90 minutes on a P100. `!nvidia-smi` will tell you what GPU you're using. At 10 million steps, this will take about 9 hours, which could likely result in Colab timing out. I recommend running this on your local computer (or somewhere else). Just click on: `File>Download`. In terms of hyperparameters optimization, my advice is to focus on these 3 hyperparameters:- `learning_rate`- `buffer_size (Experience Memory size)`- `batch_size`As a good practice, you need to **check the documentation to understand what each hyperparameters does**: https://stable-baselines3.readthedocs.io/en/master/modules/dqn.htmlparameters 2. We start the training and save the models on `logs` folder 📁- Define the algorithm after `--algo`, where we save the model after `-f` and where the hyperparameter config is after `-c`.<jupyter_code>!python -m rl_zoo3.train --algo ________ --env SpaceInvadersNoFrameskip-v4 -f _________ -c _________<jupyter_output><empty_output><jupyter_text>Solution<jupyter_code>!python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -c dqn.yml<jupyter_output><empty_output><jupyter_text>Let's evaluate our agent 👀- RL-Baselines3-Zoo provides `enjoy.py`, a python script to evaluate our agent. In most RL libraries, we call the evaluation script `enjoy.py`.- Let's evaluate it for 5000 timesteps 🔥<jupyter_code>!python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 --no-render --n-timesteps _________ --folder logs/<jupyter_output><empty_output><jupyter_text>Solution<jupyter_code>!python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 --no-render --n-timesteps 5000 --folder logs/<jupyter_output><empty_output><jupyter_text>Publish our trained model on the Hub 🚀Now that we saw we got good results after the training, we can publish our trained model on the hub 🤗 with one line of code. By using `rl_zoo3.push_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the hub**.This way:- You can **showcase our work** 🔥- You can **visualize your agent playing** 👀- You can **share with the community an agent that others can use** 💾- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard To be able to share your model with the community there are three more steps to follow:1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.- Create a new token (https://huggingface.co/settings/tokens) **with write role** - Copy the token - Run the cell below and past the token<jupyter_code>from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
notebook_login()
!git config --global credential.helper store<jupyter_output><empty_output><jupyter_text>If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login` 3️⃣ We're now ready to push our trained agent to the 🤗 Hub 🔥 Let's run push_to_hub.py file to upload our trained agent to the Hub.`--repo-name `: The name of the repo`-orga`: Your Hugging Face username`-f`: Where the trained model folder is (in our case `logs`)<jupyter_code>!python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 --repo-name _____________________ -orga _____________________ -f logs/<jupyter_output><empty_output><jupyter_text>Solution<jupyter_code>!python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 --repo-name dqn-SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/<jupyter_output><empty_output><jupyter_text>. Congrats 🥳 you've just trained and uploaded your first Deep Q-Learning agent using RL-Baselines-3 Zoo. The script above should have displayed a link to a model repository such as https://huggingface.co/ThomasSimonini/dqn-SpaceInvadersNoFrameskip-v4. When you go to this link, you can:- See a **video preview of your agent** at the right. - Click "Files and versions" to see all the files in the repository.- Click "Use in stable-baselines3" to get a code snippet that shows how to load the model.- A model card (`README.md` file) which gives a description of the model and the hyperparameters you used.Under the hood, the Hub uses git-based repositories (don't worry if you don't know what git is), which means you can update the model with new versions as you experiment and improve your agent.**Compare the results of your agents with your classmates** using the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) 🏆 Load a powerful trained model 🔥- The Stable-Baselines3 team uploaded **more than 150 trained Deep Reinforcement Learning agents on the Hub**.You can find them here: 👉 https://huggingface.co/sb3Some examples:- Asteroids: https://huggingface.co/sb3/dqn-AsteroidsNoFrameskip-v4- Beam Rider: https://huggingface.co/sb3/dqn-BeamRiderNoFrameskip-v4- Breakout: https://huggingface.co/sb3/dqn-BreakoutNoFrameskip-v4- Road Runner: https://huggingface.co/sb3/dqn-RoadRunnerNoFrameskip-v4Let's load an agent playing Beam Rider: https://huggingface.co/sb3/dqn-BeamRiderNoFrameskip-v4<jupyter_code>%%html
<video controls autoplay><source src="https://huggingface.co/sb3/dqn-BeamRiderNoFrameskip-v4/resolve/main/replay.mp4" type="video/mp4"></video><jupyter_output><empty_output><jupyter_text>1. We download the model using `rl_zoo3.load_from_hub`, and place it in a new folder that we can call `rl_trained`<jupyter_code># Download model and save it into the logs/ folder
!python -m rl_zoo3.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga sb3 -f rl_trained/<jupyter_output><empty_output><jupyter_text>2. Let's evaluate if for 5000 timesteps<jupyter_code>!python -m rl_zoo3.enjoy --algo dqn --env BeamRiderNoFrameskip-v4 -n 5000 -f rl_trained/ --no-render<jupyter_output><empty_output> | deep-rl-class/notebooks/unit3/unit3.ipynb/0 | {
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# Conclusion [[conclusion]]
Congrats on finishing this unit! **That was the biggest one**, and there was a lot of information. And congrats on finishing the tutorial. You’ve just trained your first Deep RL agents and shared them with the community! 🥳
It's **normal if you still feel confused by some of these elements**. This was the same for me and for all people who studied RL.
**Take time to really grasp the material** before continuing. It’s important to master these elements and have a solid foundation before entering the fun part.
Naturally, during the course, we’re going to use and explain these terms again, but it’s better to understand them before diving into the next units.
In the next (bonus) unit, we’re going to reinforce what we just learned by **training Huggy the Dog to fetch a stick**.
You will then be able to play with him 🤗.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit-bonus1/huggy.jpg" alt="Huggy"/>
Finally, we would love **to hear what you think of the course and how we can improve it**. If you have some feedback then, please 👉 [fill this form](https://forms.gle/BzKXWzLAGZESGNaE9)
### Keep Learning, stay awesome 🤗
| deep-rl-class/units/en/unit1/conclusion.mdx/0 | {
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# Hands-on [[hands-on]]
<CourseFloatingBanner classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit2/unit2.ipynb"}
]}
askForHelpUrl="http://hf.co/join/discord" />
Now that we studied the Q-Learning algorithm, let's implement it from scratch and train our Q-Learning agent in two environments:
1. [Frozen-Lake-v1 (non-slippery and slippery version)](https://gymnasium.farama.org/environments/toy_text/frozen_lake/) ☃️ : where our agent will need to **go from the starting state (S) to the goal state (G)** by walking only on frozen tiles (F) and avoiding holes (H).
2. [An autonomous taxi](https://gymnasium.farama.org/environments/toy_text/taxi/) 🚖 will need **to learn to navigate** a city to **transport its passengers from point A to point B.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/envs.gif" alt="Environments"/>
Thanks to a [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard), you'll be able to compare your results with other classmates and exchange the best practices to improve your agent's scores. Who will win the challenge for Unit 2?
To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process), you need to push your trained Taxi model to the Hub and **get a result of >= 4.5**.
To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward**
For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
And you can check your progress here 👉 https://huggingface.co/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course
**To start the hands-on click on the Open In Colab button** 👇 :
[](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit2/unit2.ipynb)
We strongly **recommend students use Google Colab for the hands-on exercises** instead of running them on their personal computers.
By using Google Colab, **you can focus on learning and experimenting without worrying about the technical aspects** of setting up your environments.
# Unit 2: Q-Learning with FrozenLake-v1 ⛄ and Taxi-v3 🚕
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/thumbnail.jpg" alt="Unit 2 Thumbnail">
In this notebook, **you'll code your first Reinforcement Learning agent from scratch** to play FrozenLake ❄️ using Q-Learning, share it with the community, and experiment with different configurations.
⬇️ Here is an example of what **you will achieve in just a couple of minutes.** ⬇️
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/envs.gif" alt="Environments"/>
### 🎮 Environments:
- [FrozenLake-v1](https://gymnasium.farama.org/environments/toy_text/frozen_lake/)
- [Taxi-v3](https://gymnasium.farama.org/environments/toy_text/taxi/)
### 📚 RL-Library:
- Python and NumPy
- [Gymnasium](https://gymnasium.farama.org/)
We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the GitHub Repo](https://github.com/huggingface/deep-rl-class/issues).
## Objectives of this notebook 🏆
At the end of the notebook, you will:
- Be able to use **Gymnasium**, the environment library.
- Be able to code a Q-Learning agent from scratch.
- Be able to **push your trained agent and the code to the Hub** with a nice video replay and an evaluation score 🔥.
## This notebook is from the Deep Reinforcement Learning Course
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/deep-rl-course-illustration.jpg" alt="Deep RL Course illustration"/>
In this free course, you will:
- 📖 Study Deep Reinforcement Learning in **theory and practice**.
- 🧑💻 Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0.
- 🤖 Train **agents in unique environments**
And more check 📚 the syllabus 👉 https://simoninithomas.github.io/deep-rl-course
Don’t forget to **<a href="http://eepurl.com/ic5ZUD">sign up to the course</a>** (we are collecting your email to be able to **send you the links when each Unit is published and give you information about the challenges and updates).**
The best way to keep in touch is to join our discord server to exchange with the community and with us 👉🏻 https://discord.gg/ydHrjt3WP5
## Prerequisites 🏗️
Before diving into the notebook, you need to:
🔲 📚 **Study [Q-Learning by reading Unit 2](https://huggingface.co/deep-rl-course/unit2/introduction)** 🤗
## A small recap of Q-Learning
*Q-Learning* **is the RL algorithm that**:
- Trains *Q-Function*, an **action-value function** that is encoded, in internal memory, by a *Q-table* **that contains all the state-action pair values.**
- Given a state and action, our Q-Function **will search the Q-table for the corresponding value.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-function-2.jpg" alt="Q function" width="100%"/>
- When the training is done, **we have an optimal Q-Function, so an optimal Q-Table.**
- And if we **have an optimal Q-function**, we
have an optimal policy, since we **know for each state, the best action to take.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/link-value-policy.jpg" alt="Link value policy" width="100%"/>
But, in the beginning, our **Q-Table is useless since it gives arbitrary value for each state-action pair (most of the time we initialize the Q-Table to 0 values)**. But, as we’ll explore the environment and update our Q-Table it will give us better and better approximations
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/q-learning.jpeg" alt="q-learning.jpeg" width="100%"/>
This is the Q-Learning pseudocode:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-learning-2.jpg" alt="Q-Learning" width="100%"/>
# Let's code our first Reinforcement Learning algorithm 🚀
To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process), you need to push your trained Taxi model to the Hub and **get a result of >= 4.5**.
To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward**
For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
## Install dependencies and create a virtual display 🔽
In the notebook, we'll need to generate a replay video. To do so, with Colab, **we need to have a virtual screen to render the environment** (and thus record the frames).
Hence the following cell will install the libraries and create and run a virtual screen 🖥
We’ll install multiple ones:
- `gymnasium`: Contains the FrozenLake-v1 ⛄ and Taxi-v3 🚕 environments.
- `pygame`: Used for the FrozenLake-v1 and Taxi-v3 UI.
- `numpy`: Used for handling our Q-table.
The Hugging Face Hub 🤗 works as a central place where anyone can share and explore models and datasets. It has versioning, metrics, visualizations and other features that will allow you to easily collaborate with others.
You can see here all the Deep RL models available (if they use Q Learning) here 👉 https://huggingface.co/models?other=q-learning
```bash
pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit2/requirements-unit2.txt
```
```bash
sudo apt-get update
sudo apt-get install -y python3-opengl
apt install ffmpeg xvfb
pip3 install pyvirtualdisplay
```
To make sure the new installed libraries are used, **sometimes it's required to restart the notebook runtime**. The next cell will force the **runtime to crash, so you'll need to connect again and run the code starting from here**. Thanks to this trick, **we will be able to run our virtual screen.**
```python
import os
os.kill(os.getpid(), 9)
```
```python
# Virtual display
from pyvirtualdisplay import Display
virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()
```
## Import the packages 📦
In addition to the installed libraries, we also use:
- `random`: To generate random numbers (that will be useful for epsilon-greedy policy).
- `imageio`: To generate a replay video.
```python
import numpy as np
import gymnasium as gym
import random
import imageio
import os
import tqdm
import pickle5 as pickle
from tqdm.notebook import tqdm
```
We're now ready to code our Q-Learning algorithm 🔥
# Part 1: Frozen Lake ⛄ (non slippery version)
## Create and understand [FrozenLake environment ⛄]((https://gymnasium.farama.org/environments/toy_text/frozen_lake/)
---
💡 A good habit when you start to use an environment is to check its documentation
👉 https://gymnasium.farama.org/environments/toy_text/frozen_lake/
---
We're going to train our Q-Learning agent **to navigate from the starting state (S) to the goal state (G) by walking only on frozen tiles (F) and avoid holes (H)**.
We can have two sizes of environment:
- `map_name="4x4"`: a 4x4 grid version
- `map_name="8x8"`: a 8x8 grid version
The environment has two modes:
- `is_slippery=False`: The agent always moves **in the intended direction** due to the non-slippery nature of the frozen lake (deterministic).
- `is_slippery=True`: The agent **may not always move in the intended direction** due to the slippery nature of the frozen lake (stochastic).
For now let's keep it simple with the 4x4 map and non-slippery.
We add a parameter called `render_mode` that specifies how the environment should be visualised. In our case because we **want to record a video of the environment at the end, we need to set render_mode to rgb_array**.
As [explained in the documentation](https://gymnasium.farama.org/api/env/#gymnasium.Env.render) “rgb_array”: Return a single frame representing the current state of the environment. A frame is a np.ndarray with shape (x, y, 3) representing RGB values for an x-by-y pixel image.
```python
# Create the FrozenLake-v1 environment using 4x4 map and non-slippery version and render_mode="rgb_array"
env = gym.make() # TODO use the correct parameters
```
### Solution
```python
env = gym.make("FrozenLake-v1", map_name="4x4", is_slippery=False, render_mode="rgb_array")
```
You can create your own custom grid like this:
```python
desc=["SFFF", "FHFH", "FFFH", "HFFG"]
gym.make('FrozenLake-v1', desc=desc, is_slippery=True)
```
but we'll use the default environment for now.
### Let's see what the Environment looks like:
```python
# We create our environment with gym.make("<name_of_the_environment>")- `is_slippery=False`: The agent always moves in the intended direction due to the non-slippery nature of the frozen lake (deterministic).
print("_____OBSERVATION SPACE_____ \n")
print("Observation Space", env.observation_space)
print("Sample observation", env.observation_space.sample()) # Get a random observation
```
We see with `Observation Space Shape Discrete(16)` that the observation is an integer representing the **agent’s current position as current_row * ncols + current_col (where both the row and col start at 0)**.
For example, the goal position in the 4x4 map can be calculated as follows: 3 * 4 + 3 = 15. The number of possible observations is dependent on the size of the map. **For example, the 4x4 map has 16 possible observations.**
For instance, this is what state = 0 looks like:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/frozenlake.png" alt="FrozenLake">
```python
print("\n _____ACTION SPACE_____ \n")
print("Action Space Shape", env.action_space.n)
print("Action Space Sample", env.action_space.sample()) # Take a random action
```
The action space (the set of possible actions the agent can take) is discrete with 4 actions available 🎮:
- 0: GO LEFT
- 1: GO DOWN
- 2: GO RIGHT
- 3: GO UP
Reward function 💰:
- Reach goal: +1
- Reach hole: 0
- Reach frozen: 0
## Create and Initialize the Q-table 🗄️
(👀 Step 1 of the pseudocode)
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-learning-2.jpg" alt="Q-Learning" width="100%"/>
It's time to initialize our Q-table! To know how many rows (states) and columns (actions) to use, we need to know the action and observation space. We already know their values from before, but we'll want to obtain them programmatically so that our algorithm generalizes for different environments. Gym provides us a way to do that: `env.action_space.n` and `env.observation_space.n`
```python
state_space =
print("There are ", state_space, " possible states")
action_space =
print("There are ", action_space, " possible actions")
```
```python
# Let's create our Qtable of size (state_space, action_space) and initialized each values at 0 using np.zeros. np.zeros needs a tuple (a,b)
def initialize_q_table(state_space, action_space):
Qtable =
return Qtable
```
```python
Qtable_frozenlake = initialize_q_table(state_space, action_space)
```
### Solution
```python
state_space = env.observation_space.n
print("There are ", state_space, " possible states")
action_space = env.action_space.n
print("There are ", action_space, " possible actions")
```
```python
# Let's create our Qtable of size (state_space, action_space) and initialized each values at 0 using np.zeros
def initialize_q_table(state_space, action_space):
Qtable = np.zeros((state_space, action_space))
return Qtable
```
```python
Qtable_frozenlake = initialize_q_table(state_space, action_space)
```
## Define the greedy policy 🤖
Remember we have two policies since Q-Learning is an **off-policy** algorithm. This means we're using a **different policy for acting and updating the value function**.
- Epsilon-greedy policy (acting policy)
- Greedy-policy (updating policy)
The greedy policy will also be the final policy we'll have when the Q-learning agent completes training. The greedy policy is used to select an action using the Q-table.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/off-on-4.jpg" alt="Q-Learning" width="100%"/>
```python
def greedy_policy(Qtable, state):
# Exploitation: take the action with the highest state, action value
action =
return action
```
#### Solution
```python
def greedy_policy(Qtable, state):
# Exploitation: take the action with the highest state, action value
action = np.argmax(Qtable[state][:])
return action
```
## Define the epsilon-greedy policy 🤖
Epsilon-greedy is the training policy that handles the exploration/exploitation trade-off.
The idea with epsilon-greedy:
- With *probability 1 - ɛ* : **we do exploitation** (i.e. our agent selects the action with the highest state-action pair value).
- With *probability ɛ*: we do **exploration** (trying a random action).
As the training continues, we progressively **reduce the epsilon value since we will need less and less exploration and more exploitation.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-learning-4.jpg" alt="Q-Learning" width="100%"/>
```python
def epsilon_greedy_policy(Qtable, state, epsilon):
# Randomly generate a number between 0 and 1
random_num =
# if random_num > greater than epsilon --> exploitation
if random_num > epsilon:
# Take the action with the highest value given a state
# np.argmax can be useful here
action =
# else --> exploration
else:
action = # Take a random action
return action
```
#### Solution
```python
def epsilon_greedy_policy(Qtable, state, epsilon):
# Randomly generate a number between 0 and 1
random_num = random.uniform(0, 1)
# if random_num > greater than epsilon --> exploitation
if random_num > epsilon:
# Take the action with the highest value given a state
# np.argmax can be useful here
action = greedy_policy(Qtable, state)
# else --> exploration
else:
action = env.action_space.sample()
return action
```
## Define the hyperparameters ⚙️
The exploration related hyperparamters are some of the most important ones.
- We need to make sure that our agent **explores enough of the state space** to learn a good value approximation. To do that, we need to have progressive decay of the epsilon.
- If you decrease epsilon too fast (too high decay_rate), **you take the risk that your agent will be stuck**, since your agent didn't explore enough of the state space and hence can't solve the problem.
```python
# Training parameters
n_training_episodes = 10000 # Total training episodes
learning_rate = 0.7 # Learning rate
# Evaluation parameters
n_eval_episodes = 100 # Total number of test episodes
# Environment parameters
env_id = "FrozenLake-v1" # Name of the environment
max_steps = 99 # Max steps per episode
gamma = 0.95 # Discounting rate
eval_seed = [] # The evaluation seed of the environment
# Exploration parameters
max_epsilon = 1.0 # Exploration probability at start
min_epsilon = 0.05 # Minimum exploration probability
decay_rate = 0.0005 # Exponential decay rate for exploration prob
```
## Create the training loop method
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-learning-2.jpg" alt="Q-Learning" width="100%"/>
The training loop goes like this:
```
For episode in the total of training episodes:
Reduce epsilon (since we need less and less exploration)
Reset the environment
For step in max timesteps:
Choose the action At using epsilon greedy policy
Take the action (a) and observe the outcome state(s') and reward (r)
Update the Q-value Q(s,a) using Bellman equation Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]
If done, finish the episode
Our next state is the new state
```
```python
def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable):
for episode in tqdm(range(n_training_episodes)):
# Reduce epsilon (because we need less and less exploration)
epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)
# Reset the environment
state, info = env.reset()
step = 0
terminated = False
truncated = False
# repeat
for step in range(max_steps):
# Choose the action At using epsilon greedy policy
action =
# Take action At and observe Rt+1 and St+1
# Take the action (a) and observe the outcome state(s') and reward (r)
new_state, reward, terminated, truncated, info =
# Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]
Qtable[state][action] =
# If terminated or truncated finish the episode
if terminated or truncated:
break
# Our next state is the new state
state = new_state
return Qtable
```
#### Solution
```python
def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable):
for episode in tqdm(range(n_training_episodes)):
# Reduce epsilon (because we need less and less exploration)
epsilon = min_epsilon + (max_epsilon - min_epsilon) * np.exp(-decay_rate * episode)
# Reset the environment
state, info = env.reset()
step = 0
terminated = False
truncated = False
# repeat
for step in range(max_steps):
# Choose the action At using epsilon greedy policy
action = epsilon_greedy_policy(Qtable, state, epsilon)
# Take action At and observe Rt+1 and St+1
# Take the action (a) and observe the outcome state(s') and reward (r)
new_state, reward, terminated, truncated, info = env.step(action)
# Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]
Qtable[state][action] = Qtable[state][action] + learning_rate * (
reward + gamma * np.max(Qtable[new_state]) - Qtable[state][action]
)
# If terminated or truncated finish the episode
if terminated or truncated:
break
# Our next state is the new state
state = new_state
return Qtable
```
## Train the Q-Learning agent 🏃
```python
Qtable_frozenlake = train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_frozenlake)
```
## Let's see what our Q-Learning table looks like now 👀
```python
Qtable_frozenlake
```
## The evaluation method 📝
- We defined the evaluation method that we're going to use to test our Q-Learning agent.
```python
def evaluate_agent(env, max_steps, n_eval_episodes, Q, seed):
"""
Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.
:param env: The evaluation environment
:param n_eval_episodes: Number of episode to evaluate the agent
:param Q: The Q-table
:param seed: The evaluation seed array (for taxi-v3)
"""
episode_rewards = []
for episode in tqdm(range(n_eval_episodes)):
if seed:
state, info = env.reset(seed=seed[episode])
else:
state, info = env.reset()
step = 0
truncated = False
terminated = False
total_rewards_ep = 0
for step in range(max_steps):
# Take the action (index) that have the maximum expected future reward given that state
action = greedy_policy(Q, state)
new_state, reward, terminated, truncated, info = env.step(action)
total_rewards_ep += reward
if terminated or truncated:
break
state = new_state
episode_rewards.append(total_rewards_ep)
mean_reward = np.mean(episode_rewards)
std_reward = np.std(episode_rewards)
return mean_reward, std_reward
```
## Evaluate our Q-Learning agent 📈
- Usually, you should have a mean reward of 1.0
- The **environment is relatively easy** since the state space is really small (16). What you can try to do is [to replace it with the slippery version](https://www.gymlibrary.dev/environments/toy_text/frozen_lake/), which introduces stochasticity, making the environment more complex.
```python
# Evaluate our Agent
mean_reward, std_reward = evaluate_agent(env, max_steps, n_eval_episodes, Qtable_frozenlake, eval_seed)
print(f"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}")
```
## Publish our trained model to the Hub 🔥
Now that we saw good results after the training, **we can publish our trained model to the Hub 🤗 with one line of code**.
Here's an example of a Model Card:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/modelcard.png" alt="Model card" width="100%"/>
Under the hood, the Hub uses git-based repositories (don't worry if you don't know what git is), which means you can update the model with new versions as you experiment and improve your agent.
#### Do not modify this code
```python
from huggingface_hub import HfApi, snapshot_download
from huggingface_hub.repocard import metadata_eval_result, metadata_save
from pathlib import Path
import datetime
import json
```
```python
def record_video(env, Qtable, out_directory, fps=1):
"""
Generate a replay video of the agent
:param env
:param Qtable: Qtable of our agent
:param out_directory
:param fps: how many frame per seconds (with taxi-v3 and frozenlake-v1 we use 1)
"""
images = []
terminated = False
truncated = False
state, info = env.reset(seed=random.randint(0, 500))
img = env.render()
images.append(img)
while not terminated or truncated:
# Take the action (index) that have the maximum expected future reward given that state
action = np.argmax(Qtable[state][:])
state, reward, terminated, truncated, info = env.step(
action
) # We directly put next_state = state for recording logic
img = env.render()
images.append(img)
imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)
```
```python
def push_to_hub(repo_id, model, env, video_fps=1, local_repo_path="hub"):
"""
Evaluate, Generate a video and Upload a model to Hugging Face Hub.
This method does the complete pipeline:
- It evaluates the model
- It generates the model card
- It generates a replay video of the agent
- It pushes everything to the Hub
:param repo_id: repo_id: id of the model repository from the Hugging Face Hub
:param env
:param video_fps: how many frame per seconds to record our video replay
(with taxi-v3 and frozenlake-v1 we use 1)
:param local_repo_path: where the local repository is
"""
_, repo_name = repo_id.split("/")
eval_env = env
api = HfApi()
# Step 1: Create the repo
repo_url = api.create_repo(
repo_id=repo_id,
exist_ok=True,
)
# Step 2: Download files
repo_local_path = Path(snapshot_download(repo_id=repo_id))
# Step 3: Save the model
if env.spec.kwargs.get("map_name"):
model["map_name"] = env.spec.kwargs.get("map_name")
if env.spec.kwargs.get("is_slippery", "") == False:
model["slippery"] = False
# Pickle the model
with open((repo_local_path) / "q-learning.pkl", "wb") as f:
pickle.dump(model, f)
# Step 4: Evaluate the model and build JSON with evaluation metrics
mean_reward, std_reward = evaluate_agent(
eval_env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]
)
evaluate_data = {
"env_id": model["env_id"],
"mean_reward": mean_reward,
"n_eval_episodes": model["n_eval_episodes"],
"eval_datetime": datetime.datetime.now().isoformat(),
}
# Write a JSON file called "results.json" that will contain the
# evaluation results
with open(repo_local_path / "results.json", "w") as outfile:
json.dump(evaluate_data, outfile)
# Step 5: Create the model card
env_name = model["env_id"]
if env.spec.kwargs.get("map_name"):
env_name += "-" + env.spec.kwargs.get("map_name")
if env.spec.kwargs.get("is_slippery", "") == False:
env_name += "-" + "no_slippery"
metadata = {}
metadata["tags"] = [env_name, "q-learning", "reinforcement-learning", "custom-implementation"]
# Add metrics
eval = metadata_eval_result(
model_pretty_name=repo_name,
task_pretty_name="reinforcement-learning",
task_id="reinforcement-learning",
metrics_pretty_name="mean_reward",
metrics_id="mean_reward",
metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
dataset_pretty_name=env_name,
dataset_id=env_name,
)
# Merges both dictionaries
metadata = {**metadata, **eval}
model_card = f"""
# **Q-Learning** Agent playing1 **{env_id}**
This is a trained model of a **Q-Learning** agent playing **{env_id}** .
## Usage
model = load_from_hub(repo_id="{repo_id}", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
"""
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
readme_path = repo_local_path / "README.md"
readme = ""
print(readme_path.exists())
if readme_path.exists():
with readme_path.open("r", encoding="utf8") as f:
readme = f.read()
else:
readme = model_card
with readme_path.open("w", encoding="utf-8") as f:
f.write(readme)
# Save our metrics to Readme metadata
metadata_save(readme_path, metadata)
# Step 6: Record a video
video_path = repo_local_path / "replay.mp4"
record_video(env, model["qtable"], video_path, video_fps)
# Step 7. Push everything to the Hub
api.upload_folder(
repo_id=repo_id,
folder_path=repo_local_path,
path_in_repo=".",
)
print("Your model is pushed to the Hub. You can view your model here: ", repo_url)
```
### .
By using `push_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the Hub**.
This way:
- You can **showcase our work** 🔥
- You can **visualize your agent playing** 👀
- You can **share an agent with the community that others can use** 💾
- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
To be able to share your model with the community there are three more steps to follow:
1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join
2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.
- Create a new token (https://huggingface.co/settings/tokens) **with write role**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/create-token.jpg" alt="Create HF Token">
```python
from huggingface_hub import notebook_login
notebook_login()
```
If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login` (or `login`)
3️⃣ We're now ready to push our trained agent to the 🤗 Hub 🔥 using `push_to_hub()` function
- Let's create **the model dictionary that contains the hyperparameters and the Q_table**.
```python
model = {
"env_id": env_id,
"max_steps": max_steps,
"n_training_episodes": n_training_episodes,
"n_eval_episodes": n_eval_episodes,
"eval_seed": eval_seed,
"learning_rate": learning_rate,
"gamma": gamma,
"max_epsilon": max_epsilon,
"min_epsilon": min_epsilon,
"decay_rate": decay_rate,
"qtable": Qtable_frozenlake,
}
```
Let's fill the `push_to_hub` function:
- `repo_id`: the name of the Hugging Face Hub Repository that will be created/updated `
(repo_id = {username}/{repo_name})`
💡 A good `repo_id` is `{username}/q-{env_id}`
- `model`: our model dictionary containing the hyperparameters and the Qtable.
- `env`: the environment.
- `commit_message`: message of the commit
```python
model
```
```python
username = "" # FILL THIS
repo_name = "q-FrozenLake-v1-4x4-noSlippery"
push_to_hub(repo_id=f"{username}/{repo_name}", model=model, env=env)
```
Congrats 🥳 you've just implemented from scratch, trained, and uploaded your first Reinforcement Learning agent.
FrozenLake-v1 no_slippery is very simple environment, let's try a harder one 🔥.
# Part 2: Taxi-v3 🚖
## Create and understand [Taxi-v3 🚕](https://gymnasium.farama.org/environments/toy_text/taxi/)
---
💡 A good habit when you start to use an environment is to check its documentation
👉 https://gymnasium.farama.org/environments/toy_text/taxi/
---
In `Taxi-v3` 🚕, there are four designated locations in the grid world indicated by R(ed), G(reen), Y(ellow), and B(lue).
When the episode starts, **the taxi starts off at a random square** and the passenger is at a random location. The taxi drives to the passenger’s location, **picks up the passenger**, drives to the passenger’s destination (another one of the four specified locations), and then **drops off the passenger**. Once the passenger is dropped off, the episode ends.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/taxi.png" alt="Taxi">
```python
env = gym.make("Taxi-v3", render_mode="rgb_array")
```
There are **500 discrete states since there are 25 taxi positions, 5 possible locations of the passenger** (including the case when the passenger is in the taxi), and **4 destination locations.**
```python
state_space = env.observation_space.n
print("There are ", state_space, " possible states")
```
```python
action_space = env.action_space.n
print("There are ", action_space, " possible actions")
```
The action space (the set of possible actions the agent can take) is discrete with **6 actions available 🎮**:
- 0: move south
- 1: move north
- 2: move east
- 3: move west
- 4: pickup passenger
- 5: drop off passenger
Reward function 💰:
- -1 per step unless other reward is triggered.
- +20 delivering passenger.
- -10 executing “pickup” and “drop-off” actions illegally.
```python
# Create our Q table with state_size rows and action_size columns (500x6)
Qtable_taxi = initialize_q_table(state_space, action_space)
print(Qtable_taxi)
print("Q-table shape: ", Qtable_taxi.shape)
```
## Define the hyperparameters ⚙️
⚠ DO NOT MODIFY EVAL_SEED: the eval_seed array **allows us to evaluate your agent with the same taxi starting positions for every classmate**
```python
# Training parameters
n_training_episodes = 25000 # Total training episodes
learning_rate = 0.7 # Learning rate
# Evaluation parameters
n_eval_episodes = 100 # Total number of test episodes
# DO NOT MODIFY EVAL_SEED
eval_seed = [
16,
54,
165,
177,
191,
191,
120,
80,
149,
178,
48,
38,
6,
125,
174,
73,
50,
172,
100,
148,
146,
6,
25,
40,
68,
148,
49,
167,
9,
97,
164,
176,
61,
7,
54,
55,
161,
131,
184,
51,
170,
12,
120,
113,
95,
126,
51,
98,
36,
135,
54,
82,
45,
95,
89,
59,
95,
124,
9,
113,
58,
85,
51,
134,
121,
169,
105,
21,
30,
11,
50,
65,
12,
43,
82,
145,
152,
97,
106,
55,
31,
85,
38,
112,
102,
168,
123,
97,
21,
83,
158,
26,
80,
63,
5,
81,
32,
11,
28,
148,
] # Evaluation seed, this ensures that all classmates agents are trained on the same taxi starting position
# Each seed has a specific starting state
# Environment parameters
env_id = "Taxi-v3" # Name of the environment
max_steps = 99 # Max steps per episode
gamma = 0.95 # Discounting rate
# Exploration parameters
max_epsilon = 1.0 # Exploration probability at start
min_epsilon = 0.05 # Minimum exploration probability
decay_rate = 0.005 # Exponential decay rate for exploration prob
```
## Train our Q-Learning agent 🏃
```python
Qtable_taxi = train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_taxi)
Qtable_taxi
```
## Create a model dictionary 💾 and publish our trained model to the Hub 🔥
- We create a model dictionary that will contain all the training hyperparameters for reproducibility and the Q-Table.
```python
model = {
"env_id": env_id,
"max_steps": max_steps,
"n_training_episodes": n_training_episodes,
"n_eval_episodes": n_eval_episodes,
"eval_seed": eval_seed,
"learning_rate": learning_rate,
"gamma": gamma,
"max_epsilon": max_epsilon,
"min_epsilon": min_epsilon,
"decay_rate": decay_rate,
"qtable": Qtable_taxi,
}
```
```python
username = "" # FILL THIS
repo_name = "" # FILL THIS
push_to_hub(repo_id=f"{username}/{repo_name}", model=model, env=env)
```
Now that it's on the Hub, you can compare the results of your Taxi-v3 with your classmates using the leaderboard 🏆 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/taxi-leaderboard.png" alt="Taxi Leaderboard">
# Part 3: Load from Hub 🔽
What's amazing with Hugging Face Hub 🤗 is that you can easily load powerful models from the community.
Loading a saved model from the Hub is really easy:
1. You go https://huggingface.co/models?other=q-learning to see the list of all the q-learning saved models.
2. You select one and copy its repo_id
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/copy-id.png" alt="Copy id">
3. Then we just need to use `load_from_hub` with:
- The repo_id
- The filename: the saved model inside the repo.
#### Do not modify this code
```python
from urllib.error import HTTPError
from huggingface_hub import hf_hub_download
def load_from_hub(repo_id: str, filename: str) -> str:
"""
Download a model from Hugging Face Hub.
:param repo_id: id of the model repository from the Hugging Face Hub
:param filename: name of the model zip file from the repository
"""
# Get the model from the Hub, download and cache the model on your local disk
pickle_model = hf_hub_download(repo_id=repo_id, filename=filename)
with open(pickle_model, "rb") as f:
downloaded_model_file = pickle.load(f)
return downloaded_model_file
```
### .
```python
model = load_from_hub(repo_id="ThomasSimonini/q-Taxi-v3", filename="q-learning.pkl") # Try to use another model
print(model)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
```python
model = load_from_hub(
repo_id="ThomasSimonini/q-FrozenLake-v1-no-slippery", filename="q-learning.pkl"
) # Try to use another model
env = gym.make(model["env_id"], is_slippery=False)
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
## Some additional challenges 🏆
The best way to learn **is to try things on your own**! As you saw, the current agent is not doing great. As a first suggestion, you can train for more steps. With 1,000,000 steps, we saw some great results!
In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?
Here are some ideas to climb up the leaderboard:
* Train more steps
* Try different hyperparameters by looking at what your classmates have done.
* **Push your new trained model** on the Hub 🔥
Are walking on ice and driving taxis too boring to you? Try to **change the environment**, why not use FrozenLake-v1 slippery version? Check how they work [using the gymnasium documentation](https://gymnasium.farama.org/) and have fun 🎉.
_____________________________________________________________________
Congrats 🥳, you've just implemented, trained, and uploaded your first Reinforcement Learning agent.
Understanding Q-Learning is an **important step to understanding value-based methods.**
In the next Unit with Deep Q-Learning, we'll see that while creating and updating a Q-table was a good strategy — **however, it is not scalable.**
For instance, imagine you create an agent that learns to play Doom.
<img src="https://vizdoom.cs.put.edu.pl/user/pages/01.tutorial/basic.png" alt="Doom"/>
Doom is a large environment with a huge state space (millions of different states). Creating and updating a Q-table for that environment would not be efficient.
That's why we'll study Deep Q-Learning in the next unit, an algorithm **where we use a neural network that approximates, given a state, the different Q-values for each action.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/atari-envs.gif" alt="Environments"/>
See you in Unit 3! 🔥
## Keep learning, stay awesome 🤗
| deep-rl-class/units/en/unit2/hands-on.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit2/hands-on.mdx",
"repo_id": "deep-rl-class",
"token_count": 13932
} | 79 |
# Glossary
This is a community-created glossary. Contributions are welcomed!
- **Tabular Method:** Type of problem in which the state and action spaces are small enough to approximate value functions to be represented as arrays and tables.
**Q-learning** is an example of tabular method since a table is used to represent the value for different state-action pairs.
- **Deep Q-Learning:** Method that trains a neural network to approximate, given a state, the different **Q-values** for each possible action at that state.
It is used to solve problems when observational space is too big to apply a tabular Q-Learning approach.
- **Temporal Limitation** is a difficulty presented when the environment state is represented by frames. A frame by itself does not provide temporal information.
In order to obtain temporal information, we need to **stack** a number of frames together.
- **Phases of Deep Q-Learning:**
- **Sampling:** Actions are performed, and observed experience tuples are stored in a **replay memory**.
- **Training:** Batches of tuples are selected randomly and the neural network updates its weights using gradient descent.
- **Solutions to stabilize Deep Q-Learning:**
- **Experience Replay:** A replay memory is created to save experiences samples that can be reused during training.
This allows the agent to learn from the same experiences multiple times. Also, it helps the agent avoid forgetting previous experiences as it gets new ones.
- **Random sampling** from replay buffer allows to remove correlation in the observation sequences and prevents action values from oscillating or diverging
catastrophically.
- **Fixed Q-Target:** In order to calculate the **Q-Target** we need to estimate the discounted optimal **Q-value** of the next state by using Bellman equation. The problem
is that the same network weights are used to calculate the **Q-Target** and the **Q-value**. This means that everytime we are modifying the **Q-value**, the **Q-Target** also moves with it.
To avoid this issue, a separate network with fixed parameters is used for estimating the Temporal Difference Target. The target network is updated by copying parameters from
our Deep Q-Network after certain **C steps**.
- **Double DQN:** Method to handle **overestimation** of **Q-Values**. This solution uses two networks to decouple the action selection from the target **Value generation**:
- **DQN Network** to select the best action to take for the next state (the action with the highest **Q-Value**)
- **Target Network** to calculate the target **Q-Value** of taking that action at the next state.
This approach reduces the **Q-Values** overestimation, it helps to train faster and have more stable learning.
If you want to improve the course, you can [open a Pull Request.](https://github.com/huggingface/deep-rl-class/pulls)
This glossary was made possible thanks to:
- [Dario Paez](https://github.com/dario248)
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# (Optional) What is Curiosity in Deep Reinforcement Learning?
This is an (optional) introduction to Curiosity. If you want to learn more, you can read two additional articles where we dive into the mathematical details:
- [Curiosity-Driven Learning through Next State Prediction](https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-next-state-prediction-f7f4e2f592fa)
- [Random Network Distillation: a new take on Curiosity-Driven Learning](https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-random-network-distillation-488ffd8e5938)
## Two Major Problems in Modern RL
To understand what Curiosity is, we first need to understand the two major problems with RL:
First, the *sparse rewards problem:* that is, **most rewards do not contain information, and hence are set to zero**.
Remember that RL is based on the *reward hypothesis*, which is the idea that each goal can be described as the maximization of the rewards. Therefore, rewards act as feedback for RL agents; **if they don’t receive any, their knowledge of which action is appropriate (or not) cannot change**.
<figure>
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit5/curiosity1.png" alt="Curiosity"/>
<figcaption>Source: Thanks to the reward, our agent knows that this action at that state was good</figcaption>
</figure>
For instance, in [Vizdoom](https://vizdoom.cs.put.edu.pl/), a set of environments based on the game Doom “DoomMyWayHome,” your agent is only rewarded **if it finds the vest**.
However, the vest is far away from your starting point, so most of your rewards will be zero. Therefore, if our agent does not receive useful feedback (dense rewards), it will take much longer to learn an optimal policy, and **it can spend time turning around without finding the goal**.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit5/curiosity2.png" alt="Curiosity"/>
The second big problem is that **the extrinsic reward function is handmade; in each environment, a human has to implement a reward function**. But how we can scale that in big and complex environments?
## So what is Curiosity?
A solution to these problems is **to develop a reward function intrinsic to the agent, i.e., generated by the agent itself**. The agent will act as a self-learner since it will be the student and its own feedback master.
**This intrinsic reward mechanism is known as Curiosity** because this reward pushes the agent to explore states that are novel/unfamiliar. To achieve that, our agent will receive a high reward when exploring new trajectories.
This reward is inspired by how humans act. ** We naturally have an intrinsic desire to explore environments and discover new things**.
There are different ways to calculate this intrinsic reward. The classical approach (Curiosity through next-state prediction) is to calculate Curiosity **as the error of our agent in predicting the next state, given the current state and action taken**.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit5/curiosity3.png" alt="Curiosity"/>
Because the idea of Curiosity is to **encourage our agent to perform actions that reduce the uncertainty in the agent’s ability to predict the consequences of its actions** (uncertainty will be higher in areas where the agent has spent less time or in areas with complex dynamics).
If the agent spends a lot of time on these states, it will be good at predicting the next state (low Curiosity). On the other hand, if it’s in a new, unexplored state, it will be hard to predict the following state (high Curiosity).
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit5/curiosity4.png" alt="Curiosity"/>
Using Curiosity will push our agent to favor transitions with high prediction error (which will be higher in areas where the agent has spent less time, or in areas with complex dynamics) and **consequently better explore our environment**.
There’s also **other curiosity calculation methods**. ML-Agents uses a more advanced one called Curiosity through random network distillation. This is out of the scope of the tutorial but if you’re interested [I wrote an article explaining it in detail](https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-random-network-distillation-488ffd8e5938).
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# Hands-on
Now that you learned the basics of multi-agents, you're ready to train your first agents in a multi-agent system: **a 2vs2 soccer team that needs to beat the opponent team**.
And you’re going to participate in AI vs. AI challenges where your trained agent will compete against other classmates’ **agents every day and be ranked on a new leaderboard.**
To validate this hands-on for the certification process, you just need to push a trained model. There **are no minimal results to attain to validate it.**
For more information about the certification process, check this section 👉 [https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process)
This hands-on will be different since to get correct results **you need to train your agents from 4 hours to 8 hours**. And given the risk of timeout in Colab, we advise you to train on your computer. You don’t need a supercomputer: a simple laptop is good enough for this exercise.
Let's get started! 🔥
## What is AI vs. AI?
AI vs. AI is an open-source tool we developed at Hugging Face to compete agents on the Hub against one another in a multi-agent setting. These models are then ranked in a leaderboard.
The idea of this tool is to have a robust evaluation tool: **by evaluating your agent with a lot of others, you’ll get a good idea of the quality of your policy.**
More precisely, AI vs. AI is three tools:
- A *matchmaking process* defining the matches (which model against which) and running the model fights using a background task in the Space.
- A *leaderboard* getting the match history results and displaying the models’ ELO ratings: [https://huggingface.co/spaces/huggingface-projects/AIvsAI-SoccerTwos](https://huggingface.co/spaces/huggingface-projects/AIvsAI-SoccerTwos)
- A *Space demo* to visualize your agents playing against others: [https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos](https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos)
In addition to these three tools, your classmate cyllum created a 🤗 SoccerTwos Challenge Analytics where you can check the detailed match results of a model: [https://huggingface.co/spaces/cyllum/soccertwos-analytics](https://huggingface.co/spaces/cyllum/soccertwos-analytics)
We're [wrote a blog post to explain this AI vs. AI tool in detail](https://huggingface.co/blog/aivsai), but to give you the big picture it works this way:
- Every four hours, our algorithm **fetches all the available models for a given environment (in our case ML-Agents-SoccerTwos).**
- It creates a **queue of matches with the matchmaking algorithm.**
- We simulate the match in a Unity headless process and **gather the match result** (1 if the first model won, 0.5 if it’s a draw, 0 if the second model won) in a Dataset.
- Then, when all matches from the matches queue are done, **we update the ELO score for each model and update the leaderboard.**
### Competition Rules
This first AI vs. AI competition **is an experiment**: the goal is to improve the tool in the future with your feedback. So some **breakups can happen during the challenge**. But don't worry
**all the results are saved in a dataset so we can always restart the calculation correctly without losing information**.
In order for your model to get correctly evaluated against others you need to follow these rules:
1. **You can't change the observation space or action space of the agent.** By doing that your model will not work during evaluation.
2. You **can't use a custom trainer for now,** you need to use the Unity MLAgents ones.
3. We provide executables to train your agents. You can also use the Unity Editor if you prefer **, but to avoid bugs, we advise that you use our executables**.
What will make the difference during this challenge are **the hyperparameters you choose**.
We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the GitHub Repo](https://github.com/huggingface/deep-rl-class/issues).
### Chat with your classmates, share advice and ask questions on Discord
- We created a new channel called `ai-vs-ai-challenge` to exchange advice and ask questions.
- If you didn’t join the discord server yet, you can [join here](https://discord.gg/ydHrjt3WP5)
## Step 0: Install MLAgents and download the correct executable
We advise you to use [conda](https://docs.conda.io/en/latest/) as a package manager and create a new environment.
With conda, we create a new environment called rl with **Python 3.10.12**:
```bash
conda create --name rl python=3.10.12
conda activate rl
```
To be able to train our agents correctly and push to the Hub, we need to install ML-Agents
```bash
git clone https://github.com/Unity-Technologies/ml-agents
```
When the cloning is done (it takes 2.63 GB), we go inside the repository and install the package
```bash
cd ml-agents
pip install -e ./ml-agents-envs
pip install -e ./ml-agents
```
Finally, you need to install git-lfs: https://git-lfs.com/
Now that it’s installed, we need to add the environment training executable. Based on your operating system you need to download one of them, unzip it and place it in a new folder inside `ml-agents` that you call `training-envs-executables`
At the end your executable should be in `ml-agents/training-envs-executables/SoccerTwos`
Windows: Download [this executable](https://drive.google.com/file/d/1sqFxbEdTMubjVktnV4C6ICjp89wLhUcP/view?usp=sharing)
Linux (Ubuntu): Download [this executable](https://drive.google.com/file/d/1KuqBKYiXiIcU4kNMqEzhgypuFP5_45CL/view?usp=sharing)
Mac: Download [this executable](https://drive.google.com/drive/folders/1h7YB0qwjoxxghApQdEUQmk95ZwIDxrPG?usp=share_link)
⚠ For Mac you need also to call this `xattr -cr training-envs-executables/SoccerTwos/SoccerTwos.app` to be able to run SoccerTwos
## Step 1: Understand the environment
The environment is called `SoccerTwos`. The Unity MLAgents Team made it. You can find its documentation [here](https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learning-Environment-Examples.md#soccer-twos)
The goal in this environment **is to get the ball into the opponent's goal while preventing the ball from entering your own goal.**
<figure>
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/soccertwos.gif" alt="SoccerTwos"/>
<figcaption>This environment was made by the <a href="https://github.com/Unity-Technologies/ml-agents"> Unity MLAgents Team</a></figcaption>
</figure>
### The reward function
The reward function is:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/soccerreward.png" alt="SoccerTwos Reward"/>
### The observation space
The observation space is composed of vectors of size 336:
- 11 ray-casts forward distributed over 120 degrees (264 state dimensions)
- 3 ray-casts backward distributed over 90 degrees (72 state dimensions)
- Both of these ray-casts can detect 6 objects:
- Ball
- Blue Goal
- Purple Goal
- Wall
- Blue Agent
- Purple Agent
### The action space
The action space is three discrete branches:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/socceraction.png" alt="SoccerTwos Action"/>
## Step 2: Understand MA-POCA
We know how to train agents to play against others: **we can use self-play.** This is a perfect technique for a 1vs1.
But in our case we’re 2vs2, and each team has 2 agents. How then can we **train cooperative behavior for groups of agents?**
As explained in the [Unity Blog](https://blog.unity.com/technology/ml-agents-v20-release-now-supports-training-complex-cooperative-behaviors), agents typically receive a reward as a group (+1 - penalty) when the team scores a goal. This implies that **every agent on the team is rewarded even if each agent didn’t contribute the same to the win**, which makes it difficult to learn what to do independently.
The Unity MLAgents team developed the solution in a new multi-agent trainer called *MA-POCA (Multi-Agent POsthumous Credit Assignment)*.
The idea is simple but powerful: a centralized critic **processes the states of all agents in the team to estimate how well each agent is doing**. Think of this critic as a coach.
This allows each agent to **make decisions based only on what it perceives locally**, and **simultaneously evaluate how good its behavior is in the context of the whole group**.
<figure>
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/mapoca.png" alt="MA POCA"/>
<figcaption>This illustrates MA-POCA’s centralized learning and decentralized execution. Source: <a href="https://blog.unity.com/technology/ml-agents-plays-dodgeball">MLAgents Plays Dodgeball</a>
</figcaption>
</figure>
The solution then is to use Self-Play with an MA-POCA trainer (called poca). The poca trainer will help us to train cooperative behavior and self-play to win against an opponent team.
If you want to dive deeper into this MA-POCA algorithm, you need to read the paper they published [here](https://arxiv.org/pdf/2111.05992.pdf) and the sources we put on the additional readings section.
## Step 3: Define the config file
We already learned in [Unit 5](https://huggingface.co/deep-rl-course/unit5/introduction) that in ML-Agents, you define **the training hyperparameters in `config.yaml` files.**
There are multiple hyperparameters. To understand them better, you should read the explanations for each of them in **[the documentation](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Training-Configuration-File.md)**
The config file we’re going to use here is in `./config/poca/SoccerTwos.yaml`. It looks like this:
```csharp
behaviors:
SoccerTwos:
trainer_type: poca
hyperparameters:
batch_size: 2048
buffer_size: 20480
learning_rate: 0.0003
beta: 0.005
epsilon: 0.2
lambd: 0.95
num_epoch: 3
learning_rate_schedule: constant
network_settings:
normalize: false
hidden_units: 512
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 5000000
time_horizon: 1000
summary_freq: 10000
self_play:
save_steps: 50000
team_change: 200000
swap_steps: 2000
window: 10
play_against_latest_model_ratio: 0.5
initial_elo: 1200.0
```
Compared to Pyramids or SnowballTarget, we have new hyperparameters with a self-play part. How you modify them can be critical in getting good results.
The advice I can give you here is to check the explanation and recommended value for each parameters (especially self-play ones) against **[the documentation](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Training-Configuration-File.md).**
Now that you’ve modified our config file, you’re ready to train your agents.
## Step 4: Start the training
To train the agents, we need to **launch mlagents-learn and select the executable containing the environment.**
We define four parameters:
1. `mlagents-learn <config>`: the path where the hyperparameter config file is.
2. `-env`: where the environment executable is.
3. `-run_id`: the name you want to give to your training run id.
4. `-no-graphics`: to not launch the visualization during the training.
Depending on your hardware, 5M timesteps (the recommended value, but you can also try 10M) will take 5 to 8 hours of training. You can continue using your computer in the meantime, but I advise deactivating the computer standby mode to prevent the training from being stopped.
Depending on the executable you use (windows, ubuntu, mac) the training command will look like this (your executable path can be different so don’t hesitate to check before running).
```bash
mlagents-learn ./config/poca/SoccerTwos.yaml --env=./training-envs-executables/SoccerTwos.exe --run-id="SoccerTwos" --no-graphics
```
The executable contains 8 copies of SoccerTwos.
⚠️ It’s normal if you don’t see a big increase of ELO score (and even a decrease below 1200) before 2M timesteps, since your agents will spend most of their time moving randomly on the field before being able to goal.
⚠️ You can stop the training with Ctrl + C but beware of typing this command only once to stop the training since MLAgents needs to generate a final .onnx file before closing the run.
## Step 5: **Push the agent to the Hugging Face Hub**
Now that we trained our agents, we’re **ready to push them to the Hub to be able to participate in the AI vs. AI challenge and visualize them playing on your browser🔥.**
To be able to share your model with the community, there are three more steps to follow:
1️⃣ (If it’s not already done) create an account to HF ➡ [https://huggingface.co/join](https://huggingface.co/join)
2️⃣ Sign in and store your authentication token from the Hugging Face website.
Create a new token (https://huggingface.co/settings/tokens) **with write role**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/create-token.jpg" alt="Create HF Token">
Copy the token, run this, and paste the token
```bash
huggingface-cli login
```
Then, we need to run `mlagents-push-to-hf`.
And we define four parameters:
1. `-run-id`: the name of the training run id.
2. `-local-dir`: where the agent was saved, it’s results/<run_id name>, so in my case results/First Training.
3. `-repo-id`: the name of the Hugging Face repo you want to create or update. It’s always <your huggingface username>/<the repo name>
If the repo does not exist **it will be created automatically**
4. `--commit-message`: since HF repos are git repositories you need to give a commit message.
In my case
```bash
mlagents-push-to-hf --run-id="SoccerTwos" --local-dir="./results/SoccerTwos" --repo-id="ThomasSimonini/poca-SoccerTwos" --commit-message="First Push"`
```
```bash
mlagents-push-to-hf --run-id= # Add your run id --local-dir= # Your local dir --repo-id= # Your repo id --commit-message="First Push"
```
If everything worked you should see this at the end of the process (but with a different url 😆) :
Your model is pushed to the Hub. You can view your model here: https://huggingface.co/ThomasSimonini/poca-SoccerTwos
It's the link to your model. It contains a model card that explains how to use it, your Tensorboard, and your config file. **What's awesome is that it's a git repository, which means you can have different commits, update your repository with a new push, etc.**
## Step 6: Verify that your model is ready for AI vs AI Challenge
Now that your model is pushed to the Hub, **it’s going to be added automatically to the AI vs AI Challenge model pool.** It can take a little bit of time before your model is added to the leaderboard given we do a run of matches every 4h.
But to ensure that everything works perfectly you need to check:
1. That you have this tag in your model: ML-Agents-SoccerTwos. This is the tag we use to select models to be added to the challenge pool. To do that go to your model and check the tags
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/verify1.png" alt="Verify"/>
If it’s not the case you just need to modify the readme and add it
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/verify2.png" alt="Verify"/>
2. That you have a `SoccerTwos.onnx` file
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/verify3.png" alt="Verify"/>
We strongly suggest that you create a new model when you push to the Hub if you want to train it again or train a new version.
## Step 7: Visualize some match in our demo
Now that your model is part of AI vs AI Challenge, **you can visualize how good it is compared to others**: https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
In order to do that, you just need to go to this demo:
- Select your model as team blue (or team purple if you prefer) and another model to compete against. The best opponents to compare your model to are either whoever is on top of the leaderboard or the [baseline model](https://huggingface.co/unity/MLAgents-SoccerTwos)
The matches you see live are not used in the calculation of your result **but they are a good way to visualize how good your agent is**.
And don't hesitate to share the best score your agent gets on discord in the #rl-i-made-this channel 🔥
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# Conclusion [[conclusion]]
Congrats on finishing this bonus unit!
You can now sit and enjoy playing with your Huggy 🐶. And don't **forget to spread the love by sharing Huggy with your friends 🤗**. And if you share about it on social media, **please tag us @huggingface and me @simoninithomas**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit-bonus1/huggy-cover.jpeg" alt="Huggy cover" width="100%">
Finally, we would love **to hear what you think of the course and how we can improve it**. If you have some feedback then please 👉 [fill out this form](https://forms.gle/BzKXWzLAGZESGNaE9)
### Keep Learning, stay awesome 🤗
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# Model Based Reinforcement Learning (MBRL)
Model-based reinforcement learning only differs from its model-free counterpart in learning a *dynamics model*, but that has substantial downstream effects on how the decisions are made.
The dynamics model usually models the environment transition dynamics, \\( s_{t+1} = f_\theta (s_t, a_t) \\), but things like inverse dynamics models (mapping from states to actions) or reward models (predicting rewards) can be used in this framework.
## Simple definition
- There is an agent that repeatedly tries to solve a problem, **accumulating state and action data**.
- With that data, the agent creates a structured learning tool, *a dynamics model*, to reason about the world.
- With the dynamics model, the agent **decides how to act by predicting the future**.
- With those actions, **the agent collects more data, improves said model, and hopefully improves future actions**.
## Academic definition
Model-based reinforcement learning (MBRL) follows the framework of an agent interacting in an environment, **learning a model of said environment**, and then **leveraging the model for control (making decisions).
Specifically, the agent acts in a Markov Decision Process (MDP) governed by a transition function \\( s_{t+1} = f (s_t , a_t) \\) and returns a reward at each step \\( r(s_t, a_t) \\). With a collected dataset \\( D :={ s_i, a_i, s_{i+1}, r_i} \\), the agent learns a model, \\( s_{t+1} = f_\theta (s_t , a_t) \\) **to minimize the negative log-likelihood of the transitions**.
We employ sample-based model-predictive control (MPC) using the learned dynamics model, which optimizes the expected reward over a finite, recursively predicted horizon, \\( \tau \\), from a set of actions sampled from a uniform distribution \\( U(a) \\), (see [paper](https://arxiv.org/pdf/2002.04523) or [paper](https://arxiv.org/pdf/2012.09156.pdf) or [paper](https://arxiv.org/pdf/2009.01221.pdf)).
## Further reading
For more information on MBRL, we recommend you check out the following resources:
- A [blog post on debugging MBRL](https://www.natolambert.com/writing/debugging-mbrl).
- A [recent review paper on MBRL](https://arxiv.org/abs/2006.16712),
## Author
This section was written by <a href="https://twitter.com/natolambert"> Nathan Lambert </a>
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</a>
</p>
🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
🤗 Diffusers offers three core components:
- State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code.
- Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality.
- Pretrained [models](https://huggingface.co/docs/diffusers/api/models/overview) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
## Installation
We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
### PyTorch
With `pip` (official package):
```bash
pip install --upgrade diffusers[torch]
```
With `conda` (maintained by the community):
```sh
conda install -c conda-forge diffusers
```
### Flax
With `pip` (official package):
```bash
pip install --upgrade diffusers[flax]
```
### Apple Silicon (M1/M2) support
Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.
## Quickstart
Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 19000+ checkpoints):
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipeline.to("cuda")
pipeline("An image of a squirrel in Picasso style").images[0]
```
You can also dig into the models and schedulers toolbox to build your own diffusion system:
```python
from diffusers import DDPMScheduler, UNet2DModel
from PIL import Image
import torch
scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
scheduler.set_timesteps(50)
sample_size = model.config.sample_size
noise = torch.randn((1, 3, sample_size, sample_size), device="cuda")
input = noise
for t in scheduler.timesteps:
with torch.no_grad():
noisy_residual = model(input, t).sample
prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
input = prev_noisy_sample
image = (input / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = Image.fromarray((image * 255).round().astype("uint8"))
image
```
Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today!
## How to navigate the documentation
| **Documentation** | **What can I learn?** |
|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. |
| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |
| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |
| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. |
| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. |
## Contribution
We ❤️ contributions from the open-source community!
If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).
You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.
- See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute
- See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines
- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕.
## Popular Tasks & Pipelines
<table>
<tr>
<th>Task</th>
<th>Pipeline</th>
<th>🤗 Hub</th>
</tr>
<tr style="border-top: 2px solid black">
<td>Unconditional Image Generation</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/ddpm"> DDPM </a></td>
<td><a href="https://huggingface.co/google/ddpm-ema-church-256"> google/ddpm-ema-church-256 </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img">Stable Diffusion Text-to-Image</a></td>
<td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/unclip">unCLIP</a></td>
<td><a href="https://huggingface.co/kakaobrain/karlo-v1-alpha"> kakaobrain/karlo-v1-alpha </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/deepfloyd_if">DeepFloyd IF</a></td>
<td><a href="https://huggingface.co/DeepFloyd/IF-I-XL-v1.0"> DeepFloyd/IF-I-XL-v1.0 </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/kandinsky">Kandinsky</a></td>
<td><a href="https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder"> kandinsky-community/kandinsky-2-2-decoder </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/controlnet">ControlNet</a></td>
<td><a href="https://huggingface.co/lllyasviel/sd-controlnet-canny"> lllyasviel/sd-controlnet-canny </a></td>
</tr>
<tr>
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/pix2pix">InstructPix2Pix</a></td>
<td><a href="https://huggingface.co/timbrooks/instruct-pix2pix"> timbrooks/instruct-pix2pix </a></td>
</tr>
<tr>
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img">Stable Diffusion Image-to-Image</a></td>
<td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-guided Image Inpainting</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
<td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Image Variation</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/image_variation">Stable Diffusion Image Variation</a></td>
<td><a href="https://huggingface.co/lambdalabs/sd-image-variations-diffusers"> lambdalabs/sd-image-variations-diffusers </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Super Resolution</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/upscale">Stable Diffusion Upscale</a></td>
<td><a href="https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler"> stabilityai/stable-diffusion-x4-upscaler </a></td>
</tr>
<tr>
<td>Super Resolution</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/latent_upscale">Stable Diffusion Latent Upscale</a></td>
<td><a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler"> stabilityai/sd-x2-latent-upscaler </a></td>
</tr>
</table>
## Popular libraries using 🧨 Diffusers
- https://github.com/microsoft/TaskMatrix
- https://github.com/invoke-ai/InvokeAI
- https://github.com/apple/ml-stable-diffusion
- https://github.com/Sanster/lama-cleaner
- https://github.com/IDEA-Research/Grounded-Segment-Anything
- https://github.com/ashawkey/stable-dreamfusion
- https://github.com/deep-floyd/IF
- https://github.com/bentoml/BentoML
- https://github.com/bmaltais/kohya_ss
- +8000 other amazing GitHub repositories 💪
Thank you for using us ❤️.
## Credits
This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:
- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion)
- @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion)
- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim)
- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch)
We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights.
## Citation
```bibtex
@misc{von-platen-etal-2022-diffusers,
author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
title = {Diffusers: State-of-the-art diffusion models},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/diffusers}}
}
```
| diffusers/README.md/0 | {
"file_path": "diffusers/README.md",
"repo_id": "diffusers",
"token_count": 5394
} | 85 |
FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
LABEL maintainer="Hugging Face"
LABEL repository="diffusers"
ENV DEBIAN_FRONTEND=noninteractive
RUN apt update && \
apt install -y bash \
build-essential \
git \
git-lfs \
curl \
ca-certificates \
libsndfile1-dev \
libgl1 \
python3.9 \
python3.9-dev \
python3-pip \
python3.9-venv && \
rm -rf /var/lib/apt/lists
# make sure to use venv
RUN python3.9 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
RUN python3.9 -m pip install --no-cache-dir --upgrade pip && \
python3.9 -m pip install --no-cache-dir \
torch==2.1.2 \
torchvision==0.16.2 \
torchaudio==2.1.2 \
invisible_watermark && \
python3.9 -m pip install --no-cache-dir \
accelerate \
datasets \
hf-doc-builder \
huggingface-hub \
Jinja2 \
librosa \
numpy \
scipy \
tensorboard \
transformers
CMD ["/bin/bash"]
| diffusers/docker/diffusers-pytorch-compile-cuda/Dockerfile/0 | {
"file_path": "diffusers/docker/diffusers-pytorch-compile-cuda/Dockerfile",
"repo_id": "diffusers",
"token_count": 450
} | 86 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Single files
Diffusers supports loading pretrained pipeline (or model) weights stored in a single file, such as a `ckpt` or `safetensors` file. These single file types are typically produced from community trained models. There are three classes for loading single file weights:
- [`FromSingleFileMixin`] supports loading pretrained pipeline weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
- [`FromOriginalVAEMixin`] supports loading a pretrained [`AutoencoderKL`] from pretrained ControlNet weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
- [`FromOriginalControlnetMixin`] supports loading pretrained ControlNet weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
<Tip>
To learn more about how to load single file weights, see the [Load different Stable Diffusion formats](../../using-diffusers/other-formats) loading guide.
</Tip>
## FromSingleFileMixin
[[autodoc]] loaders.single_file.FromSingleFileMixin
## FromOriginalVAEMixin
[[autodoc]] loaders.autoencoder.FromOriginalVAEMixin
## FromOriginalControlnetMixin
[[autodoc]] loaders.controlnet.FromOriginalControlNetMixin | diffusers/docs/source/en/api/loaders/single_file.md/0 | {
"file_path": "diffusers/docs/source/en/api/loaders/single_file.md",
"repo_id": "diffusers",
"token_count": 480
} | 87 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Dance Diffusion
[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.
Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by [Harmonai](https://github.com/Harmonai-org).
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
## DanceDiffusionPipeline
[[autodoc]] DanceDiffusionPipeline
- all
- __call__
## AudioPipelineOutput
[[autodoc]] pipelines.AudioPipelineOutput
| diffusers/docs/source/en/api/pipelines/dance_diffusion.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/dance_diffusion.md",
"repo_id": "diffusers",
"token_count": 370
} | 88 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# SDXL Turbo
Stable Diffusion XL (SDXL) Turbo was proposed in [Adversarial Diffusion Distillation](https://stability.ai/research/adversarial-diffusion-distillation) by Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach.
The abstract from the paper is:
*We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. Our analyses show that our model clearly outperforms existing few-step methods (GANs,Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.*
## Tips
- SDXL Turbo uses the exact same architecture as [SDXL](./stable_diffusion_xl), which means it also has the same API. Please refer to the [SDXL](./stable_diffusion_xl) API reference for more details.
- SDXL Turbo should disable guidance scale by setting `guidance_scale=0.0`
- SDXL Turbo should use `timestep_spacing='trailing'` for the scheduler and use between 1 and 4 steps.
- SDXL Turbo has been trained to generate images of size 512x512.
- SDXL Turbo is open-access, but not open-source meaning that one might have to buy a model license in order to use it for commercial applications. Make sure to read the [official model card](https://huggingface.co/stabilityai/sdxl-turbo) to learn more.
<Tip>
To learn how to use SDXL Turbo for various tasks, how to optimize performance, and other usage examples, take a look at the [SDXL Turbo](../../../using-diffusers/sdxl_turbo) guide.
Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints!
</Tip>
| diffusers/docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md",
"repo_id": "diffusers",
"token_count": 678
} | 89 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# How to run Stable Diffusion with Core ML
[Core ML](https://developer.apple.com/documentation/coreml) is the model format and machine learning library supported by Apple frameworks. If you are interested in running Stable Diffusion models inside your macOS or iOS/iPadOS apps, this guide will show you how to convert existing PyTorch checkpoints into the Core ML format and use them for inference with Python or Swift.
Core ML models can leverage all the compute engines available in Apple devices: the CPU, the GPU, and the Apple Neural Engine (or ANE, a tensor-optimized accelerator available in Apple Silicon Macs and modern iPhones/iPads). Depending on the model and the device it's running on, Core ML can mix and match compute engines too, so some portions of the model may run on the CPU while others run on GPU, for example.
<Tip>
You can also run the `diffusers` Python codebase on Apple Silicon Macs using the `mps` accelerator built into PyTorch. This approach is explained in depth in [the mps guide](mps), but it is not compatible with native apps.
</Tip>
## Stable Diffusion Core ML Checkpoints
Stable Diffusion weights (or checkpoints) are stored in the PyTorch format, so you need to convert them to the Core ML format before we can use them inside native apps.
Thankfully, Apple engineers developed [a conversion tool](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) based on `diffusers` to convert the PyTorch checkpoints to Core ML.
Before you convert a model, though, take a moment to explore the Hugging Face Hub – chances are the model you're interested in is already available in Core ML format:
- the [Apple](https://huggingface.co/apple) organization includes Stable Diffusion versions 1.4, 1.5, 2.0 base, and 2.1 base
- [coreml community](https://huggingface.co/coreml-community) includes custom finetuned models
- use this [filter](https://huggingface.co/models?pipeline_tag=text-to-image&library=coreml&p=2&sort=likes) to return all available Core ML checkpoints
If you can't find the model you're interested in, we recommend you follow the instructions for [Converting Models to Core ML](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) by Apple.
## Selecting the Core ML Variant to Use
Stable Diffusion models can be converted to different Core ML variants intended for different purposes:
- The type of attention blocks used. The attention operation is used to "pay attention" to the relationship between different areas in the image representations and to understand how the image and text representations are related. Attention is compute- and memory-intensive, so different implementations exist that consider the hardware characteristics of different devices. For Core ML Stable Diffusion models, there are two attention variants:
* `split_einsum` ([introduced by Apple](https://machinelearning.apple.com/research/neural-engine-transformers)) is optimized for ANE devices, which is available in modern iPhones, iPads and M-series computers.
* The "original" attention (the base implementation used in `diffusers`) is only compatible with CPU/GPU and not ANE. It can be *faster* to run your model on CPU + GPU using `original` attention than ANE. See [this performance benchmark](https://huggingface.co/blog/fast-mac-diffusers#performance-benchmarks) as well as some [additional measures provided by the community](https://github.com/huggingface/swift-coreml-diffusers/issues/31) for additional details.
- The supported inference framework.
* `packages` are suitable for Python inference. This can be used to test converted Core ML models before attempting to integrate them inside native apps, or if you want to explore Core ML performance but don't need to support native apps. For example, an application with a web UI could perfectly use a Python Core ML backend.
* `compiled` models are required for Swift code. The `compiled` models in the Hub split the large UNet model weights into several files for compatibility with iOS and iPadOS devices. This corresponds to the [`--chunk-unet` conversion option](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). If you want to support native apps, then you need to select the `compiled` variant.
The official Core ML Stable Diffusion [models](https://huggingface.co/apple/coreml-stable-diffusion-v1-4/tree/main) include these variants, but the community ones may vary:
```
coreml-stable-diffusion-v1-4
├── README.md
├── original
│ ├── compiled
│ └── packages
└── split_einsum
├── compiled
└── packages
```
You can download and use the variant you need as shown below.
## Core ML Inference in Python
Install the following libraries to run Core ML inference in Python:
```bash
pip install huggingface_hub
pip install git+https://github.com/apple/ml-stable-diffusion
```
### Download the Model Checkpoints
To run inference in Python, use one of the versions stored in the `packages` folders because the `compiled` ones are only compatible with Swift. You may choose whether you want to use `original` or `split_einsum` attention.
This is how you'd download the `original` attention variant from the Hub to a directory called `models`:
```Python
from huggingface_hub import snapshot_download
from pathlib import Path
repo_id = "apple/coreml-stable-diffusion-v1-4"
variant = "original/packages"
model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_"))
snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False)
print(f"Model downloaded at {model_path}")
```
### Inference[[python-inference]]
Once you have downloaded a snapshot of the model, you can test it using Apple's Python script.
```shell
python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i models/coreml-stable-diffusion-v1-4_original_packages -o </path/to/output/image> --compute-unit CPU_AND_GPU --seed 93
```
Pass the path of the downloaded checkpoint with `-i` flag to the script. `--compute-unit` indicates the hardware you want to allow for inference. It must be one of the following options: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. You may also provide an optional output path, and a seed for reproducibility.
The inference script assumes you're using the original version of the Stable Diffusion model, `CompVis/stable-diffusion-v1-4`. If you use another model, you *have* to specify its Hub id in the inference command line, using the `--model-version` option. This works for models already supported and custom models you trained or fine-tuned yourself.
For example, if you want to use [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5):
```shell
python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version runwayml/stable-diffusion-v1-5
```
## Core ML inference in Swift
Running inference in Swift is slightly faster than in Python because the models are already compiled in the `mlmodelc` format. This is noticeable on app startup when the model is loaded but shouldn’t be noticeable if you run several generations afterward.
### Download
To run inference in Swift on your Mac, you need one of the `compiled` checkpoint versions. We recommend you download them locally using Python code similar to the previous example, but with one of the `compiled` variants:
```Python
from huggingface_hub import snapshot_download
from pathlib import Path
repo_id = "apple/coreml-stable-diffusion-v1-4"
variant = "original/compiled"
model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_"))
snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False)
print(f"Model downloaded at {model_path}")
```
### Inference[[swift-inference]]
To run inference, please clone Apple's repo:
```bash
git clone https://github.com/apple/ml-stable-diffusion
cd ml-stable-diffusion
```
And then use Apple's command line tool, [Swift Package Manager](https://www.swift.org/package-manager/#):
```bash
swift run StableDiffusionSample --resource-path models/coreml-stable-diffusion-v1-4_original_compiled --compute-units all "a photo of an astronaut riding a horse on mars"
```
You have to specify in `--resource-path` one of the checkpoints downloaded in the previous step, so please make sure it contains compiled Core ML bundles with the extension `.mlmodelc`. The `--compute-units` has to be one of these values: `all`, `cpuOnly`, `cpuAndGPU`, `cpuAndNeuralEngine`.
For more details, please refer to the [instructions in Apple's repo](https://github.com/apple/ml-stable-diffusion).
## Supported Diffusers Features
The Core ML models and inference code don't support many of the features, options, and flexibility of 🧨 Diffusers. These are some of the limitations to keep in mind:
- Core ML models are only suitable for inference. They can't be used for training or fine-tuning.
- Only two schedulers have been ported to Swift, the default one used by Stable Diffusion and `DPMSolverMultistepScheduler`, which we ported to Swift from our `diffusers` implementation. We recommend you use `DPMSolverMultistepScheduler`, since it produces the same quality in about half the steps.
- Negative prompts, classifier-free guidance scale, and image-to-image tasks are available in the inference code. Advanced features such as depth guidance, ControlNet, and latent upscalers are not available yet.
Apple's [conversion and inference repo](https://github.com/apple/ml-stable-diffusion) and our own [swift-coreml-diffusers](https://github.com/huggingface/swift-coreml-diffusers) repos are intended as technology demonstrators to enable other developers to build upon.
If you feel strongly about any missing features, please feel free to open a feature request or, better yet, a contribution PR 🙂.
## Native Diffusers Swift app
One easy way to run Stable Diffusion on your own Apple hardware is to use [our open-source Swift repo](https://github.com/huggingface/swift-coreml-diffusers), based on `diffusers` and Apple's conversion and inference repo. You can study the code, compile it with [Xcode](https://developer.apple.com/xcode/) and adapt it for your own needs. For your convenience, there's also a [standalone Mac app in the App Store](https://apps.apple.com/app/diffusers/id1666309574), so you can play with it without having to deal with the code or IDE. If you are a developer and have determined that Core ML is the best solution to build your Stable Diffusion app, then you can use the rest of this guide to get started with your project. We can't wait to see what you'll build 🙂.
| diffusers/docs/source/en/optimization/coreml.md/0 | {
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# Create a dataset for training
There are many datasets on the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) to train a model on, but if you can't find one you're interested in or want to use your own, you can create a dataset with the 🤗 [Datasets](hf.co/docs/datasets) library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation.
This guide will show you two ways to create a dataset to finetune on:
- provide a folder of images to the `--train_data_dir` argument
- upload a dataset to the Hub and pass the dataset repository id to the `--dataset_name` argument
<Tip>
💡 Learn more about how to create an image dataset for training in the [Create an image dataset](https://huggingface.co/docs/datasets/image_dataset) guide.
</Tip>
## Provide a dataset as a folder
For unconditional generation, you can provide your own dataset as a folder of images. The training script uses the [`ImageFolder`](https://huggingface.co/docs/datasets/en/image_dataset#imagefolder) builder from 🤗 Datasets to automatically build a dataset from the folder. Your directory structure should look like:
```bash
data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png
```
Pass the path to the dataset directory to the `--train_data_dir` argument, and then you can start training:
```bash
accelerate launch train_unconditional.py \
--train_data_dir <path-to-train-directory> \
<other-arguments>
```
## Upload your data to the Hub
<Tip>
💡 For more details and context about creating and uploading a dataset to the Hub, take a look at the [Image search with 🤗 Datasets](https://huggingface.co/blog/image-search-datasets) post.
</Tip>
Start by creating a dataset with the [`ImageFolder`](https://huggingface.co/docs/datasets/image_load#imagefolder) feature, which creates an `image` column containing the PIL-encoded images.
You can use the `data_dir` or `data_files` parameters to specify the location of the dataset. The `data_files` parameter supports mapping specific files to dataset splits like `train` or `test`:
```python
from datasets import load_dataset
# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset(
"imagefolder",
data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip",
)
# example 4: providing several splits
dataset = load_dataset(
"imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]}
)
```
Then use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:
```python
# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")
# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)
```
Now the dataset is available for training by passing the dataset name to the `--dataset_name` argument:
```bash
accelerate launch --mixed_precision="fp16" train_text_to_image.py \
--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
--dataset_name="name_of_your_dataset" \
<other-arguments>
```
## Next steps
Now that you've created a dataset, you can plug it into the `train_data_dir` (if your dataset is local) or `dataset_name` (if your dataset is on the Hub) arguments of a training script.
For your next steps, feel free to try and use your dataset to train a model for [unconditional generation](unconditional_training) or [text-to-image generation](text2image)! | diffusers/docs/source/en/training/create_dataset.md/0 | {
"file_path": "diffusers/docs/source/en/training/create_dataset.md",
"repo_id": "diffusers",
"token_count": 1301
} | 91 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# AutoPipeline
🤗 Diffusers is able to complete many different tasks, and you can often reuse the same pretrained weights for multiple tasks such as text-to-image, image-to-image, and inpainting. If you're new to the library and diffusion models though, it may be difficult to know which pipeline to use for a task. For example, if you're using the [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint for text-to-image, you might not know that you could also use it for image-to-image and inpainting by loading the checkpoint with the [`StableDiffusionImg2ImgPipeline`] and [`StableDiffusionInpaintPipeline`] classes respectively.
The `AutoPipeline` class is designed to simplify the variety of pipelines in 🤗 Diffusers. It is a generic, *task-first* pipeline that lets you focus on the task. The `AutoPipeline` automatically detects the correct pipeline class to use, which makes it easier to load a checkpoint for a task without knowing the specific pipeline class name.
<Tip>
Take a look at the [AutoPipeline](../api/pipelines/auto_pipeline) reference to see which tasks are supported. Currently, it supports text-to-image, image-to-image, and inpainting.
</Tip>
This tutorial shows you how to use an `AutoPipeline` to automatically infer the pipeline class to load for a specific task, given the pretrained weights.
## Choose an AutoPipeline for your task
Start by picking a checkpoint. For example, if you're interested in text-to-image with the [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint, use [`AutoPipelineForText2Image`]:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
prompt = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"
image = pipeline(prompt, num_inference_steps=25).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-text2img.png" alt="generated image of peasant fighting dragon in wood cutting style"/>
</div>
Under the hood, [`AutoPipelineForText2Image`]:
1. automatically detects a `"stable-diffusion"` class from the [`model_index.json`](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json) file
2. loads the corresponding text-to-image [`StableDiffusionPipeline`] based on the `"stable-diffusion"` class name
Likewise, for image-to-image, [`AutoPipelineForImage2Image`] detects a `"stable-diffusion"` checkpoint from the `model_index.json` file and it'll load the corresponding [`StableDiffusionImg2ImgPipeline`] behind the scenes. You can also pass any additional arguments specific to the pipeline class such as `strength`, which determines the amount of noise or variation added to an input image:
```py
from diffusers import AutoPipelineForImage2Image
import torch
import requests
from PIL import Image
from io import BytesIO
pipeline = AutoPipelineForImage2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
use_safetensors=True,
).to("cuda")
prompt = "a portrait of a dog wearing a pearl earring"
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/1665_Girl_with_a_Pearl_Earring.jpg/800px-1665_Girl_with_a_Pearl_Earring.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
image.thumbnail((768, 768))
image = pipeline(prompt, image, num_inference_steps=200, strength=0.75, guidance_scale=10.5).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-img2img.png" alt="generated image of a vermeer portrait of a dog wearing a pearl earring"/>
</div>
And if you want to do inpainting, then [`AutoPipelineForInpainting`] loads the underlying [`StableDiffusionInpaintPipeline`] class in the same way:
```py
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForInpainting.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = load_image(img_url).convert("RGB")
mask_image = load_image(mask_url).convert("RGB")
prompt = "A majestic tiger sitting on a bench"
image = pipeline(prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-inpaint.png" alt="generated image of a tiger sitting on a bench"/>
</div>
If you try to load an unsupported checkpoint, it'll throw an error:
```py
from diffusers import AutoPipelineForImage2Image
import torch
pipeline = AutoPipelineForImage2Image.from_pretrained(
"openai/shap-e-img2img", torch_dtype=torch.float16, use_safetensors=True
)
"ValueError: AutoPipeline can't find a pipeline linked to ShapEImg2ImgPipeline for None"
```
## Use multiple pipelines
For some workflows or if you're loading many pipelines, it is more memory-efficient to reuse the same components from a checkpoint instead of reloading them which would unnecessarily consume additional memory. For example, if you're using a checkpoint for text-to-image and you want to use it again for image-to-image, use the [`~AutoPipelineForImage2Image.from_pipe`] method. This method creates a new pipeline from the components of a previously loaded pipeline at no additional memory cost.
The [`~AutoPipelineForImage2Image.from_pipe`] method detects the original pipeline class and maps it to the new pipeline class corresponding to the task you want to do. For example, if you load a `"stable-diffusion"` class pipeline for text-to-image:
```py
from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
import torch
pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
)
print(type(pipeline_text2img))
"<class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'>"
```
Then [`~AutoPipelineForImage2Image.from_pipe`] maps the original `"stable-diffusion"` pipeline class to [`StableDiffusionImg2ImgPipeline`]:
```py
pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img)
print(type(pipeline_img2img))
"<class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline'>"
```
If you passed an optional argument - like disabling the safety checker - to the original pipeline, this argument is also passed on to the new pipeline:
```py
from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
import torch
pipeline_text2img = AutoPipelineForText2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16,
use_safetensors=True,
requires_safety_checker=False,
).to("cuda")
pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img)
print(pipeline_img2img.config.requires_safety_checker)
"False"
```
You can overwrite any of the arguments and even configuration from the original pipeline if you want to change the behavior of the new pipeline. For example, to turn the safety checker back on and add the `strength` argument:
```py
pipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img, requires_safety_checker=True, strength=0.3)
print(pipeline_img2img.config.requires_safety_checker)
"True"
```
| diffusers/docs/source/en/tutorials/autopipeline.md/0 | {
"file_path": "diffusers/docs/source/en/tutorials/autopipeline.md",
"repo_id": "diffusers",
"token_count": 2762
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Improve generation quality with FreeU
[[open-in-colab]]
The UNet is responsible for denoising during the reverse diffusion process, and there are two distinct features in its architecture:
1. Backbone features primarily contribute to the denoising process
2. Skip features mainly introduce high-frequency features into the decoder module and can make the network overlook the semantics in the backbone features
However, the skip connection can sometimes introduce unnatural image details. [FreeU](https://hf.co/papers/2309.11497) is a technique for improving image quality by rebalancing the contributions from the UNet’s skip connections and backbone feature maps.
FreeU is applied during inference and it does not require any additional training. The technique works for different tasks such as text-to-image, image-to-image, and text-to-video.
In this guide, you will apply FreeU to the [`StableDiffusionPipeline`], [`StableDiffusionXLPipeline`], and [`TextToVideoSDPipeline`]. You need to install Diffusers from source to run the examples below.
## StableDiffusionPipeline
Load the pipeline:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None
).to("cuda")
```
Then enable the FreeU mechanism with the FreeU-specific hyperparameters. These values are scaling factors for the backbone and skip features.
```py
pipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
```
The values above are from the official FreeU [code repository](https://github.com/ChenyangSi/FreeU) where you can also find [reference hyperparameters](https://github.com/ChenyangSi/FreeU#range-for-more-parameters) for different models.
<Tip>
Disable the FreeU mechanism by calling `disable_freeu()` on a pipeline.
</Tip>
And then run inference:
```py
prompt = "A squirrel eating a burger"
seed = 2023
image = pipeline(prompt, generator=torch.manual_seed(seed)).images[0]
image
```
The figure below compares non-FreeU and FreeU results respectively for the same hyperparameters used above (`prompt` and `seed`):
Let's see how Stable Diffusion 2 results are impacted:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, safety_checker=None
).to("cuda")
prompt = "A squirrel eating a burger"
seed = 2023
pipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.1, b2=1.2)
image = pipeline(prompt, generator=torch.manual_seed(seed)).images[0]
image
```
## Stable Diffusion XL
Finally, let's take a look at how FreeU affects Stable Diffusion XL results:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16,
).to("cuda")
prompt = "A squirrel eating a burger"
seed = 2023
# Comes from
# https://wandb.ai/nasirk24/UNET-FreeU-SDXL/reports/FreeU-SDXL-Optimal-Parameters--Vmlldzo1NDg4NTUw
pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
image = pipeline(prompt, generator=torch.manual_seed(seed)).images[0]
image
```
## Text-to-video generation
FreeU can also be used to improve video quality:
```python
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video
import torch
model_id = "cerspense/zeroscope_v2_576w"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "an astronaut riding a horse on mars"
seed = 2023
# The values come from
# https://github.com/lyn-rgb/FreeU_Diffusers#video-pipelines
pipe.enable_freeu(b1=1.2, b2=1.4, s1=0.9, s2=0.2)
video_frames = pipe(prompt, height=320, width=576, num_frames=30, generator=torch.manual_seed(seed)).frames
export_to_video(video_frames, "astronaut_rides_horse.mp4")
```
Thanks to [kadirnar](https://github.com/kadirnar/) for helping to integrate the feature, and to [justindujardin](https://github.com/justindujardin) for the helpful discussions.
| diffusers/docs/source/en/using-diffusers/freeu.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/freeu.md",
"repo_id": "diffusers",
"token_count": 1634
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Stable Diffusion XL
[[open-in-colab]]
[Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways:
1. the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number of parameters
2. introduces size and crop-conditioning to preserve training data from being discarded and gain more control over how a generated image should be cropped
3. introduces a two-stage model process; the *base* model (can also be run as a standalone model) generates an image as an input to the *refiner* model which adds additional high-quality details
This guide will show you how to use SDXL for text-to-image, image-to-image, and inpainting.
Before you begin, make sure you have the following libraries installed:
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate invisible-watermark>=0.2.0
```
<Tip warning={true}>
We recommend installing the [invisible-watermark](https://pypi.org/project/invisible-watermark/) library to help identify images that are generated. If the invisible-watermark library is installed, it is used by default. To disable the watermarker:
```py
pipeline = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)
```
</Tip>
## Load model checkpoints
Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~StableDiffusionXLPipeline.from_pretrained`] method:
```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
).to("cuda")
```
You can also use the [`~StableDiffusionXLPipeline.from_single_file`] method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally:
```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
"https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
).to("cuda")
```
## Text-to-image
For text-to-image, pass a text prompt. By default, SDXL generates a 1024x1024 image for the best results. You can try setting the `height` and `width` parameters to 768x768 or 512x512, but anything below 512x512 is not likely to work.
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipeline_text2image(prompt=prompt).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png" alt="generated image of an astronaut in a jungle"/>
</div>
## Image-to-image
For image-to-image, SDXL works especially well with image sizes between 768x768 and 1024x1024. Pass an initial image, and a text prompt to condition the image with:
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image, make_image_grid
# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
init_image = load_image(url)
prompt = "a dog catching a frisbee in the jungle"
image = pipeline(prompt, image=init_image, strength=0.8, guidance_scale=10.5).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-img2img.png" alt="generated image of a dog catching a frisbee in a jungle"/>
</div>
## Inpainting
For inpainting, you'll need the original image and a mask of what you want to replace in the original image. Create a prompt to describe what you want to replace the masked area with.
```py
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid
# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline = AutoPipelineForInpainting.from_pipe(pipeline_text2image).to("cuda")
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png"
init_image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "A deep sea diver floating"
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, guidance_scale=12.5).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint.png" alt="generated image of a deep sea diver in a jungle"/>
</div>
## Refine image quality
SDXL includes a [refiner model](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0) specialized in denoising low-noise stage images to generate higher-quality images from the base model. There are two ways to use the refiner:
1. use the base and refiner models together to produce a refined image
2. use the base model to produce an image, and subsequently use the refiner model to add more details to the image (this is how SDXL was originally trained)
### Base + refiner model
When you use the base and refiner model together to generate an image, this is known as an [*ensemble of expert denoisers*](https://research.nvidia.com/labs/dir/eDiff-I/). The ensemble of expert denoisers approach requires fewer overall denoising steps versus passing the base model's output to the refiner model, so it should be significantly faster to run. However, you won't be able to inspect the base model's output because it still contains a large amount of noise.
As an ensemble of expert denoisers, the base model serves as the expert during the high-noise diffusion stage and the refiner model serves as the expert during the low-noise diffusion stage. Load the base and refiner model:
```py
from diffusers import DiffusionPipeline
import torch
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
```
To use this approach, you need to define the number of timesteps for each model to run through their respective stages. For the base model, this is controlled by the [`denoising_end`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.denoising_end) parameter and for the refiner model, it is controlled by the [`denoising_start`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline.__call__.denoising_start) parameter.
<Tip>
The `denoising_end` and `denoising_start` parameters should be a float between 0 and 1. These parameters are represented as a proportion of discrete timesteps as defined by the scheduler. If you're also using the `strength` parameter, it'll be ignored because the number of denoising steps is determined by the discrete timesteps the model is trained on and the declared fractional cutoff.
</Tip>
Let's set `denoising_end=0.8` so the base model performs the first 80% of denoising the **high-noise** timesteps and set `denoising_start=0.8` so the refiner model performs the last 20% of denoising the **low-noise** timesteps. The base model output should be in **latent** space instead of a PIL image.
```py
prompt = "A majestic lion jumping from a big stone at night"
image = base(
prompt=prompt,
num_inference_steps=40,
denoising_end=0.8,
output_type="latent",
).images
image = refiner(
prompt=prompt,
num_inference_steps=40,
denoising_start=0.8,
image=image,
).images[0]
image
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_base.png" alt="generated image of a lion on a rock at night" />
<figcaption class="mt-2 text-center text-sm text-gray-500">default base model</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_refined.png" alt="generated image of a lion on a rock at night in higher quality" />
<figcaption class="mt-2 text-center text-sm text-gray-500">ensemble of expert denoisers</figcaption>
</div>
</div>
The refiner model can also be used for inpainting in the [`StableDiffusionXLInpaintPipeline`]:
```py
from diffusers import StableDiffusionXLInpaintPipeline
from diffusers.utils import load_image, make_image_grid
import torch
base = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "A majestic tiger sitting on a bench"
num_inference_steps = 75
high_noise_frac = 0.7
image = base(
prompt=prompt,
image=init_image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_end=high_noise_frac,
output_type="latent",
).images
image = refiner(
prompt=prompt,
image=image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_start=high_noise_frac,
).images[0]
make_image_grid([init_image, mask_image, image.resize((512, 512))], rows=1, cols=3)
```
This ensemble of expert denoisers method works well for all available schedulers!
### Base to refiner model
SDXL gets a boost in image quality by using the refiner model to add additional high-quality details to the fully-denoised image from the base model, in an image-to-image setting.
Load the base and refiner models:
```py
from diffusers import DiffusionPipeline
import torch
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
```
Generate an image from the base model, and set the model output to **latent** space:
```py
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = base(prompt=prompt, output_type="latent").images[0]
```
Pass the generated image to the refiner model:
```py
image = refiner(prompt=prompt, image=image[None, :]).images[0]
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/init_image.png" alt="generated image of an astronaut riding a green horse on Mars" />
<figcaption class="mt-2 text-center text-sm text-gray-500">base model</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_image.png" alt="higher quality generated image of an astronaut riding a green horse on Mars" />
<figcaption class="mt-2 text-center text-sm text-gray-500">base model + refiner model</figcaption>
</div>
</div>
For inpainting, load the base and the refiner model in the [`StableDiffusionXLInpaintPipeline`], remove the `denoising_end` and `denoising_start` parameters, and choose a smaller number of inference steps for the refiner.
## Micro-conditioning
SDXL training involves several additional conditioning techniques, which are referred to as *micro-conditioning*. These include original image size, target image size, and cropping parameters. The micro-conditionings can be used at inference time to create high-quality, centered images.
<Tip>
You can use both micro-conditioning and negative micro-conditioning parameters thanks to classifier-free guidance. They are available in the [`StableDiffusionXLPipeline`], [`StableDiffusionXLImg2ImgPipeline`], [`StableDiffusionXLInpaintPipeline`], and [`StableDiffusionXLControlNetPipeline`].
</Tip>
### Size conditioning
There are two types of size conditioning:
- [`original_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.original_size) conditioning comes from upscaled images in the training batch (because it would be wasteful to discard the smaller images which make up almost 40% of the total training data). This way, SDXL learns that upscaling artifacts are not supposed to be present in high-resolution images. During inference, you can use `original_size` to indicate the original image resolution. Using the default value of `(1024, 1024)` produces higher-quality images that resemble the 1024x1024 images in the dataset. If you choose to use a lower resolution, such as `(256, 256)`, the model still generates 1024x1024 images, but they'll look like the low resolution images (simpler patterns, blurring) in the dataset.
- [`target_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.target_size) conditioning comes from finetuning SDXL to support different image aspect ratios. During inference, if you use the default value of `(1024, 1024)`, you'll get an image that resembles the composition of square images in the dataset. We recommend using the same value for `target_size` and `original_size`, but feel free to experiment with other options!
🤗 Diffusers also lets you specify negative conditions about an image's size to steer generation away from certain image resolutions:
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(
prompt=prompt,
negative_original_size=(512, 512),
negative_target_size=(1024, 1024),
).images[0]
```
<div class="flex flex-col justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/negative_conditions.png"/>
<figcaption class="text-center">Images negatively conditioned on image resolutions of (128, 128), (256, 256), and (512, 512).</figcaption>
</div>
### Crop conditioning
Images generated by previous Stable Diffusion models may sometimes appear to be cropped. This is because images are actually cropped during training so that all the images in a batch have the same size. By conditioning on crop coordinates, SDXL *learns* that no cropping - coordinates `(0, 0)` - usually correlates with centered subjects and complete faces (this is the default value in 🤗 Diffusers). You can experiment with different coordinates if you want to generate off-centered compositions!
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipeline(prompt=prompt, crops_coords_top_left=(256, 0)).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-cropped.png" alt="generated image of an astronaut in a jungle, slightly cropped"/>
</div>
You can also specify negative cropping coordinates to steer generation away from certain cropping parameters:
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(
prompt=prompt,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(0, 0),
negative_target_size=(1024, 1024),
).images[0]
image
```
## Use a different prompt for each text-encoder
SDXL uses two text-encoders, so it is possible to pass a different prompt to each text-encoder, which can [improve quality](https://github.com/huggingface/diffusers/issues/4004#issuecomment-1627764201). Pass your original prompt to `prompt` and the second prompt to `prompt_2` (use `negative_prompt` and `negative_prompt_2` if you're using negative prompts):
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
# prompt is passed to OAI CLIP-ViT/L-14
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
# prompt_2 is passed to OpenCLIP-ViT/bigG-14
prompt_2 = "Van Gogh painting"
image = pipeline(prompt=prompt, prompt_2=prompt_2).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-double-prompt.png" alt="generated image of an astronaut in a jungle in the style of a van gogh painting"/>
</div>
The dual text-encoders also support textual inversion embeddings that need to be loaded separately as explained in the [SDXL textual inversion](textual_inversion_inference#stable-diffusion-xl) section.
## Optimizations
SDXL is a large model, and you may need to optimize memory to get it to run on your hardware. Here are some tips to save memory and speed up inference.
1. Offload the model to the CPU with [`~StableDiffusionXLPipeline.enable_model_cpu_offload`] for out-of-memory errors:
```diff
- base.to("cuda")
- refiner.to("cuda")
+ base.enable_model_cpu_offload()
+ refiner.enable_model_cpu_offload()
```
2. Use `torch.compile` for ~20% speed-up (you need `torch>=2.0`):
```diff
+ base.unet = torch.compile(base.unet, mode="reduce-overhead", fullgraph=True)
+ refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
```
3. Enable [xFormers](../optimization/xformers) to run SDXL if `torch<2.0`:
```diff
+ base.enable_xformers_memory_efficient_attention()
+ refiner.enable_xformers_memory_efficient_attention()
```
## Other resources
If you're interested in experimenting with a minimal version of the [`UNet2DConditionModel`] used in SDXL, take a look at the [minSDXL](https://github.com/cloneofsimo/minSDXL) implementation which is written in PyTorch and directly compatible with 🤗 Diffusers.
| diffusers/docs/source/en/using-diffusers/sdxl.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/sdxl.md",
"repo_id": "diffusers",
"token_count": 7004
} | 94 |
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-->
[[open-in-colab]]
# 훑어보기
Diffusion 모델은 이미지나 오디오와 같은 관심 샘플들을 생성하기 위해 랜덤 가우시안 노이즈를 단계별로 제거하도록 학습됩니다. 이로 인해 생성 AI에 대한 관심이 매우 높아졌으며, 인터넷에서 diffusion 생성 이미지의 예를 본 적이 있을 것입니다. 🧨 Diffusers는 누구나 diffusion 모델들을 널리 이용할 수 있도록 하기 위한 라이브러리입니다.
개발자든 일반 사용자든 이 훑어보기를 통해 🧨 diffusers를 소개하고 빠르게 생성할 수 있도록 도와드립니다! 알아야 할 라이브러리의 주요 구성 요소는 크게 세 가지입니다:
* [`DiffusionPipeline`]은 추론을 위해 사전 학습된 diffusion 모델에서 샘플을 빠르게 생성하도록 설계된 높은 수준의 엔드투엔드 클래스입니다.
* Diffusion 시스템 생성을 위한 빌딩 블록으로 사용할 수 있는 널리 사용되는 사전 학습된 [model](./api/models) 아키텍처 및 모듈.
* 다양한 [schedulers](./api/schedulers/overview) - 학습을 위해 노이즈를 추가하는 방법과 추론 중에 노이즈 제거된 이미지를 생성하는 방법을 제어하는 알고리즘입니다.
훑어보기에서는 추론을 위해 [`DiffusionPipeline`]을 사용하는 방법을 보여준 다음, 모델과 스케줄러를 결합하여 [`DiffusionPipeline`] 내부에서 일어나는 일을 복제하는 방법을 안내합니다.
<Tip>
훑어보기는 간결한 버전의 🧨 Diffusers 소개로서 [노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) 빠르게 시작할 수 있도록 도와드립니다. 디퓨저의 목표, 디자인 철학, 핵심 API에 대한 추가 세부 정보를 자세히 알아보려면 노트북을 확인하세요!
</Tip>
시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
```py
# 주석 풀어서 Colab에 필요한 라이브러리 설치하기.
#!pip install --upgrade diffusers accelerate transformers
```
- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index)는 추론 및 학습을 위한 모델 로딩 속도를 높여줍니다.
- [🤗 Transformers](https://huggingface.co/docs/transformers/index)는 [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview)과 같이 가장 많이 사용되는 diffusion 모델을 실행하는 데 필요합니다.
## DiffusionPipeline
[`DiffusionPipeline`] 은 추론을 위해 사전 학습된 diffusion 시스템을 사용하는 가장 쉬운 방법입니다. 모델과 스케줄러를 포함하는 엔드 투 엔드 시스템입니다. 다양한 작업에 [`DiffusionPipeline`]을 바로 사용할 수 있습니다. 아래 표에서 지원되는 몇 가지 작업을 살펴보고, 지원되는 작업의 전체 목록은 [🧨 Diffusers Summary](./api/pipelines/overview#diffusers-summary) 표에서 확인할 수 있습니다.
| **Task** | **Description** | **Pipeline**
|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|
| Unconditional Image Generation | generate an image from Gaussian noise | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |
| Text-Guided Image Generation | generate an image given a text prompt | [conditional_image_generation](./using-diffusers/conditional_image_generation) |
| Text-Guided Image-to-Image Translation | adapt an image guided by a text prompt | [img2img](./using-diffusers/img2img) |
| Text-Guided Image-Inpainting | fill the masked part of an image given the image, the mask and a text prompt | [inpaint](./using-diffusers/inpaint) |
| Text-Guided Depth-to-Image Translation | adapt parts of an image guided by a text prompt while preserving structure via depth estimation | [depth2img](./using-diffusers/depth2img) |
먼저 [`DiffusionPipeline`]의 인스턴스를 생성하고 다운로드할 파이프라인 체크포인트를 지정합니다.
허깅페이스 허브에 저장된 모든 [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads)에 대해 [`DiffusionPipeline`]을 사용할 수 있습니다.
이 훑어보기에서는 text-to-image 생성을 위한 [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 체크포인트를 로드합니다.
<Tip warning={true}>
[Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) 모델의 경우, 모델을 실행하기 전에 [라이선스](https://huggingface.co/spaces/CompVis/stable-diffusion-license)를 먼저 주의 깊게 읽어주세요. 🧨 Diffusers는 불쾌하거나 유해한 콘텐츠를 방지하기 위해 [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py)를 구현하고 있지만, 모델의 향상된 이미지 생성 기능으로 인해 여전히 잠재적으로 유해한 콘텐츠가 생성될 수 있습니다.
</Tip>
[`~DiffusionPipeline.from_pretrained`] 방법으로 모델 로드하기:
```python
>>> from diffusers import DiffusionPipeline
>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
```
The [`DiffusionPipeline`]은 모든 모델링, 토큰화, 스케줄링 컴포넌트를 다운로드하고 캐시합니다. Stable Diffusion Pipeline은 무엇보다도 [`UNet2DConditionModel`]과 [`PNDMScheduler`]로 구성되어 있음을 알 수 있습니다:
```py
>>> pipeline
StableDiffusionPipeline {
"_class_name": "StableDiffusionPipeline",
"_diffusers_version": "0.13.1",
...,
"scheduler": [
"diffusers",
"PNDMScheduler"
],
...,
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
```
이 모델은 약 14억 개의 파라미터로 구성되어 있으므로 GPU에서 파이프라인을 실행할 것을 강력히 권장합니다.
PyTorch에서와 마찬가지로 제너레이터 객체를 GPU로 이동할 수 있습니다:
```python
>>> pipeline.to("cuda")
```
이제 `파이프라인`에 텍스트 프롬프트를 전달하여 이미지를 생성한 다음 노이즈가 제거된 이미지에 액세스할 수 있습니다. 기본적으로 이미지 출력은 [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) 객체로 감싸집니다.
```python
>>> image = pipeline("An image of a squirrel in Picasso style").images[0]
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_of_squirrel_painting.png"/>
</div>
`save`를 호출하여 이미지를 저장합니다:
```python
>>> image.save("image_of_squirrel_painting.png")
```
### 로컬 파이프라인
파이프라인을 로컬에서 사용할 수도 있습니다. 유일한 차이점은 가중치를 먼저 다운로드해야 한다는 점입니다:
```bash
!git lfs install
!git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
```
그런 다음 저장된 가중치를 파이프라인에 로드합니다:
```python
>>> pipeline = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
```
이제 위 섹션에서와 같이 파이프라인을 실행할 수 있습니다.
### 스케줄러 교체
스케줄러마다 노이즈 제거 속도와 품질이 서로 다릅니다. 자신에게 가장 적합한 스케줄러를 찾는 가장 좋은 방법은 직접 사용해 보는 것입니다! 🧨 Diffusers의 주요 기능 중 하나는 스케줄러 간에 쉽게 전환이 가능하다는 것입니다. 예를 들어, 기본 스케줄러인 [`PNDMScheduler`]를 [`EulerDiscreteScheduler`]로 바꾸려면, [`~diffusers.ConfigMixin.from_config`] 메서드를 사용하여 로드하세요:
```py
>>> from diffusers import EulerDiscreteScheduler
>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
```
새 스케줄러로 이미지를 생성해보고 어떤 차이가 있는지 확인해 보세요!
다음 섹션에서는 모델과 스케줄러라는 [`DiffusionPipeline`]을 구성하는 컴포넌트를 자세히 살펴보고 이러한 컴포넌트를 사용하여 고양이 이미지를 생성하는 방법을 배워보겠습니다.
## 모델
대부분의 모델은 노이즈가 있는 샘플을 가져와 각 시간 간격마다 노이즈가 적은 이미지와 입력 이미지 사이의 차이인 *노이즈 잔차*(다른 모델은 이전 샘플을 직접 예측하거나 속도 또는 [`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)을 예측하는 학습을 합니다)을 예측합니다. 모델을 믹스 앤 매치하여 다른 diffusion 시스템을 만들 수 있습니다.
모델은 [`~ModelMixin.from_pretrained`] 메서드로 시작되며, 이 메서드는 모델 가중치를 로컬에 캐시하여 다음에 모델을 로드할 때 더 빠르게 로드할 수 있습니다. 훑어보기에서는 고양이 이미지에 대해 학습된 체크포인트가 있는 기본적인 unconditional 이미지 생성 모델인 [`UNet2DModel`]을 로드합니다:
```py
>>> from diffusers import UNet2DModel
>>> repo_id = "google/ddpm-cat-256"
>>> model = UNet2DModel.from_pretrained(repo_id)
```
모델 매개변수에 액세스하려면 `model.config`를 호출합니다:
```py
>>> model.config
```
모델 구성은 🧊 고정된 🧊 딕셔너리로, 모델이 생성된 후에는 해당 매개 변수들을 변경할 수 없습니다. 이는 의도적인 것으로, 처음에 모델 아키텍처를 정의하는 데 사용된 매개변수는 동일하게 유지하면서 다른 매개변수는 추론 중에 조정할 수 있도록 하기 위한 것입니다.
가장 중요한 매개변수들은 다음과 같습니다:
* `sample_size`: 입력 샘플의 높이 및 너비 치수입니다.
* `in_channels`: 입력 샘플의 입력 채널 수입니다.
* `down_block_types` 및 `up_block_types`: UNet 아키텍처를 생성하는 데 사용되는 다운 및 업샘플링 블록의 유형.
* `block_out_channels`: 다운샘플링 블록의 출력 채널 수. 업샘플링 블록의 입력 채널 수에 역순으로 사용되기도 합니다.
* `layers_per_block`: 각 UNet 블록에 존재하는 ResNet 블록의 수입니다.
추론에 모델을 사용하려면 랜덤 가우시안 노이즈로 이미지 모양을 만듭니다. 모델이 여러 개의 무작위 노이즈를 수신할 수 있으므로 'batch' 축, 입력 채널 수에 해당하는 'channel' 축, 이미지의 높이와 너비를 나타내는 'sample_size' 축이 있어야 합니다:
```py
>>> import torch
>>> torch.manual_seed(0)
>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
>>> noisy_sample.shape
torch.Size([1, 3, 256, 256])
```
추론을 위해 모델에 노이즈가 있는 이미지와 `timestep`을 전달합니다. 'timestep'은 입력 이미지의 노이즈 정도를 나타내며, 시작 부분에 더 많은 노이즈가 있고 끝 부분에 더 적은 노이즈가 있습니다. 이를 통해 모델이 diffusion 과정에서 시작 또는 끝에 더 가까운 위치를 결정할 수 있습니다. `sample` 메서드를 사용하여 모델 출력을 얻습니다:
```py
>>> with torch.no_grad():
... noisy_residual = model(sample=noisy_sample, timestep=2).sample
```
하지만 실제 예를 생성하려면 노이즈 제거 프로세스를 안내할 스케줄러가 필요합니다. 다음 섹션에서는 모델을 스케줄러와 결합하는 방법에 대해 알아봅니다.
## 스케줄러
스케줄러는 모델 출력이 주어졌을 때 노이즈가 많은 샘플에서 노이즈가 적은 샘플로 전환하는 것을 관리합니다 - 이 경우 'noisy_residual'.
<Tip>
🧨 Diffusers는 Diffusion 시스템을 구축하기 위한 툴박스입니다. [`DiffusionPipeline`]을 사용하면 미리 만들어진 Diffusion 시스템을 편리하게 시작할 수 있지만, 모델과 스케줄러 구성 요소를 개별적으로 선택하여 사용자 지정 Diffusion 시스템을 구축할 수도 있습니다.
</Tip>
훑어보기의 경우, [`~diffusers.ConfigMixin.from_config`] 메서드를 사용하여 [`DDPMScheduler`]를 인스턴스화합니다:
```py
>>> from diffusers import DDPMScheduler
>>> scheduler = DDPMScheduler.from_config(repo_id)
>>> scheduler
DDPMScheduler {
"_class_name": "DDPMScheduler",
"_diffusers_version": "0.13.1",
"beta_end": 0.02,
"beta_schedule": "linear",
"beta_start": 0.0001,
"clip_sample": true,
"clip_sample_range": 1.0,
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"trained_betas": null,
"variance_type": "fixed_small"
}
```
<Tip>
💡 스케줄러가 구성에서 어떻게 인스턴스화되는지 주목하세요. 모델과 달리 스케줄러에는 학습 가능한 가중치가 없으며 매개변수도 없습니다!
</Tip>
가장 중요한 매개변수는 다음과 같습니다:
* `num_train_timesteps`: 노이즈 제거 프로세스의 길이, 즉 랜덤 가우스 노이즈를 데이터 샘플로 처리하는 데 필요한 타임스텝 수입니다.
* `beta_schedule`: 추론 및 학습에 사용할 노이즈 스케줄 유형입니다.
* `beta_start` 및 `beta_end`: 노이즈 스케줄의 시작 및 종료 노이즈 값입니다.
노이즈가 약간 적은 이미지를 예측하려면 스케줄러의 [`~diffusers.DDPMScheduler.step`] 메서드에 모델 출력, `timestep`, 현재 `sample`을 전달하세요.
```py
>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample
>>> less_noisy_sample.shape
```
`less_noisy_sample`을 다음 `timestep`으로 넘기면 노이즈가 더 줄어듭니다! 이제 이 모든 것을 한데 모아 전체 노이즈 제거 과정을 시각화해 보겠습니다.
먼저 노이즈 제거된 이미지를 후처리하여 `PIL.Image`로 표시하는 함수를 만듭니다:
```py
>>> import PIL.Image
>>> import numpy as np
>>> def display_sample(sample, i):
... image_processed = sample.cpu().permute(0, 2, 3, 1)
... image_processed = (image_processed + 1.0) * 127.5
... image_processed = image_processed.numpy().astype(np.uint8)
... image_pil = PIL.Image.fromarray(image_processed[0])
... display(f"Image at step {i}")
... display(image_pil)
```
노이즈 제거 프로세스의 속도를 높이려면 입력과 모델을 GPU로 옮기세요:
```py
>>> model.to("cuda")
>>> noisy_sample = noisy_sample.to("cuda")
```
이제 노이즈가 적은 샘플의 잔차를 예측하고 스케줄러로 노이즈가 적은 샘플을 계산하는 노이즈 제거 루프를 생성합니다:
```py
>>> import tqdm
>>> sample = noisy_sample
>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
... # 1. predict noise residual
... with torch.no_grad():
... residual = model(sample, t).sample
... # 2. compute less noisy image and set x_t -> x_t-1
... sample = scheduler.step(residual, t, sample).prev_sample
... # 3. optionally look at image
... if (i + 1) % 50 == 0:
... display_sample(sample, i + 1)
```
가만히 앉아서 고양이가 소음으로만 생성되는 것을 지켜보세요!😻
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/diffusion-quicktour.png"/>
</div>
## 다음 단계
이번 훑어보기에서 🧨 Diffusers로 멋진 이미지를 만들어 보셨기를 바랍니다! 다음 단계로 넘어가세요:
* [training](./tutorials/basic_training) 튜토리얼에서 모델을 학습하거나 파인튜닝하여 나만의 이미지를 생성할 수 있습니다.
* 다양한 사용 사례는 공식 및 커뮤니티 [학습 또는 파인튜닝 스크립트](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) 예시를 참조하세요.
* 스케줄러 로드, 액세스, 변경 및 비교에 대한 자세한 내용은 [다른 스케줄러 사용](./using-diffusers/schedulers) 가이드에서 확인하세요.
* [Stable Diffusion](./stable_diffusion) 가이드에서 프롬프트 엔지니어링, 속도 및 메모리 최적화, 고품질 이미지 생성을 위한 팁과 요령을 살펴보세요.
* [GPU에서 파이토치 최적화](./optimization/fp16) 가이드와 [애플 실리콘(M1/M2)에서의 Stable Diffusion](./optimization/mps) 및 [ONNX 런타임](./optimization/onnx) 실행에 대한 추론 가이드를 통해 🧨 Diffuser 속도를 높이는 방법을 더 자세히 알아보세요. | diffusers/docs/source/ko/quicktour.md/0 | {
"file_path": "diffusers/docs/source/ko/quicktour.md",
"repo_id": "diffusers",
"token_count": 11430
} | 95 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# 조건부 이미지 생성
[[open-in-colab]]
조건부 이미지 생성을 사용하면 텍스트 프롬프트에서 이미지를 생성할 수 있습니다. 텍스트는 임베딩으로 변환되며, 임베딩은 노이즈에서 이미지를 생성하도록 모델을 조건화하는 데 사용됩니다.
[`DiffusionPipeline`]은 추론을 위해 사전 훈련된 diffusion 시스템을 사용하는 가장 쉬운 방법입니다.
먼저 [`DiffusionPipeline`]의 인스턴스를 생성하고 다운로드할 파이프라인 [체크포인트](https://huggingface.co/models?library=diffusers&sort=downloads)를 지정합니다.
이 가이드에서는 [잠재 Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256)과 함께 텍스트-이미지 생성에 [`DiffusionPipeline`]을 사용합니다:
```python
>>> from diffusers import DiffusionPipeline
>>> generator = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
```
[`DiffusionPipeline`]은 모든 모델링, 토큰화, 스케줄링 구성 요소를 다운로드하고 캐시합니다.
이 모델은 약 14억 개의 파라미터로 구성되어 있기 때문에 GPU에서 실행할 것을 강력히 권장합니다.
PyTorch에서와 마찬가지로 생성기 객체를 GPU로 이동할 수 있습니다:
```python
>>> generator.to("cuda")
```
이제 텍스트 프롬프트에서 `생성기`를 사용할 수 있습니다:
```python
>>> image = generator("An image of a squirrel in Picasso style").images[0]
```
출력값은 기본적으로 [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) 객체로 래핑됩니다.
호출하여 이미지를 저장할 수 있습니다:
```python
>>> image.save("image_of_squirrel_painting.png")
```
아래 스페이스를 사용해보고 안내 배율 매개변수를 자유롭게 조정하여 이미지 품질에 어떤 영향을 미치는지 확인해 보세요!
<iframe
src="https://stabilityai-stable-diffusion.hf.space"
frameborder="0"
width="850"
height="500"
></iframe> | diffusers/docs/source/ko/using-diffusers/conditional_image_generation.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/conditional_image_generation.md",
"repo_id": "diffusers",
"token_count": 1552
} | 96 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# JAX / Flax에서의 🧨 Stable Diffusion!
[[open-in-colab]]
🤗 Hugging Face [Diffusers] (https://github.com/huggingface/diffusers) 는 버전 0.5.1부터 Flax를 지원합니다! 이를 통해 Colab, Kaggle, Google Cloud Platform에서 사용할 수 있는 것처럼 Google TPU에서 초고속 추론이 가능합니다.
이 노트북은 JAX / Flax를 사용해 추론을 실행하는 방법을 보여줍니다. Stable Diffusion의 작동 방식에 대한 자세한 내용을 원하거나 GPU에서 실행하려면 이 [노트북] ](https://huggingface.co/docs/diffusers/stable_diffusion)을 참조하세요.
먼저, TPU 백엔드를 사용하고 있는지 확인합니다. Colab에서 이 노트북을 실행하는 경우, 메뉴에서 런타임을 선택한 다음 "런타임 유형 변경" 옵션을 선택한 다음 하드웨어 가속기 설정에서 TPU를 선택합니다.
JAX는 TPU 전용은 아니지만 각 TPU 서버에는 8개의 TPU 가속기가 병렬로 작동하기 때문에 해당 하드웨어에서 더 빛을 발한다는 점은 알아두세요.
## Setup
먼저 diffusers가 설치되어 있는지 확인합니다.
```bash
!pip install jax==0.3.25 jaxlib==0.3.25 flax transformers ftfy
!pip install diffusers
```
```python
import jax.tools.colab_tpu
jax.tools.colab_tpu.setup_tpu()
import jax
```
```python
num_devices = jax.device_count()
device_type = jax.devices()[0].device_kind
print(f"Found {num_devices} JAX devices of type {device_type}.")
assert (
"TPU" in device_type
), "Available device is not a TPU, please select TPU from Edit > Notebook settings > Hardware accelerator"
```
```python out
Found 8 JAX devices of type Cloud TPU.
```
그런 다음 모든 dependencies를 가져옵니다.
```python
import numpy as np
import jax
import jax.numpy as jnp
from pathlib import Path
from jax import pmap
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from PIL import Image
from huggingface_hub import notebook_login
from diffusers import FlaxStableDiffusionPipeline
```
## 모델 불러오기
TPU 장치는 효율적인 half-float 유형인 bfloat16을 지원합니다. 테스트에는 이 유형을 사용하지만 대신 float32를 사용하여 전체 정밀도(full precision)를 사용할 수도 있습니다.
```python
dtype = jnp.bfloat16
```
Flax는 함수형 프레임워크이므로 모델은 무상태(stateless)형이며 매개변수는 모델 외부에 저장됩니다. 사전학습된 Flax 파이프라인을 불러오면 파이프라인 자체와 모델 가중치(또는 매개변수)가 모두 반환됩니다. 저희는 bf16 버전의 가중치를 사용하고 있으므로 유형 경고가 표시되지만 무시해도 됩니다.
```python
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
revision="bf16",
dtype=dtype,
)
```
## 추론
TPU에는 일반적으로 8개의 디바이스가 병렬로 작동하므로 보유한 디바이스 수만큼 프롬프트를 복제합니다. 그런 다음 각각 하나의 이미지 생성을 담당하는 8개의 디바이스에서 한 번에 추론을 수행합니다. 따라서 하나의 칩이 하나의 이미지를 생성하는 데 걸리는 시간과 동일한 시간에 8개의 이미지를 얻을 수 있습니다.
프롬프트를 복제하고 나면 파이프라인의 `prepare_inputs` 함수를 호출하여 토큰화된 텍스트 ID를 얻습니다. 토큰화된 텍스트의 길이는 기본 CLIP 텍스트 모델의 구성에 따라 77토큰으로 설정됩니다.
```python
prompt = "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic"
prompt = [prompt] * jax.device_count()
prompt_ids = pipeline.prepare_inputs(prompt)
prompt_ids.shape
```
```python out
(8, 77)
```
### 복사(Replication) 및 정렬화
모델 매개변수와 입력값은 우리가 보유한 8개의 병렬 장치에 복사(Replication)되어야 합니다. 매개변수 딕셔너리는 `flax.jax_utils.replicate`(딕셔너리를 순회하며 가중치의 모양을 변경하여 8번 반복하는 함수)를 사용하여 복사됩니다. 배열은 `shard`를 사용하여 복제됩니다.
```python
p_params = replicate(params)
```
```python
prompt_ids = shard(prompt_ids)
prompt_ids.shape
```
```python out
(8, 1, 77)
```
이 shape은 8개의 디바이스 각각이 shape `(1, 77)`의 jnp 배열을 입력값으로 받는다는 의미입니다. 즉 1은 디바이스당 batch(배치) 크기입니다. 메모리가 충분한 TPU에서는 한 번에 여러 이미지(칩당)를 생성하려는 경우 1보다 클 수 있습니다.
이미지를 생성할 준비가 거의 완료되었습니다! 이제 생성 함수에 전달할 난수 생성기만 만들면 됩니다. 이것은 난수를 다루는 모든 함수에 난수 생성기가 있어야 한다는, 난수에 대해 매우 진지하고 독단적인 Flax의 표준 절차입니다. 이렇게 하면 여러 분산된 기기에서 훈련할 때에도 재현성이 보장됩니다.
아래 헬퍼 함수는 시드를 사용하여 난수 생성기를 초기화합니다. 동일한 시드를 사용하는 한 정확히 동일한 결과를 얻을 수 있습니다. 나중에 노트북에서 결과를 탐색할 때엔 다른 시드를 자유롭게 사용하세요.
```python
def create_key(seed=0):
return jax.random.PRNGKey(seed)
```
rng를 얻은 다음 8번 '분할'하여 각 디바이스가 다른 제너레이터를 수신하도록 합니다. 따라서 각 디바이스마다 다른 이미지가 생성되며 전체 프로세스를 재현할 수 있습니다.
```python
rng = create_key(0)
rng = jax.random.split(rng, jax.device_count())
```
JAX 코드는 매우 빠르게 실행되는 효율적인 표현으로 컴파일할 수 있습니다. 하지만 후속 호출에서 모든 입력이 동일한 모양을 갖도록 해야 하며, 그렇지 않으면 JAX가 코드를 다시 컴파일해야 하므로 최적화된 속도를 활용할 수 없습니다.
`jit = True`를 인수로 전달하면 Flax 파이프라인이 코드를 컴파일할 수 있습니다. 또한 모델이 사용 가능한 8개의 디바이스에서 병렬로 실행되도록 보장합니다.
다음 셀을 처음 실행하면 컴파일하는 데 시간이 오래 걸리지만 이후 호출(입력이 다른 경우에도)은 훨씬 빨라집니다. 예를 들어, 테스트했을 때 TPU v2-8에서 컴파일하는 데 1분 이상 걸리지만 이후 추론 실행에는 약 7초가 걸립니다.
```
%%time
images = pipeline(prompt_ids, p_params, rng, jit=True)[0]
```
```python out
CPU times: user 56.2 s, sys: 42.5 s, total: 1min 38s
Wall time: 1min 29s
```
반환된 배열의 shape은 `(8, 1, 512, 512, 3)`입니다. 이를 재구성하여 두 번째 차원을 제거하고 512 × 512 × 3의 이미지 8개를 얻은 다음 PIL로 변환합니다.
```python
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
images = pipeline.numpy_to_pil(images)
```
### 시각화
이미지를 그리드에 표시하는 도우미 함수를 만들어 보겠습니다.
```python
def image_grid(imgs, rows, cols):
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
```
```python
image_grid(images, 2, 4)
```
## 다른 프롬프트 사용
모든 디바이스에서 동일한 프롬프트를 복제할 필요는 없습니다. 프롬프트 2개를 각각 4번씩 생성하거나 한 번에 8개의 서로 다른 프롬프트를 생성하는 등 원하는 것은 무엇이든 할 수 있습니다. 한번 해보세요!
먼저 입력 준비 코드를 편리한 함수로 리팩터링하겠습니다:
```python
prompts = [
"Labrador in the style of Hokusai",
"Painting of a squirrel skating in New York",
"HAL-9000 in the style of Van Gogh",
"Times Square under water, with fish and a dolphin swimming around",
"Ancient Roman fresco showing a man working on his laptop",
"Close-up photograph of young black woman against urban background, high quality, bokeh",
"Armchair in the shape of an avocado",
"Clown astronaut in space, with Earth in the background",
]
```
```python
prompt_ids = pipeline.prepare_inputs(prompts)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, p_params, rng, jit=True).images
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
images = pipeline.numpy_to_pil(images)
image_grid(images, 2, 4)
```
## 병렬화(parallelization)는 어떻게 작동하는가?
앞서 `diffusers` Flax 파이프라인이 모델을 자동으로 컴파일하고 사용 가능한 모든 기기에서 병렬로 실행한다고 말씀드렸습니다. 이제 그 프로세스를 간략하게 살펴보고 작동 방식을 보여드리겠습니다.
JAX 병렬화는 여러 가지 방법으로 수행할 수 있습니다. 가장 쉬운 방법은 jax.pmap 함수를 사용하여 단일 프로그램, 다중 데이터(SPMD) 병렬화를 달성하는 것입니다. 즉, 동일한 코드의 복사본을 각각 다른 데이터 입력에 대해 여러 개 실행하는 것입니다. 더 정교한 접근 방식도 가능하므로 관심이 있으시다면 [JAX 문서](https://jax.readthedocs.io/en/latest/index.html)와 [`pjit` 페이지](https://jax.readthedocs.io/en/latest/jax-101/08-pjit.html?highlight=pjit)에서 이 주제를 살펴보시기 바랍니다!
`jax.pmap`은 두 가지 기능을 수행합니다:
- `jax.jit()`를 호출한 것처럼 코드를 컴파일(또는 `jit`)합니다. 이 작업은 `pmap`을 호출할 때가 아니라 pmapped 함수가 처음 호출될 때 수행됩니다.
- 컴파일된 코드가 사용 가능한 모든 기기에서 병렬로 실행되도록 합니다.
작동 방식을 보여드리기 위해 이미지 생성을 실행하는 비공개 메서드인 파이프라인의 `_generate` 메서드를 `pmap`합니다. 이 메서드는 향후 `Diffusers` 릴리스에서 이름이 변경되거나 제거될 수 있다는 점에 유의하세요.
```python
p_generate = pmap(pipeline._generate)
```
`pmap`을 사용한 후 준비된 함수 `p_generate`는 개념적으로 다음을 수행합니다:
* 각 장치에서 기본 함수 `pipeline._generate`의 복사본을 호출합니다.
* 각 장치에 입력 인수의 다른 부분을 보냅니다. 이것이 바로 샤딩이 사용되는 이유입니다. 이 경우 `prompt_ids`의 shape은 `(8, 1, 77, 768)`입니다. 이 배열은 8개로 분할되고 `_generate`의 각 복사본은 `(1, 77, 768)`의 shape을 가진 입력을 받게 됩니다.
병렬로 호출된다는 사실을 완전히 무시하고 `_generate`를 코딩할 수 있습니다. batch(배치) 크기(이 예제에서는 `1`)와 코드에 적합한 차원만 신경 쓰면 되며, 병렬로 작동하기 위해 아무것도 변경할 필요가 없습니다.
파이프라인 호출을 사용할 때와 마찬가지로, 다음 셀을 처음 실행할 때는 시간이 걸리지만 그 이후에는 훨씬 빨라집니다.
```
%%time
images = p_generate(prompt_ids, p_params, rng)
images = images.block_until_ready()
images.shape
```
```python out
CPU times: user 1min 15s, sys: 18.2 s, total: 1min 34s
Wall time: 1min 15s
```
```python
images.shape
```
```python out
(8, 1, 512, 512, 3)
```
JAX는 비동기 디스패치를 사용하고 가능한 한 빨리 제어권을 Python 루프에 반환하기 때문에 추론 시간을 정확하게 측정하기 위해 `block_until_ready()`를 사용합니다. 아직 구체화되지 않은 계산 결과를 사용하려는 경우 자동으로 차단이 수행되므로 코드에서 이 함수를 사용할 필요가 없습니다. | diffusers/docs/source/ko/using-diffusers/stable_diffusion_jax_how_to.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/stable_diffusion_jax_how_to.md",
"repo_id": "diffusers",
"token_count": 8475
} | 97 |
"""
modeled after the textual_inversion.py / train_dreambooth.py and the work
of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb
"""
import inspect
import warnings
from typing import List, Optional, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from accelerate import Accelerator
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from tqdm.auto import tqdm
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def preprocess(image):
w, h = image.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
class ImagicStableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for imagic image editing.
See paper here: https://arxiv.org/pdf/2210.09276.pdf
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offsensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def train(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image],
height: Optional[int] = 512,
width: Optional[int] = 512,
generator: Optional[torch.Generator] = None,
embedding_learning_rate: float = 0.001,
diffusion_model_learning_rate: float = 2e-6,
text_embedding_optimization_steps: int = 500,
model_fine_tuning_optimization_steps: int = 1000,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
accelerator = Accelerator(
gradient_accumulation_steps=1,
mixed_precision="fp16",
)
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# Freeze vae and unet
self.vae.requires_grad_(False)
self.unet.requires_grad_(False)
self.text_encoder.requires_grad_(False)
self.unet.eval()
self.vae.eval()
self.text_encoder.eval()
if accelerator.is_main_process:
accelerator.init_trackers(
"imagic",
config={
"embedding_learning_rate": embedding_learning_rate,
"text_embedding_optimization_steps": text_embedding_optimization_steps,
},
)
# get text embeddings for prompt
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = torch.nn.Parameter(
self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True
)
text_embeddings = text_embeddings.detach()
text_embeddings.requires_grad_()
text_embeddings_orig = text_embeddings.clone()
# Initialize the optimizer
optimizer = torch.optim.Adam(
[text_embeddings], # only optimize the embeddings
lr=embedding_learning_rate,
)
if isinstance(image, PIL.Image.Image):
image = preprocess(image)
latents_dtype = text_embeddings.dtype
image = image.to(device=self.device, dtype=latents_dtype)
init_latent_image_dist = self.vae.encode(image).latent_dist
image_latents = init_latent_image_dist.sample(generator=generator)
image_latents = 0.18215 * image_latents
progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
logger.info("First optimizing the text embedding to better reconstruct the init image")
for _ in range(text_embedding_optimization_steps):
with accelerator.accumulate(text_embeddings):
# Sample noise that we'll add to the latents
noise = torch.randn(image_latents.shape).to(image_latents.device)
timesteps = torch.randint(1000, (1,), device=image_latents.device)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
accelerator.wait_for_everyone()
text_embeddings.requires_grad_(False)
# Now we fine tune the unet to better reconstruct the image
self.unet.requires_grad_(True)
self.unet.train()
optimizer = torch.optim.Adam(
self.unet.parameters(), # only optimize unet
lr=diffusion_model_learning_rate,
)
progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process)
logger.info("Next fine tuning the entire model to better reconstruct the init image")
for _ in range(model_fine_tuning_optimization_steps):
with accelerator.accumulate(self.unet.parameters()):
# Sample noise that we'll add to the latents
noise = torch.randn(image_latents.shape).to(image_latents.device)
timesteps = torch.randint(1000, (1,), device=image_latents.device)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
accelerator.wait_for_everyone()
self.text_embeddings_orig = text_embeddings_orig
self.text_embeddings = text_embeddings
@torch.no_grad()
def __call__(
self,
alpha: float = 1.2,
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
guidance_scale: float = 7.5,
eta: float = 0.0,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if self.text_embeddings is None:
raise ValueError("Please run the pipe.train() before trying to generate an image.")
if self.text_embeddings_orig is None:
raise ValueError("Please run the pipe.train() before trying to generate an image.")
text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens = [""]
max_length = self.tokenizer.model_max_length
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.view(1, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
latents_shape = (1, self.unet.config.in_channels, height // 8, width // 8)
latents_dtype = text_embeddings.dtype
if self.device.type == "mps":
# randn does not exist on mps
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
self.device
)
else:
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
self.device
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers/examples/community/imagic_stable_diffusion.py/0 | {
"file_path": "diffusers/examples/community/imagic_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 10425
} | 98 |
import inspect
from copy import deepcopy
from enum import Enum
from typing import List, Optional, Tuple, Union
import torch
from tqdm.auto import tqdm
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
try:
from ligo.segments import segment
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
except ImportError:
raise ImportError("Please install transformers and ligo-segments to use the mixture pipeline")
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import LMSDiscreteScheduler, DiffusionPipeline
>>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
>>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler, custom_pipeline="mixture_tiling")
>>> pipeline.to("cuda")
>>> image = pipeline(
>>> prompt=[[
>>> "A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
>>> "A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
>>> "An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece"
>>> ]],
>>> tile_height=640,
>>> tile_width=640,
>>> tile_row_overlap=0,
>>> tile_col_overlap=256,
>>> guidance_scale=8,
>>> seed=7178915308,
>>> num_inference_steps=50,
>>> )["images"][0]
```
"""
def _tile2pixel_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
"""Given a tile row and column numbers returns the range of pixels affected by that tiles in the overall image
Returns a tuple with:
- Starting coordinates of rows in pixel space
- Ending coordinates of rows in pixel space
- Starting coordinates of columns in pixel space
- Ending coordinates of columns in pixel space
"""
px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap)
px_row_end = px_row_init + tile_height
px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap)
px_col_end = px_col_init + tile_width
return px_row_init, px_row_end, px_col_init, px_col_end
def _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end):
"""Translates coordinates in pixel space to coordinates in latent space"""
return px_row_init // 8, px_row_end // 8, px_col_init // 8, px_col_end // 8
def _tile2latent_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
"""Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image
Returns a tuple with:
- Starting coordinates of rows in latent space
- Ending coordinates of rows in latent space
- Starting coordinates of columns in latent space
- Ending coordinates of columns in latent space
"""
px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
)
return _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end)
def _tile2latent_exclusive_indices(
tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, rows, columns
):
"""Given a tile row and column numbers returns the range of latents affected only by that tile in the overall image
Returns a tuple with:
- Starting coordinates of rows in latent space
- Ending coordinates of rows in latent space
- Starting coordinates of columns in latent space
- Ending coordinates of columns in latent space
"""
row_init, row_end, col_init, col_end = _tile2latent_indices(
tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
)
row_segment = segment(row_init, row_end)
col_segment = segment(col_init, col_end)
# Iterate over the rest of tiles, clipping the region for the current tile
for row in range(rows):
for column in range(columns):
if row != tile_row and column != tile_col:
clip_row_init, clip_row_end, clip_col_init, clip_col_end = _tile2latent_indices(
row, column, tile_width, tile_height, tile_row_overlap, tile_col_overlap
)
row_segment = row_segment - segment(clip_row_init, clip_row_end)
col_segment = col_segment - segment(clip_col_init, clip_col_end)
# return row_init, row_end, col_init, col_end
return row_segment[0], row_segment[1], col_segment[0], col_segment[1]
class StableDiffusionExtrasMixin:
"""Mixin providing additional convenience method to Stable Diffusion pipelines"""
def decode_latents(self, latents, cpu_vae=False):
"""Decodes a given array of latents into pixel space"""
# scale and decode the image latents with vae
if cpu_vae:
lat = deepcopy(latents).cpu()
vae = deepcopy(self.vae).cpu()
else:
lat = latents
vae = self.vae
lat = 1 / 0.18215 * lat
image = vae.decode(lat).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
return self.numpy_to_pil(image)
class StableDiffusionTilingPipeline(DiffusionPipeline, StableDiffusionExtrasMixin):
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
class SeedTilesMode(Enum):
"""Modes in which the latents of a particular tile can be re-seeded"""
FULL = "full"
EXCLUSIVE = "exclusive"
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[List[str]]],
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
seed: Optional[int] = None,
tile_height: Optional[int] = 512,
tile_width: Optional[int] = 512,
tile_row_overlap: Optional[int] = 256,
tile_col_overlap: Optional[int] = 256,
guidance_scale_tiles: Optional[List[List[float]]] = None,
seed_tiles: Optional[List[List[int]]] = None,
seed_tiles_mode: Optional[Union[str, List[List[str]]]] = "full",
seed_reroll_regions: Optional[List[Tuple[int, int, int, int, int]]] = None,
cpu_vae: Optional[bool] = False,
):
r"""
Function to run the diffusion pipeline with tiling support.
Args:
prompt: either a single string (no tiling) or a list of lists with all the prompts to use (one list for each row of tiles). This will also define the tiling structure.
num_inference_steps: number of diffusions steps.
guidance_scale: classifier-free guidance.
seed: general random seed to initialize latents.
tile_height: height in pixels of each grid tile.
tile_width: width in pixels of each grid tile.
tile_row_overlap: number of overlap pixels between tiles in consecutive rows.
tile_col_overlap: number of overlap pixels between tiles in consecutive columns.
guidance_scale_tiles: specific weights for classifier-free guidance in each tile.
guidance_scale_tiles: specific weights for classifier-free guidance in each tile. If None, the value provided in guidance_scale will be used.
seed_tiles: specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard seed parameter.
seed_tiles_mode: either "full" "exclusive". If "full", all the latents affected by the tile be overriden. If "exclusive", only the latents that are affected exclusively by this tile (and no other tiles) will be overrriden.
seed_reroll_regions: a list of tuples in the form (start row, end row, start column, end column, seed) defining regions in pixel space for which the latents will be overriden using the given seed. Takes priority over seed_tiles.
cpu_vae: the decoder from latent space to pixel space can require too mucho GPU RAM for large images. If you find out of memory errors at the end of the generation process, try setting this parameter to True to run the decoder in CPU. Slower, but should run without memory issues.
Examples:
Returns:
A PIL image with the generated image.
"""
if not isinstance(prompt, list) or not all(isinstance(row, list) for row in prompt):
raise ValueError(f"`prompt` has to be a list of lists but is {type(prompt)}")
grid_rows = len(prompt)
grid_cols = len(prompt[0])
if not all(len(row) == grid_cols for row in prompt):
raise ValueError("All prompt rows must have the same number of prompt columns")
if not isinstance(seed_tiles_mode, str) and (
not isinstance(seed_tiles_mode, list) or not all(isinstance(row, list) for row in seed_tiles_mode)
):
raise ValueError(f"`seed_tiles_mode` has to be a string or list of lists but is {type(prompt)}")
if isinstance(seed_tiles_mode, str):
seed_tiles_mode = [[seed_tiles_mode for _ in range(len(row))] for row in prompt]
modes = [mode.value for mode in self.SeedTilesMode]
if any(mode not in modes for row in seed_tiles_mode for mode in row):
raise ValueError(f"Seed tiles mode must be one of {modes}")
if seed_reroll_regions is None:
seed_reroll_regions = []
batch_size = 1
# create original noisy latents using the timesteps
height = tile_height + (grid_rows - 1) * (tile_height - tile_row_overlap)
width = tile_width + (grid_cols - 1) * (tile_width - tile_col_overlap)
latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
generator = torch.Generator("cuda").manual_seed(seed)
latents = torch.randn(latents_shape, generator=generator, device=self.device)
# overwrite latents for specific tiles if provided
if seed_tiles is not None:
for row in range(grid_rows):
for col in range(grid_cols):
if (seed_tile := seed_tiles[row][col]) is not None:
mode = seed_tiles_mode[row][col]
if mode == self.SeedTilesMode.FULL.value:
row_init, row_end, col_init, col_end = _tile2latent_indices(
row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
)
else:
row_init, row_end, col_init, col_end = _tile2latent_exclusive_indices(
row,
col,
tile_width,
tile_height,
tile_row_overlap,
tile_col_overlap,
grid_rows,
grid_cols,
)
tile_generator = torch.Generator("cuda").manual_seed(seed_tile)
tile_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
tile_shape, generator=tile_generator, device=self.device
)
# overwrite again for seed reroll regions
for row_init, row_end, col_init, col_end, seed_reroll in seed_reroll_regions:
row_init, row_end, col_init, col_end = _pixel2latent_indices(
row_init, row_end, col_init, col_end
) # to latent space coordinates
reroll_generator = torch.Generator("cuda").manual_seed(seed_reroll)
region_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
region_shape, generator=reroll_generator, device=self.device
)
# Prepare scheduler
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = latents * self.scheduler.sigmas[0]
# get prompts text embeddings
text_input = [
[
self.tokenizer(
col,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
for col in row
]
for row in prompt
]
text_embeddings = [[self.text_encoder(col.input_ids.to(self.device))[0] for col in row] for row in text_input]
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0 # TODO: also active if any tile has guidance scale
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
for i in range(grid_rows):
for j in range(grid_cols):
max_length = text_input[i][j].input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings[i][j] = torch.cat([uncond_embeddings, text_embeddings[i][j]])
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# Mask for tile weights strenght
tile_weights = self._gaussian_weights(tile_width, tile_height, batch_size)
# Diffusion timesteps
for i, t in tqdm(enumerate(self.scheduler.timesteps)):
# Diffuse each tile
noise_preds = []
for row in range(grid_rows):
noise_preds_row = []
for col in range(grid_cols):
px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
)
tile_latents = latents[:, :, px_row_init:px_row_end, px_col_init:px_col_end]
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([tile_latents] * 2) if do_classifier_free_guidance else tile_latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings[row][col])[
"sample"
]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
guidance = (
guidance_scale
if guidance_scale_tiles is None or guidance_scale_tiles[row][col] is None
else guidance_scale_tiles[row][col]
)
noise_pred_tile = noise_pred_uncond + guidance * (noise_pred_text - noise_pred_uncond)
noise_preds_row.append(noise_pred_tile)
noise_preds.append(noise_preds_row)
# Stitch noise predictions for all tiles
noise_pred = torch.zeros(latents.shape, device=self.device)
contributors = torch.zeros(latents.shape, device=self.device)
# Add each tile contribution to overall latents
for row in range(grid_rows):
for col in range(grid_cols):
px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
)
noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += (
noise_preds[row][col] * tile_weights
)
contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights
# Average overlapping areas with more than 1 contributor
noise_pred /= contributors
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
# scale and decode the image latents with vae
image = self.decode_latents(latents, cpu_vae)
return {"images": image}
def _gaussian_weights(self, tile_width, tile_height, nbatches):
"""Generates a gaussian mask of weights for tile contributions"""
import numpy as np
from numpy import exp, pi, sqrt
latent_width = tile_width // 8
latent_height = tile_height // 8
var = 0.01
midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1
x_probs = [
exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)
for x in range(latent_width)
]
midpoint = latent_height / 2
y_probs = [
exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)
for y in range(latent_height)
]
weights = np.outer(y_probs, x_probs)
return torch.tile(torch.tensor(weights, device=self.device), (nbatches, self.unet.config.in_channels, 1, 1))
| diffusers/examples/community/mixture_tiling.py/0 | {
"file_path": "diffusers/examples/community/mixture_tiling.py",
"repo_id": "diffusers",
"token_count": 9148
} | 99 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
import torchvision.transforms as T
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
from diffusers.models.attention_processor import Attention, AttnProcessor
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import BaseOutput, deprecate, logging
from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
gmflow_dir = "/path/to/gmflow"
sys.path.insert(0, gmflow_dir)
from gmflow.gmflow import GMFlow # noqa: E402
from utils.utils import InputPadder # noqa: E402
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def coords_grid(b, h, w, homogeneous=False, device=None):
y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W]
stacks = [x, y]
if homogeneous:
ones = torch.ones_like(x) # [H, W]
stacks.append(ones)
grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W]
grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W]
if device is not None:
grid = grid.to(device)
return grid
def bilinear_sample(img, sample_coords, mode="bilinear", padding_mode="zeros", return_mask=False):
# img: [B, C, H, W]
# sample_coords: [B, 2, H, W] in image scale
if sample_coords.size(1) != 2: # [B, H, W, 2]
sample_coords = sample_coords.permute(0, 3, 1, 2)
b, _, h, w = sample_coords.shape
# Normalize to [-1, 1]
x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1
y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1
grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2]
img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True)
if return_mask:
mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1) # [B, H, W]
return img, mask
return img
def flow_warp(feature, flow, mask=False, mode="bilinear", padding_mode="zeros"):
b, c, h, w = feature.size()
assert flow.size(1) == 2
grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W]
grid = grid.to(feature.dtype)
return bilinear_sample(feature, grid, mode=mode, padding_mode=padding_mode, return_mask=mask)
def forward_backward_consistency_check(fwd_flow, bwd_flow, alpha=0.01, beta=0.5):
# fwd_flow, bwd_flow: [B, 2, H, W]
# alpha and beta values are following UnFlow
# (https://arxiv.org/abs/1711.07837)
assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4
assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2
flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1) # [B, H, W]
warped_bwd_flow = flow_warp(bwd_flow, fwd_flow) # [B, 2, H, W]
warped_fwd_flow = flow_warp(fwd_flow, bwd_flow) # [B, 2, H, W]
diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1) # [B, H, W]
diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1)
threshold = alpha * flow_mag + beta
fwd_occ = (diff_fwd > threshold).float() # [B, H, W]
bwd_occ = (diff_bwd > threshold).float()
return fwd_occ, bwd_occ
@torch.no_grad()
def get_warped_and_mask(flow_model, image1, image2, image3=None, pixel_consistency=False):
if image3 is None:
image3 = image1
padder = InputPadder(image1.shape, padding_factor=8)
image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
results_dict = flow_model(
image1, image2, attn_splits_list=[2], corr_radius_list=[-1], prop_radius_list=[-1], pred_bidir_flow=True
)
flow_pr = results_dict["flow_preds"][-1] # [B, 2, H, W]
fwd_flow = padder.unpad(flow_pr[0]).unsqueeze(0) # [1, 2, H, W]
bwd_flow = padder.unpad(flow_pr[1]).unsqueeze(0) # [1, 2, H, W]
fwd_occ, bwd_occ = forward_backward_consistency_check(fwd_flow, bwd_flow) # [1, H, W] float
if pixel_consistency:
warped_image1 = flow_warp(image1, bwd_flow)
bwd_occ = torch.clamp(
bwd_occ + (abs(image2 - warped_image1).mean(dim=1) > 255 * 0.25).float(), 0, 1
).unsqueeze(0)
warped_results = flow_warp(image3, bwd_flow)
return warped_results, bwd_occ, bwd_flow
blur = T.GaussianBlur(kernel_size=(9, 9), sigma=(18, 18))
@dataclass
class TextToVideoSDPipelineOutput(BaseOutput):
"""
Output class for text-to-video pipelines.
Args:
frames (`List[np.ndarray]` or `torch.FloatTensor`)
List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as
a `torch` tensor. The length of the list denotes the video length (the number of frames).
"""
frames: Union[List[np.ndarray], torch.FloatTensor]
@torch.no_grad()
def find_flat_region(mask):
device = mask.device
kernel_x = torch.Tensor([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0).to(device)
kernel_y = torch.Tensor([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]).unsqueeze(0).unsqueeze(0).to(device)
mask_ = F.pad(mask.unsqueeze(0), (1, 1, 1, 1), mode="replicate")
grad_x = torch.nn.functional.conv2d(mask_, kernel_x)
grad_y = torch.nn.functional.conv2d(mask_, kernel_y)
return ((abs(grad_x) + abs(grad_y)) == 0).float()[0]
class AttnState:
STORE = 0
LOAD = 1
LOAD_AND_STORE_PREV = 2
def __init__(self):
self.reset()
@property
def state(self):
return self.__state
@property
def timestep(self):
return self.__timestep
def set_timestep(self, t):
self.__timestep = t
def reset(self):
self.__state = AttnState.STORE
self.__timestep = 0
def to_load(self):
self.__state = AttnState.LOAD
def to_load_and_store_prev(self):
self.__state = AttnState.LOAD_AND_STORE_PREV
class CrossFrameAttnProcessor(AttnProcessor):
"""
Cross frame attention processor. Each frame attends the first frame and previous frame.
Args:
attn_state: Whether the model is processing the first frame or an intermediate frame
"""
def __init__(self, attn_state: AttnState):
super().__init__()
self.attn_state = attn_state
self.first_maps = {}
self.prev_maps = {}
def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
# Is self attention
if encoder_hidden_states is None:
t = self.attn_state.timestep
if self.attn_state.state == AttnState.STORE:
self.first_maps[t] = hidden_states.detach()
self.prev_maps[t] = hidden_states.detach()
res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)
else:
if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:
tmp = hidden_states.detach()
cross_map = torch.cat((self.first_maps[t], self.prev_maps[t]), dim=1)
res = super().__call__(attn, hidden_states, cross_map, attention_mask, temb)
if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:
self.prev_maps[t] = tmp
else:
res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)
return res
def prepare_image(image):
if isinstance(image, torch.Tensor):
# Batch single image
if image.ndim == 3:
image = image.unsqueeze(0)
image = image.to(dtype=torch.float32)
else:
# preprocess image
if isinstance(image, (PIL.Image.Image, np.ndarray)):
image = [image]
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
image = [np.array(i.convert("RGB"))[None, :] for i in image]
image = np.concatenate(image, axis=0)
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
image = np.concatenate([i[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
return image
class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
r"""
Pipeline for video-to-video translation using Stable Diffusion with Rerender Algorithm.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
In addition the pipeline inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
as a list, the outputs from each ControlNet are added together to create one combined additional
conditioning.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
image_encoder=None,
requires_safety_checker: bool = True,
):
super().__init__(
vae,
text_encoder,
tokenizer,
unet,
controlnet,
scheduler,
safety_checker,
feature_extractor,
image_encoder,
requires_safety_checker,
)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
if isinstance(controlnet, (list, tuple)):
controlnet = MultiControlNetModel(controlnet)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
self.control_image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
self.attn_state = AttnState()
attn_processor_dict = {}
for k in unet.attn_processors.keys():
if k.startswith("up"):
attn_processor_dict[k] = CrossFrameAttnProcessor(self.attn_state)
else:
attn_processor_dict[k] = AttnProcessor()
self.unet.set_attn_processor(attn_processor_dict)
flow_model = GMFlow(
feature_channels=128,
num_scales=1,
upsample_factor=8,
num_head=1,
attention_type="swin",
ffn_dim_expansion=4,
num_transformer_layers=6,
).to("cuda")
checkpoint = torch.utils.model_zoo.load_url(
"https://huggingface.co/Anonymous-sub/Rerender/resolve/main/models/gmflow_sintel-0c07dcb3.pth",
map_location=lambda storage, loc: storage,
)
weights = checkpoint["model"] if "model" in checkpoint else checkpoint
flow_model.load_state_dict(weights, strict=False)
flow_model.eval()
self.flow_model = flow_model
# Modified from src/diffusers/pipelines/controlnet/pipeline_controlnet.StableDiffusionControlNetImg2ImgPipeline.check_inputs
def check_inputs(
self,
prompt,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
controlnet_conditioning_scale=1.0,
control_guidance_start=0.0,
control_guidance_end=1.0,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# `prompt` needs more sophisticated handling when there are multiple
# conditionings.
if isinstance(self.controlnet, MultiControlNetModel):
if isinstance(prompt, list):
logger.warning(
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
" prompts. The conditionings will be fixed across the prompts."
)
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
elif (
isinstance(self.controlnet, MultiControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
):
if isinstance(controlnet_conditioning_scale, list):
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
self.controlnet.nets
):
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False
if len(control_guidance_start) != len(control_guidance_end):
raise ValueError(
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
)
if isinstance(self.controlnet, MultiControlNetModel):
if len(control_guidance_start) != len(self.controlnet.nets):
raise ValueError(
f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
)
for start, end in zip(control_guidance_start, control_guidance_end):
if start >= end:
raise ValueError(
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
)
if start < 0.0:
raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
if end > 1.0:
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
def prepare_control_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidance=False,
guess_mode=False,
):
image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
image_batch_size = image.shape[0]
if image_batch_size == 1:
repeat_by = batch_size
else:
# image batch size is the same as prompt batch size
repeat_by = num_images_per_prompt
image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype)
if do_classifier_free_guidance and not guess_mode:
image = torch.cat([image] * 2)
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
elif isinstance(generator, list):
init_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.vae.encode(image).latent_dist.sample(generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
frames: Union[List[np.ndarray], torch.FloatTensor] = None,
control_frames: Union[List[np.ndarray], torch.FloatTensor] = None,
strength: float = 0.8,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 1.0,
warp_start: Union[float, List[float]] = 0.0,
warp_end: Union[float, List[float]] = 0.3,
mask_start: Union[float, List[float]] = 0.5,
mask_end: Union[float, List[float]] = 0.8,
smooth_boundary: bool = True,
mask_strength: Union[float, List[float]] = 0.5,
inner_strength: Union[float, List[float]] = 0.9,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
frames (`List[np.ndarray]` or `torch.FloatTensor`): The input images to be used as the starting point for the image generation process.
control_frames (`List[np.ndarray]` or `torch.FloatTensor`): The ControlNet input images condition to provide guidance to the `unet` for generation.
strength ('float'): SDEdit strength.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
than for [`~StableDiffusionControlNetPipeline.__call__`].
guess_mode (`bool`, *optional*, defaults to `False`):
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the controlnet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the controlnet stops applying.
warp_start (`float`): Shape-aware fusion start timestep.
warp_end (`float`): Shape-aware fusion end timestep.
mask_start (`float`): Pixel-aware fusion start timestep.
mask_end (`float`):Pixel-aware fusion end timestep.
smooth_boundary (`bool`): Smooth fusion boundary. Set `True` to prevent artifacts at boundary.
mask_strength (`float`): Pixel-aware fusion strength.
inner_strength (`float`): Pixel-aware fusion detail level.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
# align format for control guidance
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
control_guidance_start, control_guidance_end = (
mult * [control_guidance_start],
mult * [control_guidance_end],
)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
controlnet_conditioning_scale,
control_guidance_start,
control_guidance_end,
)
# 2. Define call parameters
# Currently we only support 1 prompt
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
assert False
else:
assert False
num_images_per_prompt = 1
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
global_pool_conditions = (
controlnet.config.global_pool_conditions
if isinstance(controlnet, ControlNetModel)
else controlnet.nets[0].config.global_pool_conditions
)
guess_mode = guess_mode or global_pool_conditions
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Process the first frame
height, width = None, None
output_frames = []
self.attn_state.reset()
# 4.1 prepare frames
image = self.image_processor.preprocess(frames[0]).to(dtype=torch.float32)
first_image = image[0] # C, H, W
# 4.2 Prepare controlnet_conditioning_image
# Currently we only support single control
if isinstance(controlnet, ControlNetModel):
control_image = self.prepare_control_image(
image=control_frames[0],
width=width,
height=height,
batch_size=batch_size,
num_images_per_prompt=1,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
else:
assert False
# 4.3 Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size)
# 4.4 Prepare latent variables
latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
)
# 4.5 Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 4.6 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
first_x0_list = []
# 4.7 Denoising loop
num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order
with self.progress_bar(total=cur_num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
self.attn_state.set_timestep(t.item())
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# controlnet(s) inference
if guess_mode and do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
else:
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=control_image,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
if guess_mode and do_classifier_free_guidance:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
alpha_prod_t = self.scheduler.alphas_cumprod[t]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
first_x0 = pred_x0.detach()
first_x0_list.append(first_x0)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
first_result = image
prev_result = image
do_denormalize = [True] * image.shape[0]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
output_frames.append(image[0])
# 5. Process each frame
for idx in range(1, len(frames)):
image = frames[idx]
prev_image = frames[idx - 1]
control_image = control_frames[idx]
# 5.1 prepare frames
image = self.image_processor.preprocess(image).to(dtype=torch.float32)
prev_image = self.image_processor.preprocess(prev_image).to(dtype=torch.float32)
warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask(
self.flow_model, first_image, image[0], first_result, False
)
blend_mask_0 = blur(F.max_pool2d(bwd_occ_0, kernel_size=9, stride=1, padding=4))
blend_mask_0 = torch.clamp(blend_mask_0 + bwd_occ_0, 0, 1)
warped_pre, bwd_occ_pre, bwd_flow_pre = get_warped_and_mask(
self.flow_model, prev_image[0], image[0], prev_result, False
)
blend_mask_pre = blur(F.max_pool2d(bwd_occ_pre, kernel_size=9, stride=1, padding=4))
blend_mask_pre = torch.clamp(blend_mask_pre + bwd_occ_pre, 0, 1)
warp_mask = 1 - F.max_pool2d(blend_mask_0, kernel_size=8)
warp_flow = F.interpolate(bwd_flow_0 / 8.0, scale_factor=1.0 / 8, mode="bilinear")
# 5.2 Prepare controlnet_conditioning_image
# Currently we only support single control
if isinstance(controlnet, ControlNetModel):
control_image = self.prepare_control_image(
image=control_image,
width=width,
height=height,
batch_size=batch_size,
num_images_per_prompt=1,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
else:
assert False
# 5.3 Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size)
skip_t = int(num_inference_steps * (1 - strength))
warp_start_t = int(warp_start * num_inference_steps)
warp_end_t = int(warp_end * num_inference_steps)
mask_start_t = int(mask_start * num_inference_steps)
mask_end_t = int(mask_end * num_inference_steps)
# 5.4 Prepare latent variables
init_latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
)
# 5.5 Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 5.6 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
# 5.7 Denoising loop
num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order
def denoising_loop(latents, mask=None, xtrg=None, noise_rescale=None):
dir_xt = 0
latents_dtype = latents.dtype
with self.progress_bar(total=cur_num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
self.attn_state.set_timestep(t.item())
if i + skip_t >= mask_start_t and i + skip_t <= mask_end_t and xtrg is not None:
rescale = torch.maximum(1.0 - mask, (1 - mask**2) ** 0.5 * inner_strength)
if noise_rescale is not None:
rescale = (1.0 - mask) * (1 - noise_rescale) + rescale * noise_rescale
noise = randn_tensor(xtrg.shape, generator=generator, device=device, dtype=xtrg.dtype)
latents_ref = self.scheduler.add_noise(xtrg, noise, t)
latents = latents_ref * mask + (1.0 - mask) * (latents - dir_xt) + rescale * dir_xt
latents = latents.to(latents_dtype)
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# controlnet(s) inference
if guess_mode and do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
else:
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=control_image,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
if guess_mode and do_classifier_free_guidance:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_block_res_samples = [
torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples
]
mid_block_res_sample = torch.cat(
[torch.zeros_like(mid_block_res_sample), mid_block_res_sample]
)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# Get pred_x0 from scheduler
alpha_prod_t = self.scheduler.alphas_cumprod[t]
beta_prod_t = 1 - alpha_prod_t
pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
if i + skip_t >= warp_start_t and i + skip_t <= warp_end_t:
# warp x_0
pred_x0 = (
flow_warp(first_x0_list[i], warp_flow, mode="nearest") * warp_mask
+ (1 - warp_mask) * pred_x0
)
# get x_t from x_0
latents = self.scheduler.add_noise(pred_x0, noise_pred, t).to(latents_dtype)
prev_t = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
if i == len(timesteps) - 1:
alpha_t_prev = 1.0
else:
alpha_t_prev = self.scheduler.alphas_cumprod[prev_t]
dir_xt = (1.0 - alpha_t_prev) ** 0.5 * noise_pred
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[
0
]
# call the callback, if provided
if i == len(timesteps) - 1 or (
(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
return latents
if mask_start_t <= mask_end_t:
self.attn_state.to_load()
else:
self.attn_state.to_load_and_store_prev()
latents = denoising_loop(init_latents)
if mask_start_t <= mask_end_t:
direct_result = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
blend_results = (1 - blend_mask_pre) * warped_pre + blend_mask_pre * direct_result
blend_results = (1 - blend_mask_0) * warped_0 + blend_mask_0 * blend_results
bwd_occ = 1 - torch.clamp(1 - bwd_occ_pre + 1 - bwd_occ_0, 0, 1)
blend_mask = blur(F.max_pool2d(bwd_occ, kernel_size=9, stride=1, padding=4))
blend_mask = 1 - torch.clamp(blend_mask + bwd_occ, 0, 1)
blend_results = blend_results.to(latents.dtype)
xtrg = self.vae.encode(blend_results).latent_dist.sample(generator)
xtrg = self.vae.config.scaling_factor * xtrg
blend_results_rec = self.vae.decode(xtrg / self.vae.config.scaling_factor, return_dict=False)[0]
xtrg_rec = self.vae.encode(blend_results_rec).latent_dist.sample(generator)
xtrg_rec = self.vae.config.scaling_factor * xtrg_rec
xtrg_ = xtrg + (xtrg - xtrg_rec)
blend_results_rec_new = self.vae.decode(xtrg_ / self.vae.config.scaling_factor, return_dict=False)[0]
tmp = (abs(blend_results_rec_new - blend_results).mean(dim=1, keepdims=True) > 0.25).float()
mask_x = F.max_pool2d(
(F.interpolate(tmp, scale_factor=1 / 8.0, mode="bilinear") > 0).float(),
kernel_size=3,
stride=1,
padding=1,
)
mask = 1 - F.max_pool2d(1 - blend_mask, kernel_size=8) # * (1-mask_x)
if smooth_boundary:
noise_rescale = find_flat_region(mask)
else:
noise_rescale = torch.ones_like(mask)
xtrg = (xtrg + (1 - mask_x) * (xtrg - xtrg_rec)) * mask
xtrg = xtrg.to(latents.dtype)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
self.attn_state.to_load_and_store_prev()
latents = denoising_loop(init_latents, mask * mask_strength, xtrg, noise_rescale)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
prev_result = image
do_denormalize = [True] * image.shape[0]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
output_frames.append(image[0])
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return output_frames
return TextToVideoSDPipelineOutput(frames=output_frames)
| diffusers/examples/community/rerender_a_video.py/0 | {
"file_path": "diffusers/examples/community/rerender_a_video.py",
"repo_id": "diffusers",
"token_count": 26689
} | 100 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel
from diffusers.configuration_utils import FrozenDict, deprecate
from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
is_accelerate_available,
is_accelerate_version,
logging,
)
from diffusers.utils.torch_utils import randn_tensor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def prepare_mask_and_masked_image(image, mask):
"""
Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
``image`` and ``1`` for the ``mask``.
The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
Args:
image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
Raises:
ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
(ot the other way around).
Returns:
tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
dimensions: ``batch x channels x height x width``.
"""
if isinstance(image, torch.Tensor):
if not isinstance(mask, torch.Tensor):
raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
# Batch single image
if image.ndim == 3:
assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
image = image.unsqueeze(0)
# Batch and add channel dim for single mask
if mask.ndim == 2:
mask = mask.unsqueeze(0).unsqueeze(0)
# Batch single mask or add channel dim
if mask.ndim == 3:
# Single batched mask, no channel dim or single mask not batched but channel dim
if mask.shape[0] == 1:
mask = mask.unsqueeze(0)
# Batched masks no channel dim
else:
mask = mask.unsqueeze(1)
assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
# Check image is in [-1, 1]
if image.min() < -1 or image.max() > 1:
raise ValueError("Image should be in [-1, 1] range")
# Check mask is in [0, 1]
if mask.min() < 0 or mask.max() > 1:
raise ValueError("Mask should be in [0, 1] range")
# Binarize mask
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
# Image as float32
image = image.to(dtype=torch.float32)
elif isinstance(mask, torch.Tensor):
raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
else:
# preprocess image
if isinstance(image, (PIL.Image.Image, np.ndarray)):
image = [image]
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
image = [np.array(i.convert("RGB"))[None, :] for i in image]
image = np.concatenate(image, axis=0)
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
image = np.concatenate([i[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
# preprocess mask
if isinstance(mask, (PIL.Image.Image, np.ndarray)):
mask = [mask]
if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
mask = mask.astype(np.float32) / 255.0
elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
# masked_image = image * (mask >= 0.5)
masked_image = image
return mask, masked_image
class StableDiffusionRepaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
In addition the pipeline inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
as well as the following saving methods:
- *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate(
"skip_prk_steps not set",
"1.0.0",
deprecation_message,
standard_warn=False,
)
new_config = dict(scheduler.config)
new_config["skip_prk_steps"] = True
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
# Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
if unet.config.in_channels != 4:
logger.warning(
f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
f" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,"
". If you did not intend to modify"
" this behavior, please check whether you have loaded the right checkpoint."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
def enable_sequential_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
cpu_offload(cpu_offloaded_model, device)
if self.safety_checker is not None:
cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
else:
has_nsfw_concept = None
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):
shape = (
batch_size,
num_channels_latents,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_mask_latents(
self,
mask,
masked_image,
batch_size,
height,
width,
dtype,
device,
generator,
do_classifier_free_guidance,
):
# resize the mask to latents shape as we concatenate the mask to the latents
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
# and half precision
mask = torch.nn.functional.interpolate(
mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
)
mask = mask.to(device=device, dtype=dtype)
masked_image = masked_image.to(device=device, dtype=dtype)
# encode the mask image into latents space so we can concatenate it to the latents
if isinstance(generator, list):
masked_image_latents = [
self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(batch_size)
]
masked_image_latents = torch.cat(masked_image_latents, dim=0)
else:
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
" of masks that you pass is divisible by the total requested batch size."
)
mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
if masked_image_latents.shape[0] < batch_size:
if not batch_size % masked_image_latents.shape[0] == 0:
raise ValueError(
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
" Make sure the number of images that you pass is divisible by the total requested batch size."
)
masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
masked_image_latents = (
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
)
# aligning device to prevent device errors when concating it with the latent model input
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
return mask, masked_image_latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
jump_length: Optional[int] = 10,
jump_n_sample: Optional[int] = 10,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
be masked out with `mask_image` and repainted according to `prompt`.
mask_image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
jump_length (`int`, *optional*, defaults to 10):
The number of steps taken forward in time before going backward in time for a single jump ("j" in
RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
jump_n_sample (`int`, *optional*, defaults to 10):
The number of times we will make forward time jump for a given chosen time sample. Take a look at
Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Examples:
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import StableDiffusionPipeline, RePaintScheduler
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> base_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/"
>>> img_url = base_url + "overture-creations-5sI6fQgYIuo.png"
>>> mask_url = base_url + "overture-creations-5sI6fQgYIuo_mask.png "
>>> init_image = download_image(img_url).resize((512, 512))
>>> mask_image = download_image(mask_url).resize((512, 512))
>>> pipe = DiffusionPipeline.from_pretrained(
... "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, custom_pipeline="stable_diffusion_repaint",
... )
>>> pipe.scheduler = RePaintScheduler.from_config(pipe.scheduler.config)
>>> pipe = pipe.to("cuda")
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
>>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs
self.check_inputs(
prompt,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
if image is None:
raise ValueError("`image` input cannot be undefined.")
if mask_image is None:
raise ValueError("`mask_image` input cannot be undefined.")
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Preprocess mask and image
mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, device)
self.scheduler.eta = eta
timesteps = self.scheduler.timesteps
# latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 7. Prepare mask latent variables
mask, masked_image_latents = self.prepare_mask_latents(
mask,
masked_image,
batch_size * num_images_per_prompt,
height,
width,
prompt_embeds.dtype,
device,
generator,
do_classifier_free_guidance=False, # We do not need duplicate mask and image
)
# 8. Check that sizes of mask, masked image and latents match
# num_channels_mask = mask.shape[1]
# num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} "
f" = Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
t_last = timesteps[0] + 1
# 10. Denoising loop
with self.progress_bar(total=len(timesteps)) as progress_bar:
for i, t in enumerate(timesteps):
if t >= t_last:
# compute the reverse: x_t-1 -> x_t
latents = self.scheduler.undo_step(latents, t_last, generator)
progress_bar.update()
t_last = t
continue
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
masked_image_latents,
mask,
**extra_step_kwargs,
).prev_sample
# call the callback, if provided
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
t_last = t
# 11. Post-processing
image = self.decode_latents(latents)
# 12. Run safety checker
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 13. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| diffusers/examples/community/stable_diffusion_repaint.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_repaint.py",
"repo_id": "diffusers",
"token_count": 21148
} | 101 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import functools
import gc
import itertools
import json
import logging
import math
import os
import random
import shutil
from pathlib import Path
from typing import List, Union
import accelerate
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms.functional as TF
import transformers
import webdataset as wds
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from braceexpand import braceexpand
from huggingface_hub import create_repo, upload_folder
from packaging import version
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
from torch.utils.data import default_collate
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig
from webdataset.tariterators import (
base_plus_ext,
tar_file_expander,
url_opener,
valid_sample,
)
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
LCMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.training_utils import resolve_interpolation_mode
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
MAX_SEQ_LENGTH = 77
if is_wandb_available():
import wandb
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.26.0.dev0")
logger = get_logger(__name__)
def get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype, adapter_name: str = "default"):
kohya_ss_state_dict = {}
for peft_key, weight in get_peft_model_state_dict(module, adapter_name=adapter_name).items():
kohya_key = peft_key.replace("base_model.model", prefix)
kohya_key = kohya_key.replace("lora_A", "lora_down")
kohya_key = kohya_key.replace("lora_B", "lora_up")
kohya_key = kohya_key.replace(".", "_", kohya_key.count(".") - 2)
kohya_ss_state_dict[kohya_key] = weight.to(dtype)
# Set alpha parameter
if "lora_down" in kohya_key:
alpha_key = f'{kohya_key.split(".")[0]}.alpha'
kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)
return kohya_ss_state_dict
def filter_keys(key_set):
def _f(dictionary):
return {k: v for k, v in dictionary.items() if k in key_set}
return _f
def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
"""Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
lower case (Default value = True)
"""
current_sample = None
for filesample in data:
assert isinstance(filesample, dict)
fname, value = filesample["fname"], filesample["data"]
prefix, suffix = keys(fname)
if prefix is None:
continue
if lcase:
suffix = suffix.lower()
# FIXME webdataset version throws if suffix in current_sample, but we have a potential for
# this happening in the current LAION400m dataset if a tar ends with same prefix as the next
# begins, rare, but can happen since prefix aren't unique across tar files in that dataset
if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
if valid_sample(current_sample):
yield current_sample
current_sample = {"__key__": prefix, "__url__": filesample["__url__"]}
if suffixes is None or suffix in suffixes:
current_sample[suffix] = value
if valid_sample(current_sample):
yield current_sample
def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
# NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
streams = url_opener(src, handler=handler)
files = tar_file_expander(streams, handler=handler)
samples = group_by_keys_nothrow(files, handler=handler)
return samples
class WebdatasetFilter:
def __init__(self, min_size=1024, max_pwatermark=0.5):
self.min_size = min_size
self.max_pwatermark = max_pwatermark
def __call__(self, x):
try:
if "json" in x:
x_json = json.loads(x["json"])
filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get(
"original_height", 0
) >= self.min_size
filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark
return filter_size and filter_watermark
else:
return False
except Exception:
return False
class SDText2ImageDataset:
def __init__(
self,
train_shards_path_or_url: Union[str, List[str]],
num_train_examples: int,
per_gpu_batch_size: int,
global_batch_size: int,
num_workers: int,
resolution: int = 512,
interpolation_type: str = "bilinear",
shuffle_buffer_size: int = 1000,
pin_memory: bool = False,
persistent_workers: bool = False,
):
if not isinstance(train_shards_path_or_url, str):
train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
# flatten list using itertools
train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))
interpolation_mode = resolve_interpolation_mode(interpolation_type)
def transform(example):
# resize image
image = example["image"]
image = TF.resize(image, resolution, interpolation=interpolation_mode)
# get crop coordinates and crop image
c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
image = TF.crop(image, c_top, c_left, resolution, resolution)
image = TF.to_tensor(image)
image = TF.normalize(image, [0.5], [0.5])
example["image"] = image
return example
processing_pipeline = [
wds.decode("pil", handler=wds.ignore_and_continue),
wds.rename(image="jpg;png;jpeg;webp", text="text;txt;caption", handler=wds.warn_and_continue),
wds.map(filter_keys({"image", "text"})),
wds.map(transform),
wds.to_tuple("image", "text"),
]
# Create train dataset and loader
pipeline = [
wds.ResampledShards(train_shards_path_or_url),
tarfile_to_samples_nothrow,
wds.shuffle(shuffle_buffer_size),
*processing_pipeline,
wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
]
num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker
num_batches = num_worker_batches * num_workers
num_samples = num_batches * global_batch_size
# each worker is iterating over this
self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)
self._train_dataloader = wds.WebLoader(
self._train_dataset,
batch_size=None,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory,
persistent_workers=persistent_workers,
)
# add meta-data to dataloader instance for convenience
self._train_dataloader.num_batches = num_batches
self._train_dataloader.num_samples = num_samples
@property
def train_dataset(self):
return self._train_dataset
@property
def train_dataloader(self):
return self._train_dataloader
def log_validation(vae, unet, args, accelerator, weight_dtype, step):
logger.info("Running validation... ")
unet = accelerator.unwrap_model(unet)
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_teacher_model,
vae=vae,
scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder="scheduler"),
revision=args.revision,
torch_dtype=weight_dtype,
safety_checker=None,
)
pipeline.set_progress_bar_config(disable=True)
lora_state_dict = get_module_kohya_state_dict(unet, "lora_unet", weight_dtype)
pipeline.load_lora_weights(lora_state_dict)
pipeline.fuse_lora()
pipeline = pipeline.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
validation_prompts = [
"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
]
image_logs = []
for _, prompt in enumerate(validation_prompts):
images = []
with torch.autocast("cuda", dtype=weight_dtype):
images = pipeline(
prompt=prompt,
num_inference_steps=4,
num_images_per_prompt=4,
generator=generator,
guidance_scale=1.0,
).images
image_logs.append({"validation_prompt": prompt, "images": images})
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
for log in image_logs:
images = log["images"]
validation_prompt = log["validation_prompt"]
formatted_images = []
for image in images:
formatted_images.append(np.asarray(image))
formatted_images = np.stack(formatted_images)
tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
elif tracker.name == "wandb":
formatted_images = []
for log in image_logs:
images = log["images"]
validation_prompt = log["validation_prompt"]
for image in images:
image = wandb.Image(image, caption=validation_prompt)
formatted_images.append(image)
tracker.log({"validation": formatted_images})
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
gc.collect()
torch.cuda.empty_cache()
return image_logs
# From LatentConsistencyModel.get_guidance_scale_embedding
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these timesteps
embedding_dim (`int`, *optional*, defaults to 512):
dimension of the embeddings to generate
dtype:
data type of the generated embeddings
Returns:
`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,) * dims_to_append]
# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
scaled_timestep = timestep_scaling * timestep
c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
return c_skip, c_out
# Compare LCMScheduler.step, Step 4
def get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
if prediction_type == "epsilon":
pred_x_0 = (sample - sigmas * model_output) / alphas
elif prediction_type == "sample":
pred_x_0 = model_output
elif prediction_type == "v_prediction":
pred_x_0 = alphas * sample - sigmas * model_output
else:
raise ValueError(
f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
f" are supported."
)
return pred_x_0
# Based on step 4 in DDIMScheduler.step
def get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
if prediction_type == "epsilon":
pred_epsilon = model_output
elif prediction_type == "sample":
pred_epsilon = (sample - alphas * model_output) / sigmas
elif prediction_type == "v_prediction":
pred_epsilon = alphas * model_output + sigmas * sample
else:
raise ValueError(
f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
f" are supported."
)
return pred_epsilon
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
class DDIMSolver:
def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
# DDIM sampling parameters
step_ratio = timesteps // ddim_timesteps
self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
self.ddim_alpha_cumprods_prev = np.asarray(
[alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
)
# convert to torch tensors
self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
def to(self, device):
self.ddim_timesteps = self.ddim_timesteps.to(device)
self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
return self
def ddim_step(self, pred_x0, pred_noise, timestep_index):
alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
return x_prev
@torch.no_grad()
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
"""
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src, alpha=1 - rate)
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
# ----------Model Checkpoint Loading Arguments----------
parser.add_argument(
"--pretrained_teacher_model",
type=str,
default=None,
required=True,
help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--teacher_revision",
type=str,
default=None,
required=False,
help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained LDM model identifier from huggingface.co/models.",
)
# ----------Training Arguments----------
# ----General Training Arguments----
parser.add_argument(
"--output_dir",
type=str,
default="lcm-xl-distilled",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
# ----Logging----
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
# ----Checkpointing----
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
# ----Image Processing----
parser.add_argument(
"--train_shards_path_or_url",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--interpolation_type",
type=str,
default="bilinear",
help=(
"The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,"
" `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`."
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
# ----Dataloader----
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
# ----Batch Size and Training Steps----
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
# ----Learning Rate----
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
# ----Optimizer (Adam)----
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
# ----Diffusion Training Arguments----
parser.add_argument(
"--proportion_empty_prompts",
type=float,
default=0,
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
)
# ----Latent Consistency Distillation (LCD) Specific Arguments----
parser.add_argument(
"--w_min",
type=float,
default=5.0,
required=False,
help=(
"The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
" compared to the original paper."
),
)
parser.add_argument(
"--w_max",
type=float,
default=15.0,
required=False,
help=(
"The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
" compared to the original paper."
),
)
parser.add_argument(
"--num_ddim_timesteps",
type=int,
default=50,
help="The number of timesteps to use for DDIM sampling.",
)
parser.add_argument(
"--loss_type",
type=str,
default="l2",
choices=["l2", "huber"],
help="The type of loss to use for the LCD loss.",
)
parser.add_argument(
"--huber_c",
type=float,
default=0.001,
help="The huber loss parameter. Only used if `--loss_type=huber`.",
)
parser.add_argument(
"--lora_rank",
type=int,
default=64,
help="The rank of the LoRA projection matrix.",
)
parser.add_argument(
"--lora_alpha",
type=int,
default=64,
help=(
"The value of the LoRA alpha parameter, which controls the scaling factor in front of the LoRA weight"
" update delta_W. No scaling will be performed if this value is equal to `lora_rank`."
),
)
parser.add_argument(
"--lora_dropout",
type=float,
default=0.0,
help="The dropout probability for the dropout layer added before applying the LoRA to each layer input.",
)
parser.add_argument(
"--lora_target_modules",
type=str,
default=None,
help=(
"A comma-separated string of target module keys to add LoRA to. If not set, a default list of modules will"
" be used. By default, LoRA will be applied to all conv and linear layers."
),
)
parser.add_argument(
"--vae_encode_batch_size",
type=int,
default=32,
required=False,
help=(
"The batch size used when encoding (and decoding) images to latents (and vice versa) using the VAE."
" Encoding or decoding the whole batch at once may run into OOM issues."
),
)
parser.add_argument(
"--timestep_scaling_factor",
type=float,
default=10.0,
help=(
"The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The"
" higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically"
" suffice."
),
)
# ----Mixed Precision----
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--cast_teacher_unet",
action="store_true",
help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
)
# ----Training Optimizations----
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
# ----Distributed Training----
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
# ----------Validation Arguments----------
parser.add_argument(
"--validation_steps",
type=int,
default=200,
help="Run validation every X steps.",
)
# ----------Huggingface Hub Arguments-----------
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
# ----------Accelerate Arguments----------
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
return args
# Adapted from pipelines.StableDiffusionPipeline.encode_prompt
def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):
captions = []
for caption in prompt_batch:
if random.random() < proportion_empty_prompts:
captions.append("")
elif isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
with torch.no_grad():
text_inputs = tokenizer(
captions,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device))[0]
return prompt_embeds
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name,
exist_ok=True,
token=args.hub_token,
private=True,
).repo_id
# 1. Create the noise scheduler and the desired noise schedule.
noise_scheduler = DDPMScheduler.from_pretrained(
args.pretrained_teacher_model, subfolder="scheduler", revision=args.teacher_revision
)
# DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us
alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
# Initialize the DDIM ODE solver for distillation.
solver = DDIMSolver(
noise_scheduler.alphas_cumprod.numpy(),
timesteps=noise_scheduler.config.num_train_timesteps,
ddim_timesteps=args.num_ddim_timesteps,
)
# 2. Load tokenizers from SD 1.X/2.X checkpoint.
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_teacher_model, subfolder="tokenizer", revision=args.teacher_revision, use_fast=False
)
# 3. Load text encoders from SD 1.X/2.X checkpoint.
# import correct text encoder classes
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_teacher_model, subfolder="text_encoder", revision=args.teacher_revision
)
# 4. Load VAE from SD 1.X/2.X checkpoint
vae = AutoencoderKL.from_pretrained(
args.pretrained_teacher_model,
subfolder="vae",
revision=args.teacher_revision,
)
# 5. Load teacher U-Net from SD 1.X/2.X checkpoint
teacher_unet = UNet2DConditionModel.from_pretrained(
args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
)
# 6. Freeze teacher vae, text_encoder, and teacher_unet
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
teacher_unet.requires_grad_(False)
# 7. Create online student U-Net.
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
)
unet.train()
# Check that all trainable models are in full precision
low_precision_error_string = (
" Please make sure to always have all model weights in full float32 precision when starting training - even if"
" doing mixed precision training, copy of the weights should still be float32."
)
if accelerator.unwrap_model(unet).dtype != torch.float32:
raise ValueError(
f"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
)
# 8. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.
if args.lora_target_modules is not None:
lora_target_modules = [module_key.strip() for module_key in args.lora_target_modules.split(",")]
else:
lora_target_modules = [
"to_q",
"to_k",
"to_v",
"to_out.0",
"proj_in",
"proj_out",
"ff.net.0.proj",
"ff.net.2",
"conv1",
"conv2",
"conv_shortcut",
"downsamplers.0.conv",
"upsamplers.0.conv",
"time_emb_proj",
]
lora_config = LoraConfig(
r=args.lora_rank,
target_modules=lora_target_modules,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
)
unet = get_peft_model(unet, lora_config)
# 9. Handle mixed precision and device placement
# For mixed precision training we cast all non-trainable weigths to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
# The VAE is in float32 to avoid NaN losses.
vae.to(accelerator.device)
if args.pretrained_vae_model_name_or_path is not None:
vae.to(dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
# Move teacher_unet to device, optionally cast to weight_dtype
teacher_unet.to(accelerator.device)
if args.cast_teacher_unet:
teacher_unet.to(dtype=weight_dtype)
# Also move the alpha and sigma noise schedules to accelerator.device.
alpha_schedule = alpha_schedule.to(accelerator.device)
sigma_schedule = sigma_schedule.to(accelerator.device)
# Move the ODE solver to accelerator.device.
solver = solver.to(accelerator.device)
# 10. Handle saving and loading of checkpoints
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
unet_ = accelerator.unwrap_model(unet)
lora_state_dict = get_peft_model_state_dict(unet_, adapter_name="default")
StableDiffusionPipeline.save_lora_weights(os.path.join(output_dir, "unet_lora"), lora_state_dict)
# save weights in peft format to be able to load them back
unet_.save_pretrained(output_dir)
for _, model in enumerate(models):
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
# load the LoRA into the model
unet_ = accelerator.unwrap_model(unet)
unet_.load_adapter(input_dir, "default", is_trainable=True)
for _ in range(len(models)):
# pop models so that they are not loaded again
models.pop()
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# 11. Enable optimizations
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
teacher_unet.enable_xformers_memory_efficient_attention()
# target_unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# 12. Optimizer creation
optimizer = optimizer_class(
unet.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# 13. Dataset creation and data processing
# Here, we compute not just the text embeddings but also the additional embeddings
# needed for the SD XL UNet to operate.
def compute_embeddings(prompt_batch, proportion_empty_prompts, text_encoder, tokenizer, is_train=True):
prompt_embeds = encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train)
return {"prompt_embeds": prompt_embeds}
dataset = SDText2ImageDataset(
train_shards_path_or_url=args.train_shards_path_or_url,
num_train_examples=args.max_train_samples,
per_gpu_batch_size=args.train_batch_size,
global_batch_size=args.train_batch_size * accelerator.num_processes,
num_workers=args.dataloader_num_workers,
resolution=args.resolution,
interpolation_type=args.interpolation_type,
shuffle_buffer_size=1000,
pin_memory=True,
persistent_workers=True,
)
train_dataloader = dataset.train_dataloader
compute_embeddings_fn = functools.partial(
compute_embeddings,
proportion_empty_prompts=0,
text_encoder=text_encoder,
tokenizer=tokenizer,
)
# 14. LR Scheduler creation
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps,
num_training_steps=args.max_train_steps,
)
# 15. Prepare for training
# Prepare everything with our `accelerator`.
unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
uncond_input_ids = tokenizer(
[""] * args.train_batch_size, return_tensors="pt", padding="max_length", max_length=77
).input_ids.to(accelerator.device)
uncond_prompt_embeds = text_encoder(uncond_input_ids)[0]
# 16. Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num batches each epoch = {train_dataloader.num_batches}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# 1. Load and process the image and text conditioning
image, text = batch
image = image.to(accelerator.device, non_blocking=True)
encoded_text = compute_embeddings_fn(text)
pixel_values = image.to(dtype=weight_dtype)
if vae.dtype != weight_dtype:
vae.to(dtype=weight_dtype)
# encode pixel values with batch size of at most args.vae_encode_batch_size
latents = []
for i in range(0, pixel_values.shape[0], args.vae_encode_batch_size):
latents.append(vae.encode(pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample())
latents = torch.cat(latents, dim=0)
latents = latents * vae.config.scaling_factor
latents = latents.to(weight_dtype)
bsz = latents.shape[0]
# 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.
# For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]
topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps
index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()
start_timesteps = solver.ddim_timesteps[index]
timesteps = start_timesteps - topk
timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
# 3. Get boundary scalings for start_timesteps and (end) timesteps.
c_skip_start, c_out_start = scalings_for_boundary_conditions(
start_timesteps, timestep_scaling=args.timestep_scaling_factor
)
c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]
c_skip, c_out = scalings_for_boundary_conditions(
timesteps, timestep_scaling=args.timestep_scaling_factor
)
c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]
# 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each
# timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]
noise = torch.randn_like(latents)
noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)
# 5. Sample a random guidance scale w from U[w_min, w_max]
# Note that for LCM-LoRA distillation it is not necessary to use a guidance scale embedding
w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
w = w.reshape(bsz, 1, 1, 1)
w = w.to(device=latents.device, dtype=latents.dtype)
# 6. Prepare prompt embeds and unet_added_conditions
prompt_embeds = encoded_text.pop("prompt_embeds")
# 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)
noise_pred = unet(
noisy_model_input,
start_timesteps,
timestep_cond=None,
encoder_hidden_states=prompt_embeds.float(),
added_cond_kwargs=encoded_text,
).sample
pred_x_0 = get_predicted_original_sample(
noise_pred,
start_timesteps,
noisy_model_input,
noise_scheduler.config.prediction_type,
alpha_schedule,
sigma_schedule,
)
model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
# 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the
# predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these
# estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE
# solver timestep.
with torch.no_grad():
with torch.autocast("cuda"):
# 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c
cond_teacher_output = teacher_unet(
noisy_model_input.to(weight_dtype),
start_timesteps,
encoder_hidden_states=prompt_embeds.to(weight_dtype),
).sample
cond_pred_x0 = get_predicted_original_sample(
cond_teacher_output,
start_timesteps,
noisy_model_input,
noise_scheduler.config.prediction_type,
alpha_schedule,
sigma_schedule,
)
cond_pred_noise = get_predicted_noise(
cond_teacher_output,
start_timesteps,
noisy_model_input,
noise_scheduler.config.prediction_type,
alpha_schedule,
sigma_schedule,
)
# 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0
uncond_teacher_output = teacher_unet(
noisy_model_input.to(weight_dtype),
start_timesteps,
encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),
).sample
uncond_pred_x0 = get_predicted_original_sample(
uncond_teacher_output,
start_timesteps,
noisy_model_input,
noise_scheduler.config.prediction_type,
alpha_schedule,
sigma_schedule,
)
uncond_pred_noise = get_predicted_noise(
uncond_teacher_output,
start_timesteps,
noisy_model_input,
noise_scheduler.config.prediction_type,
alpha_schedule,
sigma_schedule,
)
# 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)
# Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation
pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)
# 4. Run one step of the ODE solver to estimate the next point x_prev on the
# augmented PF-ODE trajectory (solving backward in time)
# Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.
x_prev = solver.ddim_step(pred_x0, pred_noise, index)
# 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)
# Note that we do not use a separate target network for LCM-LoRA distillation.
with torch.no_grad():
with torch.autocast("cuda", dtype=weight_dtype):
target_noise_pred = unet(
x_prev.float(),
timesteps,
timestep_cond=None,
encoder_hidden_states=prompt_embeds.float(),
).sample
pred_x_0 = get_predicted_original_sample(
target_noise_pred,
timesteps,
x_prev,
noise_scheduler.config.prediction_type,
alpha_schedule,
sigma_schedule,
)
target = c_skip * x_prev + c_out * pred_x_0
# 10. Calculate loss
if args.loss_type == "l2":
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
elif args.loss_type == "huber":
loss = torch.mean(
torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c
)
# 11. Backpropagate on the online student model (`unet`)
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if global_step % args.validation_steps == 0:
log_validation(vae, unet, args, accelerator, weight_dtype, global_step)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet.save_pretrained(args.output_dir)
lora_state_dict = get_peft_model_state_dict(unet, adapter_name="default")
StableDiffusionPipeline.save_lora_weights(os.path.join(args.output_dir, "unet_lora"), lora_state_dict)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| diffusers/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py/0 | {
"file_path": "diffusers/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py",
"repo_id": "diffusers",
"token_count": 26782
} | 102 |
# Copyright 2023 Custom Diffusion authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def retrieve(class_prompt, class_data_dir, num_class_images):
factor = 1.5
num_images = int(factor * num_class_images)
client = ClipClient(
url="https://knn.laion.ai/knn-service", indice_name="laion_400m", num_images=num_images, aesthetic_weight=0.1
)
os.makedirs(f"{class_data_dir}/images", exist_ok=True)
if len(list(Path(f"{class_data_dir}/images").iterdir())) >= num_class_images:
return
while True:
class_images = client.query(text=class_prompt)
if len(class_images) >= factor * num_class_images or num_images > 1e4:
break
else:
num_images = int(factor * num_images)
client = ClipClient(
url="https://knn.laion.ai/knn-service",
indice_name="laion_400m",
num_images=num_images,
aesthetic_weight=0.1,
)
count = 0
total = 0
pbar = tqdm(desc="downloading real regularization images", total=num_class_images)
with open(f"{class_data_dir}/caption.txt", "w") as f1, open(f"{class_data_dir}/urls.txt", "w") as f2, open(
f"{class_data_dir}/images.txt", "w"
) as f3:
while total < num_class_images:
images = class_images[count]
count += 1
try:
img = requests.get(images["url"], timeout=30)
if img.status_code == 200:
_ = Image.open(BytesIO(img.content))
with open(f"{class_data_dir}/images/{total}.jpg", "wb") as f:
f.write(img.content)
f1.write(images["caption"] + "\n")
f2.write(images["url"] + "\n")
f3.write(f"{class_data_dir}/images/{total}.jpg" + "\n")
total += 1
pbar.update(1)
else:
continue
except Exception:
continue
return
def parse_args():
parser = argparse.ArgumentParser("", add_help=False)
parser.add_argument("--class_prompt", help="text prompt to retrieve images", required=True, type=str)
parser.add_argument("--class_data_dir", help="path to save images", required=True, type=str)
parser.add_argument("--num_class_images", help="number of images to download", default=200, type=int)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| diffusers/examples/custom_diffusion/retrieve.py/0 | {
"file_path": "diffusers/examples/custom_diffusion/retrieve.py",
"repo_id": "diffusers",
"token_count": 1430
} | 103 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| diffusers/examples/inference/inpainting.py/0 | {
"file_path": "diffusers/examples/inference/inpainting.py",
"repo_id": "diffusers",
"token_count": 89
} | 104 |
# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)
[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
The `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion.
By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel.
## Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
```bash
pip install -r requirements.txt
```
## Install [ColossalAI](https://github.com/hpcaitech/ColossalAI.git)
**From PyPI**
```bash
pip install colossalai
```
**From source**
```bash
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# install colossalai
pip install .
```
## Dataset for Teyvat BLIP captions
Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion).
BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided.
The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
## Training
The argument `placement` can be `cpu`, `auto`, `cuda`, with `cpu` the GPU RAM required can be minimized to 4GB but will deceleration, with `cuda` you can also reduce GPU memory by half but accelerated training， with `auto` a more balanced solution for speed and memory can be obtained。
**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export OUTPUT_DIR="path-to-save-model"
torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400 \
--placement="cuda"
```
### Training with prior-preservation loss
Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=800 \
--placement="cuda"
```
## Inference
Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "path-to-save-model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A photo of sks dog in a bucket"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
```
| diffusers/examples/research_projects/colossalai/README.md/0 | {
"file_path": "diffusers/examples/research_projects/colossalai/README.md",
"repo_id": "diffusers",
"token_count": 1659
} | 105 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNet2DConditionModel
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m",
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"-c",
"--caption",
type=str,
default="robotic cat with wings",
help="Text used to generate images.",
)
parser.add_argument(
"-n",
"--images_num",
type=int,
default=4,
help="How much images to generate.",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=42,
help="Seed for random process.",
)
parser.add_argument(
"-ci",
"--cuda_id",
type=int,
default=0,
help="cuda_id.",
)
args = parser.parse_args()
return args
def image_grid(imgs, rows, cols):
if not len(imgs) == rows * cols:
raise ValueError("The specified number of rows and columns are not correct.")
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
grid_w, grid_h = grid.size
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def generate_images(
pipeline,
prompt="robotic cat with wings",
guidance_scale=7.5,
num_inference_steps=50,
num_images_per_prompt=1,
seed=42,
):
generator = torch.Generator(pipeline.device).manual_seed(seed)
images = pipeline(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
num_images_per_prompt=num_images_per_prompt,
).images
_rows = int(math.sqrt(num_images_per_prompt))
grid = image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)
return grid, images
args = parse_args()
# Load models and create wrapper for stable diffusion
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
pipeline.safety_checker = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
unet = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, "unet", unet)
else:
unet = unet.to(torch.device("cuda", args.cuda_id))
pipeline = pipeline.to(unet.device)
grid, images = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
dirname = os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, "{}.png".format(idx + 1)))
| diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/text2images.py/0 | {
"file_path": "diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/text2images.py",
"repo_id": "diffusers",
"token_count": 1518
} | 106 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import shutil
import sys
import tempfile
from diffusers import DiffusionPipeline, UNet2DConditionModel # noqa: E402
sys.path.append("..")
from test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class TextToImage(ExamplesTestsAccelerate):
def test_text_to_image(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
""".split()
run_command(self._launch_args + test_args)
# save_pretrained smoke test
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
def test_text_to_image_checkpointing(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 4, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 4
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4"},
)
# check can run an intermediate checkpoint
unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
# Run training script for 2 total steps resuming from checkpoint 4
resume_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=1
--resume_from_checkpoint=checkpoint-4
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
# check can run new fully trained pipeline
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-5"},
)
def test_text_to_image_checkpointing_use_ema(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 4, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 4
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--use_ema
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4"},
)
# check can run an intermediate checkpoint
unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
# Run training script for 2 total steps resuming from checkpoint 4
resume_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=1
--resume_from_checkpoint=checkpoint-4
--use_ema
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
# check can run new fully trained pipeline
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-4", "checkpoint-5"},
)
def test_text_to_image_checkpointing_checkpoints_total_limit(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2
# Should create checkpoints at steps 2, 4, 6
# with checkpoint at step 2 deleted
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 6
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# check checkpoint directories exist
# checkpoint-2 should have been deleted
self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-4", "checkpoint-6"})
def test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 4, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 4
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4"},
)
# resume and we should try to checkpoint at 6, where we'll have to remove
# checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
resume_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 8
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-4
--checkpoints_total_limit=2
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=1)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8"},
)
class TextToImageSDXL(ExamplesTestsAccelerate):
def test_text_to_image_sdxl(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/text_to_image/train_text_to_image_sdxl.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
""".split()
run_command(self._launch_args + test_args)
# save_pretrained smoke test
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
| diffusers/examples/text_to_image/test_text_to_image.py/0 | {
"file_path": "diffusers/examples/text_to_image/test_text_to_image.py",
"repo_id": "diffusers",
"token_count": 7412
} | 107 |
## Training an unconditional diffusion model
Creating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd in the example folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
### Unconditional Flowers
The command to train a DDPM UNet model on the Oxford Flowers dataset:
```bash
accelerate launch train_unconditional.py \
--dataset_name="huggan/flowers-102-categories" \
--resolution=64 --center_crop --random_flip \
--output_dir="ddpm-ema-flowers-64" \
--train_batch_size=16 \
--num_epochs=100 \
--gradient_accumulation_steps=1 \
--use_ema \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--push_to_hub
```
An example trained model: https://huggingface.co/anton-l/ddpm-ema-flowers-64
A full training run takes 2 hours on 4xV100 GPUs.
<img src="https://user-images.githubusercontent.com/26864830/180248660-a0b143d0-b89a-42c5-8656-2ebf6ece7e52.png" width="700" />
### Unconditional Pokemon
The command to train a DDPM UNet model on the Pokemon dataset:
```bash
accelerate launch train_unconditional.py \
--dataset_name="huggan/pokemon" \
--resolution=64 --center_crop --random_flip \
--output_dir="ddpm-ema-pokemon-64" \
--train_batch_size=16 \
--num_epochs=100 \
--gradient_accumulation_steps=1 \
--use_ema \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=no \
--push_to_hub
```
An example trained model: https://huggingface.co/anton-l/ddpm-ema-pokemon-64
A full training run takes 2 hours on 4xV100 GPUs.
<img src="https://user-images.githubusercontent.com/26864830/180248200-928953b4-db38-48db-b0c6-8b740fe6786f.png" width="700" />
### Training with multiple GPUs
`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
for running distributed training with `accelerate`. Here is an example command:
```bash
accelerate launch --mixed_precision="fp16" --multi_gpu train_unconditional.py \
--dataset_name="huggan/pokemon" \
--resolution=64 --center_crop --random_flip \
--output_dir="ddpm-ema-pokemon-64" \
--train_batch_size=16 \
--num_epochs=100 \
--gradient_accumulation_steps=1 \
--use_ema \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision="fp16" \
--logger="wandb"
```
To be able to use Weights and Biases (`wandb`) as a logger you need to install the library: `pip install wandb`.
### Using your own data
To use your own dataset, there are 2 ways:
- you can either provide your own folder as `--train_data_dir`
- or you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.
Below, we explain both in more detail.
#### Provide the dataset as a folder
If you provide your own folders with images, the script expects the following directory structure:
```bash
data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png
```
In other words, the script will take care of gathering all images inside the folder. You can then run the script like this:
```bash
accelerate launch train_unconditional.py \
--train_data_dir <path-to-train-directory> \
<other-arguments>
```
Internally, the script will use the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature which will automatically turn the folders into 🤗 Dataset objects.
#### Upload your data to the hub, as a (possibly private) repo
It's very easy (and convenient) to upload your image dataset to the hub using the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature available in 🤗 Datasets. Simply do the following:
```python
from datasets import load_dataset
# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip")
# example 4: providing several splits
dataset = load_dataset("imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]})
```
`ImageFolder` will create an `image` column containing the PIL-encoded images.
Next, push it to the hub!
```python
# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")
# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)
```
and that's it! You can now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the hub.
More on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).
| diffusers/examples/unconditional_image_generation/README.md/0 | {
"file_path": "diffusers/examples/unconditional_image_generation/README.md",
"repo_id": "diffusers",
"token_count": 1939
} | 108 |
import argparse
import torch
from diffusers import MotionAdapter
def convert_motion_module(original_state_dict):
converted_state_dict = {}
for k, v in original_state_dict.items():
if "pos_encoder" in k:
continue
else:
converted_state_dict[
k.replace(".norms.0", ".norm1")
.replace(".norms.1", ".norm2")
.replace(".ff_norm", ".norm3")
.replace(".attention_blocks.0", ".attn1")
.replace(".attention_blocks.1", ".attn2")
.replace(".temporal_transformer", "")
] = v
return converted_state_dict
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--ckpt_path", type=str, required=True)
parser.add_argument("--output_path", type=str, required=True)
parser.add_argument("--use_motion_mid_block", action="store_true")
parser.add_argument("--motion_max_seq_length", type=int, default=32)
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
state_dict = torch.load(args.ckpt_path, map_location="cpu")
if "state_dict" in state_dict.keys():
state_dict = state_dict["state_dict"]
conv_state_dict = convert_motion_module(state_dict)
adapter = MotionAdapter(
use_motion_mid_block=args.use_motion_mid_block, motion_max_seq_length=args.motion_max_seq_length
)
# skip loading position embeddings
adapter.load_state_dict(conv_state_dict, strict=False)
adapter.save_pretrained(args.output_path)
adapter.save_pretrained(args.output_path, variant="fp16", torch_dtype=torch.float16)
| diffusers/scripts/convert_animatediff_motion_module_to_diffusers.py/0 | {
"file_path": "diffusers/scripts/convert_animatediff_motion_module_to_diffusers.py",
"repo_id": "diffusers",
"token_count": 696
} | 109 |
#!/usr/bin/env python3
import argparse
import fnmatch
from safetensors.torch import load_file
from diffusers import Kandinsky3UNet
MAPPING = {
"to_time_embed.1": "time_embedding.linear_1",
"to_time_embed.3": "time_embedding.linear_2",
"in_layer": "conv_in",
"out_layer.0": "conv_norm_out",
"out_layer.2": "conv_out",
"down_samples": "down_blocks",
"up_samples": "up_blocks",
"projection_lin": "encoder_hid_proj.projection_linear",
"projection_ln": "encoder_hid_proj.projection_norm",
"feature_pooling": "add_time_condition",
"to_query": "to_q",
"to_key": "to_k",
"to_value": "to_v",
"output_layer": "to_out.0",
"self_attention_block": "attentions.0",
}
DYNAMIC_MAP = {
"resnet_attn_blocks.*.0": "resnets_in.*",
"resnet_attn_blocks.*.1": ("attentions.*", 1),
"resnet_attn_blocks.*.2": "resnets_out.*",
}
# MAPPING = {}
def convert_state_dict(unet_state_dict):
"""
Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.
Args:
unet_model (torch.nn.Module): The original U-Net model.
unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with.
Returns:
OrderedDict: The converted state dictionary.
"""
# Example of renaming logic (this will vary based on your model's architecture)
converted_state_dict = {}
for key in unet_state_dict:
new_key = key
for pattern, new_pattern in MAPPING.items():
new_key = new_key.replace(pattern, new_pattern)
for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items():
has_matched = False
if fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*") and not has_matched:
star = int(new_key.split(dyn_pattern.split(".")[0])[-1].split(".")[1])
if isinstance(dyn_new_pattern, tuple):
new_star = star + dyn_new_pattern[-1]
dyn_new_pattern = dyn_new_pattern[0]
else:
new_star = star
pattern = dyn_pattern.replace("*", str(star))
new_pattern = dyn_new_pattern.replace("*", str(new_star))
new_key = new_key.replace(pattern, new_pattern)
has_matched = True
converted_state_dict[new_key] = unet_state_dict[key]
return converted_state_dict
def main(model_path, output_path):
# Load your original U-Net model
unet_state_dict = load_file(model_path)
# Initialize your Kandinsky3UNet model
config = {}
# Convert the state dict
converted_state_dict = convert_state_dict(unet_state_dict)
unet = Kandinsky3UNet(config)
unet.load_state_dict(converted_state_dict)
unet.save_pretrained(output_path)
print(f"Converted model saved to {output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Convert U-Net PyTorch model to Kandinsky3UNet format")
parser.add_argument("--model_path", type=str, required=True, help="Path to the original U-Net PyTorch model")
parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model")
args = parser.parse_args()
main(args.model_path, args.output_path)
| diffusers/scripts/convert_kandinsky3_unet.py/0 | {
"file_path": "diffusers/scripts/convert_kandinsky3_unet.py",
"repo_id": "diffusers",
"token_count": 1403
} | 110 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def onnx_export(
model,
model_args: tuple,
output_path: Path,
ordered_input_names,
output_names,
dynamic_axes,
opset,
use_external_data_format=False,
):
output_path.parent.mkdir(parents=True, exist_ok=True)
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
model,
model_args,
f=output_path.as_posix(),
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
use_external_data_format=use_external_data_format,
enable_onnx_checker=True,
opset_version=opset,
)
else:
export(
model,
model_args,
f=output_path.as_posix(),
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
opset_version=opset,
)
@torch.no_grad()
def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False):
dtype = torch.float16 if fp16 else torch.float32
if fp16 and torch.cuda.is_available():
device = "cuda"
elif fp16 and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA")
else:
device = "cpu"
pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device)
output_path = Path(output_path)
# TEXT ENCODER
num_tokens = pipeline.text_encoder.config.max_position_embeddings
text_hidden_size = pipeline.text_encoder.config.hidden_size
text_input = pipeline.tokenizer(
"A sample prompt",
padding="max_length",
max_length=pipeline.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
onnx_export(
pipeline.text_encoder,
# casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files
model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)),
output_path=output_path / "text_encoder" / "model.onnx",
ordered_input_names=["input_ids"],
output_names=["last_hidden_state", "pooler_output"],
dynamic_axes={
"input_ids": {0: "batch", 1: "sequence"},
},
opset=opset,
)
del pipeline.text_encoder
# UNET
unet_in_channels = pipeline.unet.config.in_channels
unet_sample_size = pipeline.unet.config.sample_size
unet_path = output_path / "unet" / "model.onnx"
onnx_export(
pipeline.unet,
model_args=(
torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
torch.randn(2).to(device=device, dtype=dtype),
torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
False,
),
output_path=unet_path,
ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"],
output_names=["out_sample"], # has to be different from "sample" for correct tracing
dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"timestep": {0: "batch"},
"encoder_hidden_states": {0: "batch", 1: "sequence"},
},
opset=opset,
use_external_data_format=True, # UNet is > 2GB, so the weights need to be split
)
unet_model_path = str(unet_path.absolute().as_posix())
unet_dir = os.path.dirname(unet_model_path)
unet = onnx.load(unet_model_path)
# clean up existing tensor files
shutil.rmtree(unet_dir)
os.mkdir(unet_dir)
# collate external tensor files into one
onnx.save_model(
unet,
unet_model_path,
save_as_external_data=True,
all_tensors_to_one_file=True,
location="weights.pb",
convert_attribute=False,
)
del pipeline.unet
# VAE ENCODER
vae_encoder = pipeline.vae
vae_in_channels = vae_encoder.config.in_channels
vae_sample_size = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample()
onnx_export(
vae_encoder,
model_args=(
torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),
False,
),
output_path=output_path / "vae_encoder" / "model.onnx",
ordered_input_names=["sample", "return_dict"],
output_names=["latent_sample"],
dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
},
opset=opset,
)
# VAE DECODER
vae_decoder = pipeline.vae
vae_latent_channels = vae_decoder.config.latent_channels
vae_out_channels = vae_decoder.config.out_channels
# forward only through the decoder part
vae_decoder.forward = vae_encoder.decode
onnx_export(
vae_decoder,
model_args=(
torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
False,
),
output_path=output_path / "vae_decoder" / "model.onnx",
ordered_input_names=["latent_sample", "return_dict"],
output_names=["sample"],
dynamic_axes={
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
},
opset=opset,
)
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
safety_checker = pipeline.safety_checker
clip_num_channels = safety_checker.config.vision_config.num_channels
clip_image_size = safety_checker.config.vision_config.image_size
safety_checker.forward = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker,
model_args=(
torch.randn(
1,
clip_num_channels,
clip_image_size,
clip_image_size,
).to(device=device, dtype=dtype),
torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(device=device, dtype=dtype),
),
output_path=output_path / "safety_checker" / "model.onnx",
ordered_input_names=["clip_input", "images"],
output_names=["out_images", "has_nsfw_concepts"],
dynamic_axes={
"clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
},
opset=opset,
)
del pipeline.safety_checker
safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
feature_extractor = pipeline.feature_extractor
else:
safety_checker = None
feature_extractor = None
onnx_pipeline = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),
vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"),
text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),
tokenizer=pipeline.tokenizer,
unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
scheduler=pipeline.scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
requires_safety_checker=safety_checker is not None,
)
onnx_pipeline.save_pretrained(output_path)
print("ONNX pipeline saved to", output_path)
del pipeline
del onnx_pipeline
_ = OnnxStableDiffusionPipeline.from_pretrained(output_path, provider="CPUExecutionProvider")
print("ONNX pipeline is loadable")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
args = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fp16)
| diffusers/scripts/convert_stable_diffusion_checkpoint_to_onnx.py/0 | {
"file_path": "diffusers/scripts/convert_stable_diffusion_checkpoint_to_onnx.py",
"repo_id": "diffusers",
"token_count": 4385
} | 111 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import os
from contextlib import nullcontext
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union
import safetensors
import torch
from huggingface_hub import model_info
from huggingface_hub.constants import HF_HUB_OFFLINE
from huggingface_hub.utils import validate_hf_hub_args
from packaging import version
from torch import nn
from .. import __version__
from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
from ..utils import (
USE_PEFT_BACKEND,
_get_model_file,
convert_state_dict_to_diffusers,
convert_state_dict_to_peft,
convert_unet_state_dict_to_peft,
delete_adapter_layers,
deprecate,
get_adapter_name,
get_peft_kwargs,
is_accelerate_available,
is_transformers_available,
logging,
recurse_remove_peft_layers,
scale_lora_layers,
set_adapter_layers,
set_weights_and_activate_adapters,
)
from .lora_conversion_utils import _convert_kohya_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers
if is_transformers_available():
from transformers import PreTrainedModel
from ..models.lora import PatchedLoraProjection, text_encoder_attn_modules, text_encoder_mlp_modules
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
logger = logging.get_logger(__name__)
TEXT_ENCODER_NAME = "text_encoder"
UNET_NAME = "unet"
TRANSFORMER_NAME = "transformer"
LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"
LORA_DEPRECATION_MESSAGE = "You are using an old version of LoRA backend. This will be deprecated in the next releases in favor of PEFT make sure to install the latest PEFT and transformers packages in the future."
class LoraLoaderMixin:
r"""
Load LoRA layers into [`UNet2DConditionModel`] and
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
"""
text_encoder_name = TEXT_ENCODER_NAME
unet_name = UNET_NAME
transformer_name = TRANSFORMER_NAME
num_fused_loras = 0
def load_lora_weights(
self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
`self.text_encoder`.
All kwargs are forwarded to `self.lora_state_dict`.
See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into
`self.unet`.
See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded
into `self.text_encoder`.
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
kwargs (`dict`, *optional*):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
"""
# First, ensure that the checkpoint is a compatible one and can be successfully loaded.
state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
is_correct_format = all("lora" in key for key in state_dict.keys())
if not is_correct_format:
raise ValueError("Invalid LoRA checkpoint.")
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
self.load_lora_into_unet(
state_dict,
network_alphas=network_alphas,
unet=getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet,
low_cpu_mem_usage=low_cpu_mem_usage,
adapter_name=adapter_name,
_pipeline=self,
)
self.load_lora_into_text_encoder(
state_dict,
network_alphas=network_alphas,
text_encoder=getattr(self, self.text_encoder_name)
if not hasattr(self, "text_encoder")
else self.text_encoder,
lora_scale=self.lora_scale,
low_cpu_mem_usage=low_cpu_mem_usage,
adapter_name=adapter_name,
_pipeline=self,
)
@classmethod
@validate_hf_hub_args
def lora_state_dict(
cls,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
**kwargs,
):
r"""
Return state dict for lora weights and the network alphas.
<Tip warning={true}>
We support loading A1111 formatted LoRA checkpoints in a limited capacity.
This function is experimental and might change in the future.
</Tip>
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either:
- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
with [`ModelMixin.save_pretrained`].
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
incompletely downloaded files are deleted.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
mirror (`str`, *optional*):
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.
"""
# Load the main state dict first which has the LoRA layers for either of
# UNet and text encoder or both.
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
weight_name = kwargs.pop("weight_name", None)
unet_config = kwargs.pop("unet_config", None)
use_safetensors = kwargs.pop("use_safetensors", None)
allow_pickle = False
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
model_file = None
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
# Let's first try to load .safetensors weights
if (use_safetensors and weight_name is None) or (
weight_name is not None and weight_name.endswith(".safetensors")
):
try:
# Here we're relaxing the loading check to enable more Inference API
# friendliness where sometimes, it's not at all possible to automatically
# determine `weight_name`.
if weight_name is None:
weight_name = cls._best_guess_weight_name(
pretrained_model_name_or_path_or_dict,
file_extension=".safetensors",
local_files_only=local_files_only,
)
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = safetensors.torch.load_file(model_file, device="cpu")
except (IOError, safetensors.SafetensorError) as e:
if not allow_pickle:
raise e
# try loading non-safetensors weights
model_file = None
pass
if model_file is None:
if weight_name is None:
weight_name = cls._best_guess_weight_name(
pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
)
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = torch.load(model_file, map_location="cpu")
else:
state_dict = pretrained_model_name_or_path_or_dict
network_alphas = None
# TODO: replace it with a method from `state_dict_utils`
if all(
(
k.startswith("lora_te_")
or k.startswith("lora_unet_")
or k.startswith("lora_te1_")
or k.startswith("lora_te2_")
)
for k in state_dict.keys()
):
# Map SDXL blocks correctly.
if unet_config is not None:
# use unet config to remap block numbers
state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
state_dict, network_alphas = _convert_kohya_lora_to_diffusers(state_dict)
return state_dict, network_alphas
@classmethod
def _best_guess_weight_name(
cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
):
if local_files_only or HF_HUB_OFFLINE:
raise ValueError("When using the offline mode, you must specify a `weight_name`.")
targeted_files = []
if os.path.isfile(pretrained_model_name_or_path_or_dict):
return
elif os.path.isdir(pretrained_model_name_or_path_or_dict):
targeted_files = [
f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)
]
else:
files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
if len(targeted_files) == 0:
return
# "scheduler" does not correspond to a LoRA checkpoint.
# "optimizer" does not correspond to a LoRA checkpoint
# only top-level checkpoints are considered and not the other ones, hence "checkpoint".
unallowed_substrings = {"scheduler", "optimizer", "checkpoint"}
targeted_files = list(
filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)
)
if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):
targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))
elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):
targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))
if len(targeted_files) > 1:
raise ValueError(
f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one `.safetensors` or `.bin` file in {pretrained_model_name_or_path_or_dict}."
)
weight_name = targeted_files[0]
return weight_name
@classmethod
def _optionally_disable_offloading(cls, _pipeline):
"""
Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
Args:
_pipeline (`DiffusionPipeline`):
The pipeline to disable offloading for.
Returns:
tuple:
A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
"""
is_model_cpu_offload = False
is_sequential_cpu_offload = False
if _pipeline is not None:
for _, component in _pipeline.components.items():
if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
if not is_model_cpu_offload:
is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
if not is_sequential_cpu_offload:
is_sequential_cpu_offload = isinstance(component._hf_hook, AlignDevicesHook)
logger.info(
"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
return (is_model_cpu_offload, is_sequential_cpu_offload)
@classmethod
def load_lora_into_unet(
cls, state_dict, network_alphas, unet, low_cpu_mem_usage=None, adapter_name=None, _pipeline=None
):
"""
This will load the LoRA layers specified in `state_dict` into `unet`.
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional `unet` which can be used to distinguish between text
encoder lora layers.
network_alphas (`Dict[str, float]`):
See `LoRALinearLayer` for more details.
unet (`UNet2DConditionModel`):
The UNet model to load the LoRA layers into.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
"""
low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
# If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
# then the `state_dict` keys should have `cls.unet_name` and/or `cls.text_encoder_name` as
# their prefixes.
keys = list(state_dict.keys())
if all(key.startswith("unet.unet") for key in keys):
deprecation_message = "Keys starting with 'unet.unet' are deprecated."
deprecate("unet.unet keys", "0.27", deprecation_message)
if all(key.startswith(cls.unet_name) or key.startswith(cls.text_encoder_name) for key in keys):
# Load the layers corresponding to UNet.
logger.info(f"Loading {cls.unet_name}.")
unet_keys = [k for k in keys if k.startswith(cls.unet_name)]
state_dict = {k.replace(f"{cls.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys}
if network_alphas is not None:
alpha_keys = [k for k in network_alphas.keys() if k.startswith(cls.unet_name)]
network_alphas = {
k.replace(f"{cls.unet_name}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
}
else:
# Otherwise, we're dealing with the old format. This means the `state_dict` should only
# contain the module names of the `unet` as its keys WITHOUT any prefix.
if not USE_PEFT_BACKEND:
warn_message = "You have saved the LoRA weights using the old format. To convert the old LoRA weights to the new format, you can first load them in a dictionary and then create a new dictionary like the following: `new_state_dict = {f'unet.{module_name}': params for module_name, params in old_state_dict.items()}`."
logger.warn(warn_message)
if USE_PEFT_BACKEND and len(state_dict.keys()) > 0:
from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
if adapter_name in getattr(unet, "peft_config", {}):
raise ValueError(
f"Adapter name {adapter_name} already in use in the Unet - please select a new adapter name."
)
state_dict = convert_unet_state_dict_to_peft(state_dict)
if network_alphas is not None:
# The alphas state dict have the same structure as Unet, thus we convert it to peft format using
# `convert_unet_state_dict_to_peft` method.
network_alphas = convert_unet_state_dict_to_peft(network_alphas)
rank = {}
for key, val in state_dict.items():
if "lora_B" in key:
rank[key] = val.shape[1]
lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, is_unet=True)
lora_config = LoraConfig(**lora_config_kwargs)
# adapter_name
if adapter_name is None:
adapter_name = get_adapter_name(unet)
# In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
# otherwise loading LoRA weights will lead to an error
is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
inject_adapter_in_model(lora_config, unet, adapter_name=adapter_name)
incompatible_keys = set_peft_model_state_dict(unet, state_dict, adapter_name)
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
logger.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
# Offload back.
if is_model_cpu_offload:
_pipeline.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
_pipeline.enable_sequential_cpu_offload()
# Unsafe code />
unet.load_attn_procs(
state_dict, network_alphas=network_alphas, low_cpu_mem_usage=low_cpu_mem_usage, _pipeline=_pipeline
)
@classmethod
def load_lora_into_text_encoder(
cls,
state_dict,
network_alphas,
text_encoder,
prefix=None,
lora_scale=1.0,
low_cpu_mem_usage=None,
adapter_name=None,
_pipeline=None,
):
"""
This will load the LoRA layers specified in `state_dict` into `text_encoder`
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional `text_encoder` to distinguish between unet lora layers.
network_alphas (`Dict[str, float]`):
See `LoRALinearLayer` for more details.
text_encoder (`CLIPTextModel`):
The text encoder model to load the LoRA layers into.
prefix (`str`):
Expected prefix of the `text_encoder` in the `state_dict`.
lora_scale (`float`):
How much to scale the output of the lora linear layer before it is added with the output of the regular
lora layer.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
"""
low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
# If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
# then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
# their prefixes.
keys = list(state_dict.keys())
prefix = cls.text_encoder_name if prefix is None else prefix
# Safe prefix to check with.
if any(cls.text_encoder_name in key for key in keys):
# Load the layers corresponding to text encoder and make necessary adjustments.
text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
text_encoder_lora_state_dict = {
k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
}
if len(text_encoder_lora_state_dict) > 0:
logger.info(f"Loading {prefix}.")
rank = {}
text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
if USE_PEFT_BACKEND:
# convert state dict
text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
for name, _ in text_encoder_attn_modules(text_encoder):
rank_key = f"{name}.out_proj.lora_B.weight"
rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]
patch_mlp = any(".mlp." in key for key in text_encoder_lora_state_dict.keys())
if patch_mlp:
for name, _ in text_encoder_mlp_modules(text_encoder):
rank_key_fc1 = f"{name}.fc1.lora_B.weight"
rank_key_fc2 = f"{name}.fc2.lora_B.weight"
rank[rank_key_fc1] = text_encoder_lora_state_dict[rank_key_fc1].shape[1]
rank[rank_key_fc2] = text_encoder_lora_state_dict[rank_key_fc2].shape[1]
else:
for name, _ in text_encoder_attn_modules(text_encoder):
rank_key = f"{name}.out_proj.lora_linear_layer.up.weight"
rank.update({rank_key: text_encoder_lora_state_dict[rank_key].shape[1]})
patch_mlp = any(".mlp." in key for key in text_encoder_lora_state_dict.keys())
if patch_mlp:
for name, _ in text_encoder_mlp_modules(text_encoder):
rank_key_fc1 = f"{name}.fc1.lora_linear_layer.up.weight"
rank_key_fc2 = f"{name}.fc2.lora_linear_layer.up.weight"
rank[rank_key_fc1] = text_encoder_lora_state_dict[rank_key_fc1].shape[1]
rank[rank_key_fc2] = text_encoder_lora_state_dict[rank_key_fc2].shape[1]
if network_alphas is not None:
alpha_keys = [
k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
]
network_alphas = {
k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
}
if USE_PEFT_BACKEND:
from peft import LoraConfig
lora_config_kwargs = get_peft_kwargs(
rank, network_alphas, text_encoder_lora_state_dict, is_unet=False
)
lora_config = LoraConfig(**lora_config_kwargs)
# adapter_name
if adapter_name is None:
adapter_name = get_adapter_name(text_encoder)
is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
# inject LoRA layers and load the state dict
# in transformers we automatically check whether the adapter name is already in use or not
text_encoder.load_adapter(
adapter_name=adapter_name,
adapter_state_dict=text_encoder_lora_state_dict,
peft_config=lora_config,
)
# scale LoRA layers with `lora_scale`
scale_lora_layers(text_encoder, weight=lora_scale)
else:
cls._modify_text_encoder(
text_encoder,
lora_scale,
network_alphas,
rank=rank,
patch_mlp=patch_mlp,
low_cpu_mem_usage=low_cpu_mem_usage,
)
is_pipeline_offloaded = _pipeline is not None and any(
isinstance(c, torch.nn.Module) and hasattr(c, "_hf_hook")
for c in _pipeline.components.values()
)
if is_pipeline_offloaded and low_cpu_mem_usage:
low_cpu_mem_usage = True
logger.info(
f"Pipeline {_pipeline.__class__} is offloaded. Therefore low cpu mem usage loading is forced."
)
if low_cpu_mem_usage:
device = next(iter(text_encoder_lora_state_dict.values())).device
dtype = next(iter(text_encoder_lora_state_dict.values())).dtype
unexpected_keys = load_model_dict_into_meta(
text_encoder, text_encoder_lora_state_dict, device=device, dtype=dtype
)
else:
load_state_dict_results = text_encoder.load_state_dict(
text_encoder_lora_state_dict, strict=False
)
unexpected_keys = load_state_dict_results.unexpected_keys
if len(unexpected_keys) != 0:
raise ValueError(
f"failed to load text encoder state dict, unexpected keys: {load_state_dict_results.unexpected_keys}"
)
# <Unsafe code
# We can be sure that the following works as all we do is change the dtype and device of the text encoder
# Now we remove any existing hooks to
is_model_cpu_offload = False
is_sequential_cpu_offload = False
if _pipeline is not None:
for _, component in _pipeline.components.items():
if isinstance(component, torch.nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
is_sequential_cpu_offload = isinstance(
getattr(component, "_hf_hook"), AlignDevicesHook
)
logger.info(
"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)
# Offload back.
if is_model_cpu_offload:
_pipeline.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
_pipeline.enable_sequential_cpu_offload()
# Unsafe code />
@classmethod
def load_lora_into_transformer(
cls, state_dict, network_alphas, transformer, low_cpu_mem_usage=None, adapter_name=None, _pipeline=None
):
"""
This will load the LoRA layers specified in `state_dict` into `transformer`.
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional `unet` which can be used to distinguish between text
encoder lora layers.
network_alphas (`Dict[str, float]`):
See `LoRALinearLayer` for more details.
unet (`UNet2DConditionModel`):
The UNet model to load the LoRA layers into.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
"""
low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
keys = list(state_dict.keys())
transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
state_dict = {
k.replace(f"{cls.transformer_name}.", ""): v for k, v in state_dict.items() if k in transformer_keys
}
if network_alphas is not None:
alpha_keys = [k for k in network_alphas.keys() if k.startswith(cls.transformer_name)]
network_alphas = {
k.replace(f"{cls.transformer_name}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
}
if len(state_dict.keys()) > 0:
from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
if adapter_name in getattr(transformer, "peft_config", {}):
raise ValueError(
f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
)
rank = {}
for key, val in state_dict.items():
if "lora_B" in key:
rank[key] = val.shape[1]
lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict)
lora_config = LoraConfig(**lora_config_kwargs)
# adapter_name
if adapter_name is None:
adapter_name = get_adapter_name(transformer)
# In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
# otherwise loading LoRA weights will lead to an error
is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)
incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
logger.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
# Offload back.
if is_model_cpu_offload:
_pipeline.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
_pipeline.enable_sequential_cpu_offload()
# Unsafe code />
@property
def lora_scale(self) -> float:
# property function that returns the lora scale which can be set at run time by the pipeline.
# if _lora_scale has not been set, return 1
return self._lora_scale if hasattr(self, "_lora_scale") else 1.0
def _remove_text_encoder_monkey_patch(self):
if USE_PEFT_BACKEND:
remove_method = recurse_remove_peft_layers
else:
remove_method = self._remove_text_encoder_monkey_patch_classmethod
if hasattr(self, "text_encoder"):
remove_method(self.text_encoder)
# In case text encoder have no Lora attached
if USE_PEFT_BACKEND and getattr(self.text_encoder, "peft_config", None) is not None:
del self.text_encoder.peft_config
self.text_encoder._hf_peft_config_loaded = None
if hasattr(self, "text_encoder_2"):
remove_method(self.text_encoder_2)
if USE_PEFT_BACKEND:
del self.text_encoder_2.peft_config
self.text_encoder_2._hf_peft_config_loaded = None
@classmethod
def _remove_text_encoder_monkey_patch_classmethod(cls, text_encoder):
deprecate("_remove_text_encoder_monkey_patch_classmethod", "0.27", LORA_DEPRECATION_MESSAGE)
for _, attn_module in text_encoder_attn_modules(text_encoder):
if isinstance(attn_module.q_proj, PatchedLoraProjection):
attn_module.q_proj.lora_linear_layer = None
attn_module.k_proj.lora_linear_layer = None
attn_module.v_proj.lora_linear_layer = None
attn_module.out_proj.lora_linear_layer = None
for _, mlp_module in text_encoder_mlp_modules(text_encoder):
if isinstance(mlp_module.fc1, PatchedLoraProjection):
mlp_module.fc1.lora_linear_layer = None
mlp_module.fc2.lora_linear_layer = None
@classmethod
def _modify_text_encoder(
cls,
text_encoder,
lora_scale=1,
network_alphas=None,
rank: Union[Dict[str, int], int] = 4,
dtype=None,
patch_mlp=False,
low_cpu_mem_usage=False,
):
r"""
Monkey-patches the forward passes of attention modules of the text encoder.
"""
deprecate("_modify_text_encoder", "0.27", LORA_DEPRECATION_MESSAGE)
def create_patched_linear_lora(model, network_alpha, rank, dtype, lora_parameters):
linear_layer = model.regular_linear_layer if isinstance(model, PatchedLoraProjection) else model
ctx = init_empty_weights if low_cpu_mem_usage else nullcontext
with ctx():
model = PatchedLoraProjection(linear_layer, lora_scale, network_alpha, rank, dtype=dtype)
lora_parameters.extend(model.lora_linear_layer.parameters())
return model
# First, remove any monkey-patch that might have been applied before
cls._remove_text_encoder_monkey_patch_classmethod(text_encoder)
lora_parameters = []
network_alphas = {} if network_alphas is None else network_alphas
is_network_alphas_populated = len(network_alphas) > 0
for name, attn_module in text_encoder_attn_modules(text_encoder):
query_alpha = network_alphas.pop(name + ".to_q_lora.down.weight.alpha", None)
key_alpha = network_alphas.pop(name + ".to_k_lora.down.weight.alpha", None)
value_alpha = network_alphas.pop(name + ".to_v_lora.down.weight.alpha", None)
out_alpha = network_alphas.pop(name + ".to_out_lora.down.weight.alpha", None)
if isinstance(rank, dict):
current_rank = rank.pop(f"{name}.out_proj.lora_linear_layer.up.weight")
else:
current_rank = rank
attn_module.q_proj = create_patched_linear_lora(
attn_module.q_proj, query_alpha, current_rank, dtype, lora_parameters
)
attn_module.k_proj = create_patched_linear_lora(
attn_module.k_proj, key_alpha, current_rank, dtype, lora_parameters
)
attn_module.v_proj = create_patched_linear_lora(
attn_module.v_proj, value_alpha, current_rank, dtype, lora_parameters
)
attn_module.out_proj = create_patched_linear_lora(
attn_module.out_proj, out_alpha, current_rank, dtype, lora_parameters
)
if patch_mlp:
for name, mlp_module in text_encoder_mlp_modules(text_encoder):
fc1_alpha = network_alphas.pop(name + ".fc1.lora_linear_layer.down.weight.alpha", None)
fc2_alpha = network_alphas.pop(name + ".fc2.lora_linear_layer.down.weight.alpha", None)
current_rank_fc1 = rank.pop(f"{name}.fc1.lora_linear_layer.up.weight")
current_rank_fc2 = rank.pop(f"{name}.fc2.lora_linear_layer.up.weight")
mlp_module.fc1 = create_patched_linear_lora(
mlp_module.fc1, fc1_alpha, current_rank_fc1, dtype, lora_parameters
)
mlp_module.fc2 = create_patched_linear_lora(
mlp_module.fc2, fc2_alpha, current_rank_fc2, dtype, lora_parameters
)
if is_network_alphas_populated and len(network_alphas) > 0:
raise ValueError(
f"The `network_alphas` has to be empty at this point but has the following keys \n\n {', '.join(network_alphas.keys())}"
)
return lora_parameters
@classmethod
def save_lora_weights(
cls,
save_directory: Union[str, os.PathLike],
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
transformer_lora_layers: Dict[str, torch.nn.Module] = None,
is_main_process: bool = True,
weight_name: str = None,
save_function: Callable = None,
safe_serialization: bool = True,
):
r"""
Save the LoRA parameters corresponding to the UNet and text encoder.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save LoRA parameters to. Will be created if it doesn't exist.
unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `unet`.
text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
encoder LoRA state dict because it comes from 🤗 Transformers.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
"""
state_dict = {}
def pack_weights(layers, prefix):
layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
return layers_state_dict
if not (unet_lora_layers or text_encoder_lora_layers or transformer_lora_layers):
raise ValueError(
"You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers`, or `transformer_lora_layers`."
)
if unet_lora_layers:
state_dict.update(pack_weights(unet_lora_layers, cls.unet_name))
if text_encoder_lora_layers:
state_dict.update(pack_weights(text_encoder_lora_layers, cls.text_encoder_name))
if transformer_lora_layers:
state_dict.update(pack_weights(transformer_lora_layers, "transformer"))
# Save the model
cls.write_lora_layers(
state_dict=state_dict,
save_directory=save_directory,
is_main_process=is_main_process,
weight_name=weight_name,
save_function=save_function,
safe_serialization=safe_serialization,
)
@staticmethod
def write_lora_layers(
state_dict: Dict[str, torch.Tensor],
save_directory: str,
is_main_process: bool,
weight_name: str,
save_function: Callable,
safe_serialization: bool,
):
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
if save_function is None:
if safe_serialization:
def save_function(weights, filename):
return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})
else:
save_function = torch.save
os.makedirs(save_directory, exist_ok=True)
if weight_name is None:
if safe_serialization:
weight_name = LORA_WEIGHT_NAME_SAFE
else:
weight_name = LORA_WEIGHT_NAME
save_path = Path(save_directory, weight_name).as_posix()
save_function(state_dict, save_path)
logger.info(f"Model weights saved in {save_path}")
def unload_lora_weights(self):
"""
Unloads the LoRA parameters.
Examples:
```python
>>> # Assuming `pipeline` is already loaded with the LoRA parameters.
>>> pipeline.unload_lora_weights()
>>> ...
```
"""
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
if not USE_PEFT_BACKEND:
if version.parse(__version__) > version.parse("0.23"):
logger.warning(
"You are using `unload_lora_weights` to disable and unload lora weights. If you want to iteratively enable and disable adapter weights,"
"you can use `pipe.enable_lora()` or `pipe.disable_lora()`. After installing the latest version of PEFT."
)
for _, module in unet.named_modules():
if hasattr(module, "set_lora_layer"):
module.set_lora_layer(None)
else:
recurse_remove_peft_layers(unet)
if hasattr(unet, "peft_config"):
del unet.peft_config
# Safe to call the following regardless of LoRA.
self._remove_text_encoder_monkey_patch()
def fuse_lora(
self,
fuse_unet: bool = True,
fuse_text_encoder: bool = True,
lora_scale: float = 1.0,
safe_fusing: bool = False,
adapter_names: Optional[List[str]] = None,
):
r"""
Fuses the LoRA parameters into the original parameters of the corresponding blocks.
<Tip warning={true}>
This is an experimental API.
</Tip>
Args:
fuse_unet (`bool`, defaults to `True`): Whether to fuse the UNet LoRA parameters.
fuse_text_encoder (`bool`, defaults to `True`):
Whether to fuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
LoRA parameters then it won't have any effect.
lora_scale (`float`, defaults to 1.0):
Controls how much to influence the outputs with the LoRA parameters.
safe_fusing (`bool`, defaults to `False`):
Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
adapter_names (`List[str]`, *optional*):
Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.fuse_lora(lora_scale=0.7)
```
"""
if fuse_unet or fuse_text_encoder:
self.num_fused_loras += 1
if self.num_fused_loras > 1:
logger.warn(
"The current API is supported for operating with a single LoRA file. You are trying to load and fuse more than one LoRA which is not well-supported.",
)
if fuse_unet:
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
unet.fuse_lora(lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names)
if USE_PEFT_BACKEND:
from peft.tuners.tuners_utils import BaseTunerLayer
def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, adapter_names=None):
merge_kwargs = {"safe_merge": safe_fusing}
for module in text_encoder.modules():
if isinstance(module, BaseTunerLayer):
if lora_scale != 1.0:
module.scale_layer(lora_scale)
# For BC with previous PEFT versions, we need to check the signature
# of the `merge` method to see if it supports the `adapter_names` argument.
supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
if "adapter_names" in supported_merge_kwargs:
merge_kwargs["adapter_names"] = adapter_names
elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
raise ValueError(
"The `adapter_names` argument is not supported with your PEFT version. "
"Please upgrade to the latest version of PEFT. `pip install -U peft`"
)
module.merge(**merge_kwargs)
else:
deprecate("fuse_text_encoder_lora", "0.27", LORA_DEPRECATION_MESSAGE)
def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, **kwargs):
if "adapter_names" in kwargs and kwargs["adapter_names"] is not None:
raise ValueError(
"The `adapter_names` argument is not supported in your environment. Please switch to PEFT "
"backend to use this argument by installing latest PEFT and transformers."
" `pip install -U peft transformers`"
)
for _, attn_module in text_encoder_attn_modules(text_encoder):
if isinstance(attn_module.q_proj, PatchedLoraProjection):
attn_module.q_proj._fuse_lora(lora_scale, safe_fusing)
attn_module.k_proj._fuse_lora(lora_scale, safe_fusing)
attn_module.v_proj._fuse_lora(lora_scale, safe_fusing)
attn_module.out_proj._fuse_lora(lora_scale, safe_fusing)
for _, mlp_module in text_encoder_mlp_modules(text_encoder):
if isinstance(mlp_module.fc1, PatchedLoraProjection):
mlp_module.fc1._fuse_lora(lora_scale, safe_fusing)
mlp_module.fc2._fuse_lora(lora_scale, safe_fusing)
if fuse_text_encoder:
if hasattr(self, "text_encoder"):
fuse_text_encoder_lora(self.text_encoder, lora_scale, safe_fusing, adapter_names=adapter_names)
if hasattr(self, "text_encoder_2"):
fuse_text_encoder_lora(self.text_encoder_2, lora_scale, safe_fusing, adapter_names=adapter_names)
def unfuse_lora(self, unfuse_unet: bool = True, unfuse_text_encoder: bool = True):
r"""
Reverses the effect of
[`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora).
<Tip warning={true}>
This is an experimental API.
</Tip>
Args:
unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
unfuse_text_encoder (`bool`, defaults to `True`):
Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
LoRA parameters then it won't have any effect.
"""
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
if unfuse_unet:
if not USE_PEFT_BACKEND:
unet.unfuse_lora()
else:
from peft.tuners.tuners_utils import BaseTunerLayer
for module in unet.modules():
if isinstance(module, BaseTunerLayer):
module.unmerge()
if USE_PEFT_BACKEND:
from peft.tuners.tuners_utils import BaseTunerLayer
def unfuse_text_encoder_lora(text_encoder):
for module in text_encoder.modules():
if isinstance(module, BaseTunerLayer):
module.unmerge()
else:
deprecate("unfuse_text_encoder_lora", "0.27", LORA_DEPRECATION_MESSAGE)
def unfuse_text_encoder_lora(text_encoder):
for _, attn_module in text_encoder_attn_modules(text_encoder):
if isinstance(attn_module.q_proj, PatchedLoraProjection):
attn_module.q_proj._unfuse_lora()
attn_module.k_proj._unfuse_lora()
attn_module.v_proj._unfuse_lora()
attn_module.out_proj._unfuse_lora()
for _, mlp_module in text_encoder_mlp_modules(text_encoder):
if isinstance(mlp_module.fc1, PatchedLoraProjection):
mlp_module.fc1._unfuse_lora()
mlp_module.fc2._unfuse_lora()
if unfuse_text_encoder:
if hasattr(self, "text_encoder"):
unfuse_text_encoder_lora(self.text_encoder)
if hasattr(self, "text_encoder_2"):
unfuse_text_encoder_lora(self.text_encoder_2)
self.num_fused_loras -= 1
def set_adapters_for_text_encoder(
self,
adapter_names: Union[List[str], str],
text_encoder: Optional["PreTrainedModel"] = None, # noqa: F821
text_encoder_weights: List[float] = None,
):
"""
Sets the adapter layers for the text encoder.
Args:
adapter_names (`List[str]` or `str`):
The names of the adapters to use.
text_encoder (`torch.nn.Module`, *optional*):
The text encoder module to set the adapter layers for. If `None`, it will try to get the `text_encoder`
attribute.
text_encoder_weights (`List[float]`, *optional*):
The weights to use for the text encoder. If `None`, the weights are set to `1.0` for all the adapters.
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
def process_weights(adapter_names, weights):
if weights is None:
weights = [1.0] * len(adapter_names)
elif isinstance(weights, float):
weights = [weights]
if len(adapter_names) != len(weights):
raise ValueError(
f"Length of adapter names {len(adapter_names)} is not equal to the length of the weights {len(weights)}"
)
return weights
adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names
text_encoder_weights = process_weights(adapter_names, text_encoder_weights)
text_encoder = text_encoder or getattr(self, "text_encoder", None)
if text_encoder is None:
raise ValueError(
"The pipeline does not have a default `pipe.text_encoder` class. Please make sure to pass a `text_encoder` instead."
)
set_weights_and_activate_adapters(text_encoder, adapter_names, text_encoder_weights)
def disable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
"""
Disables the LoRA layers for the text encoder.
Args:
text_encoder (`torch.nn.Module`, *optional*):
The text encoder module to disable the LoRA layers for. If `None`, it will try to get the
`text_encoder` attribute.
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
text_encoder = text_encoder or getattr(self, "text_encoder", None)
if text_encoder is None:
raise ValueError("Text Encoder not found.")
set_adapter_layers(text_encoder, enabled=False)
def enable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
"""
Enables the LoRA layers for the text encoder.
Args:
text_encoder (`torch.nn.Module`, *optional*):
The text encoder module to enable the LoRA layers for. If `None`, it will try to get the `text_encoder`
attribute.
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
text_encoder = text_encoder or getattr(self, "text_encoder", None)
if text_encoder is None:
raise ValueError("Text Encoder not found.")
set_adapter_layers(self.text_encoder, enabled=True)
def set_adapters(
self,
adapter_names: Union[List[str], str],
adapter_weights: Optional[List[float]] = None,
):
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
# Handle the UNET
unet.set_adapters(adapter_names, adapter_weights)
# Handle the Text Encoder
if hasattr(self, "text_encoder"):
self.set_adapters_for_text_encoder(adapter_names, self.text_encoder, adapter_weights)
if hasattr(self, "text_encoder_2"):
self.set_adapters_for_text_encoder(adapter_names, self.text_encoder_2, adapter_weights)
def disable_lora(self):
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
# Disable unet adapters
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
unet.disable_lora()
# Disable text encoder adapters
if hasattr(self, "text_encoder"):
self.disable_lora_for_text_encoder(self.text_encoder)
if hasattr(self, "text_encoder_2"):
self.disable_lora_for_text_encoder(self.text_encoder_2)
def enable_lora(self):
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
# Enable unet adapters
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
unet.enable_lora()
# Enable text encoder adapters
if hasattr(self, "text_encoder"):
self.enable_lora_for_text_encoder(self.text_encoder)
if hasattr(self, "text_encoder_2"):
self.enable_lora_for_text_encoder(self.text_encoder_2)
def delete_adapters(self, adapter_names: Union[List[str], str]):
"""
Args:
Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s).
adapter_names (`Union[List[str], str]`):
The names of the adapter to delete. Can be a single string or a list of strings
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
if isinstance(adapter_names, str):
adapter_names = [adapter_names]
# Delete unet adapters
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
unet.delete_adapters(adapter_names)
for adapter_name in adapter_names:
# Delete text encoder adapters
if hasattr(self, "text_encoder"):
delete_adapter_layers(self.text_encoder, adapter_name)
if hasattr(self, "text_encoder_2"):
delete_adapter_layers(self.text_encoder_2, adapter_name)
def get_active_adapters(self) -> List[str]:
"""
Gets the list of the current active adapters.
Example:
```python
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
).to("cuda")
pipeline.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
pipeline.get_active_adapters()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError(
"PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`"
)
from peft.tuners.tuners_utils import BaseTunerLayer
active_adapters = []
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
for module in unet.modules():
if isinstance(module, BaseTunerLayer):
active_adapters = module.active_adapters
break
return active_adapters
def get_list_adapters(self) -> Dict[str, List[str]]:
"""
Gets the current list of all available adapters in the pipeline.
"""
if not USE_PEFT_BACKEND:
raise ValueError(
"PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`"
)
set_adapters = {}
if hasattr(self, "text_encoder") and hasattr(self.text_encoder, "peft_config"):
set_adapters["text_encoder"] = list(self.text_encoder.peft_config.keys())
if hasattr(self, "text_encoder_2") and hasattr(self.text_encoder_2, "peft_config"):
set_adapters["text_encoder_2"] = list(self.text_encoder_2.peft_config.keys())
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
if hasattr(self, self.unet_name) and hasattr(unet, "peft_config"):
set_adapters[self.unet_name] = list(self.unet.peft_config.keys())
return set_adapters
def set_lora_device(self, adapter_names: List[str], device: Union[torch.device, str, int]) -> None:
"""
Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case
you want to load multiple adapters and free some GPU memory.
Args:
adapter_names (`List[str]`):
List of adapters to send device to.
device (`Union[torch.device, str, int]`):
Device to send the adapters to. Can be either a torch device, a str or an integer.
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
from peft.tuners.tuners_utils import BaseTunerLayer
# Handle the UNET
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
for unet_module in unet.modules():
if isinstance(unet_module, BaseTunerLayer):
for adapter_name in adapter_names:
unet_module.lora_A[adapter_name].to(device)
unet_module.lora_B[adapter_name].to(device)
# Handle the text encoder
modules_to_process = []
if hasattr(self, "text_encoder"):
modules_to_process.append(self.text_encoder)
if hasattr(self, "text_encoder_2"):
modules_to_process.append(self.text_encoder_2)
for text_encoder in modules_to_process:
# loop over submodules
for text_encoder_module in text_encoder.modules():
if isinstance(text_encoder_module, BaseTunerLayer):
for adapter_name in adapter_names:
text_encoder_module.lora_A[adapter_name].to(device)
text_encoder_module.lora_B[adapter_name].to(device)
class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
"""This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""
# Overrride to properly handle the loading and unloading of the additional text encoder.
def load_lora_weights(
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
`self.text_encoder`.
All kwargs are forwarded to `self.lora_state_dict`.
See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into
`self.unet`.
See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded
into `self.text_encoder`.
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
kwargs (`dict`, *optional*):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
"""
# We could have accessed the unet config from `lora_state_dict()` too. We pass
# it here explicitly to be able to tell that it's coming from an SDXL
# pipeline.
# First, ensure that the checkpoint is a compatible one and can be successfully loaded.
state_dict, network_alphas = self.lora_state_dict(
pretrained_model_name_or_path_or_dict,
unet_config=self.unet.config,
**kwargs,
)
is_correct_format = all("lora" in key for key in state_dict.keys())
if not is_correct_format:
raise ValueError("Invalid LoRA checkpoint.")
self.load_lora_into_unet(
state_dict, network_alphas=network_alphas, unet=self.unet, adapter_name=adapter_name, _pipeline=self
)
text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
if len(text_encoder_state_dict) > 0:
self.load_lora_into_text_encoder(
text_encoder_state_dict,
network_alphas=network_alphas,
text_encoder=self.text_encoder,
prefix="text_encoder",
lora_scale=self.lora_scale,
adapter_name=adapter_name,
_pipeline=self,
)
text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
if len(text_encoder_2_state_dict) > 0:
self.load_lora_into_text_encoder(
text_encoder_2_state_dict,
network_alphas=network_alphas,
text_encoder=self.text_encoder_2,
prefix="text_encoder_2",
lora_scale=self.lora_scale,
adapter_name=adapter_name,
_pipeline=self,
)
@classmethod
def save_lora_weights(
cls,
save_directory: Union[str, os.PathLike],
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
is_main_process: bool = True,
weight_name: str = None,
save_function: Callable = None,
safe_serialization: bool = True,
):
r"""
Save the LoRA parameters corresponding to the UNet and text encoder.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save LoRA parameters to. Will be created if it doesn't exist.
unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `unet`.
text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
encoder LoRA state dict because it comes from 🤗 Transformers.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
"""
state_dict = {}
def pack_weights(layers, prefix):
layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
return layers_state_dict
if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
raise ValueError(
"You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
)
if unet_lora_layers:
state_dict.update(pack_weights(unet_lora_layers, "unet"))
if text_encoder_lora_layers and text_encoder_2_lora_layers:
state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
cls.write_lora_layers(
state_dict=state_dict,
save_directory=save_directory,
is_main_process=is_main_process,
weight_name=weight_name,
save_function=save_function,
safe_serialization=safe_serialization,
)
def _remove_text_encoder_monkey_patch(self):
if USE_PEFT_BACKEND:
recurse_remove_peft_layers(self.text_encoder)
# TODO: @younesbelkada handle this in transformers side
if getattr(self.text_encoder, "peft_config", None) is not None:
del self.text_encoder.peft_config
self.text_encoder._hf_peft_config_loaded = None
recurse_remove_peft_layers(self.text_encoder_2)
if getattr(self.text_encoder_2, "peft_config", None) is not None:
del self.text_encoder_2.peft_config
self.text_encoder_2._hf_peft_config_loaded = None
else:
self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)
| diffusers/src/diffusers/loaders/lora.py/0 | {
"file_path": "diffusers/src/diffusers/loaders/lora.py",
"repo_id": "diffusers",
"token_count": 34909
} | 112 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...utils.accelerate_utils import apply_forward_hook
from ..modeling_outputs import AutoencoderKLOutput
from ..modeling_utils import ModelMixin
from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
r"""
Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss
for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
down_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of down block output channels.
layers_per_down_block (`int`, *optional*, defaults to `1`):
Number layers for down block.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
up_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of up block output channels.
layers_per_up_block (`int`, *optional*, defaults to `1`):
Number layers for up block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
norm_num_groups (`int`, *optional*, defaults to `32`):
Number of groups to use for the first normalization layer in ResNet blocks.
scaling_factor (`float`, *optional*, defaults to 0.18215):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
"""
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),
down_block_out_channels: Tuple[int, ...] = (64,),
layers_per_down_block: int = 1,
up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),
up_block_out_channels: Tuple[int, ...] = (64,),
layers_per_up_block: int = 1,
act_fn: str = "silu",
latent_channels: int = 4,
norm_num_groups: int = 32,
sample_size: int = 32,
scaling_factor: float = 0.18215,
) -> None:
super().__init__()
# pass init params to Encoder
self.encoder = Encoder(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
block_out_channels=down_block_out_channels,
layers_per_block=layers_per_down_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
double_z=True,
)
# pass init params to Decoder
self.decoder = MaskConditionDecoder(
in_channels=latent_channels,
out_channels=out_channels,
up_block_types=up_block_types,
block_out_channels=up_block_out_channels,
layers_per_block=layers_per_up_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
)
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
self.use_slicing = False
self.use_tiling = False
self.register_to_config(block_out_channels=up_block_out_channels)
self.register_to_config(force_upcast=False)
@apply_forward_hook
def encode(
self, x: torch.FloatTensor, return_dict: bool = True
) -> Union[AutoencoderKLOutput, Tuple[torch.FloatTensor]]:
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def _decode(
self,
z: torch.FloatTensor,
image: Optional[torch.FloatTensor] = None,
mask: Optional[torch.FloatTensor] = None,
return_dict: bool = True,
) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]:
z = self.post_quant_conv(z)
dec = self.decoder(z, image, mask)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
@apply_forward_hook
def decode(
self,
z: torch.FloatTensor,
generator: Optional[torch.Generator] = None,
image: Optional[torch.FloatTensor] = None,
mask: Optional[torch.FloatTensor] = None,
return_dict: bool = True,
) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]:
decoded = self._decode(z, image, mask).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=decoded)
def forward(
self,
sample: torch.FloatTensor,
mask: Optional[torch.FloatTensor] = None,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[DecoderOutput, Tuple[torch.FloatTensor]]:
r"""
Args:
sample (`torch.FloatTensor`): Input sample.
mask (`torch.FloatTensor`, *optional*, defaults to `None`): Optional inpainting mask.
sample_posterior (`bool`, *optional*, defaults to `False`):
Whether to sample from the posterior.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
x = sample
posterior = self.encode(x).latent_dist
if sample_posterior:
z = posterior.sample(generator=generator)
else:
z = posterior.mode()
dec = self.decode(z, sample, mask).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
| diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py/0 | {
"file_path": "diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py",
"repo_id": "diffusers",
"token_count": 3209
} | 113 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch - Flax general utilities."""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
logger = logging.get_logger(__name__)
#####################
# Flax => PyTorch #
#####################
# from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_flax_pytorch_utils.py#L224-L352
def load_flax_checkpoint_in_pytorch_model(pt_model, model_file):
try:
with open(model_file, "rb") as flax_state_f:
flax_state = from_bytes(None, flax_state_f.read())
except UnpicklingError as e:
try:
with open(model_file) as f:
if f.read().startswith("version"):
raise OSError(
"You seem to have cloned a repository without having git-lfs installed. Please"
" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
" folder you cloned."
)
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")
return load_flax_weights_in_pytorch_model(pt_model, flax_state)
def load_flax_weights_in_pytorch_model(pt_model, flax_state):
"""Load flax checkpoints in a PyTorch model"""
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions."
)
raise
# check if we have bf16 weights
is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()
if any(is_type_bf16):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
"before loading those in PyTorch model."
)
flax_state = jax.tree_util.tree_map(
lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state
)
pt_model.base_model_prefix = ""
flax_state_dict = flatten_dict(flax_state, sep=".")
pt_model_dict = pt_model.state_dict()
# keep track of unexpected & missing keys
unexpected_keys = []
missing_keys = set(pt_model_dict.keys())
for flax_key_tuple, flax_tensor in flax_state_dict.items():
flax_key_tuple_array = flax_key_tuple.split(".")
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]
flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
elif flax_key_tuple_array[-1] == "kernel":
flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]
flax_tensor = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(flax_key_tuple_array):
flax_key_tuple_array[i] = (
flax_key_tuple_string.replace("_0", ".0")
.replace("_1", ".1")
.replace("_2", ".2")
.replace("_3", ".3")
.replace("_4", ".4")
.replace("_5", ".5")
.replace("_6", ".6")
.replace("_7", ".7")
.replace("_8", ".8")
.replace("_9", ".9")
)
flax_key = ".".join(flax_key_tuple_array)
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."
)
else:
# add weight to pytorch dict
flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor
pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
# remove from missing keys
missing_keys.remove(flax_key)
else:
# weight is not expected by PyTorch model
unexpected_keys.append(flax_key)
pt_model.load_state_dict(pt_model_dict)
# re-transform missing_keys to list
missing_keys = list(missing_keys)
if len(unexpected_keys) > 0:
logger.warning(
"Some weights of the Flax model were not used when initializing the PyTorch model"
f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
" FlaxBertForSequenceClassification model)."
)
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
" use it for predictions and inference."
)
return pt_model
| diffusers/src/diffusers/models/modeling_pytorch_flax_utils.py/0 | {
"file_path": "diffusers/src/diffusers/models/modeling_pytorch_flax_utils.py",
"repo_id": "diffusers",
"token_count": 3051
} | 114 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..utils import deprecate
from .unets.unet_1d_blocks import (
AttnDownBlock1D,
AttnUpBlock1D,
DownBlock1D,
DownBlock1DNoSkip,
DownResnetBlock1D,
Downsample1d,
MidResTemporalBlock1D,
OutConv1DBlock,
OutValueFunctionBlock,
ResConvBlock,
SelfAttention1d,
UNetMidBlock1D,
UpBlock1D,
UpBlock1DNoSkip,
UpResnetBlock1D,
Upsample1d,
ValueFunctionMidBlock1D,
)
class DownResnetBlock1D(DownResnetBlock1D):
deprecation_message = "Importing `DownResnetBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownResnetBlock1D`, instead."
deprecate("DownResnetBlock1D", "0.29", deprecation_message)
class UpResnetBlock1D(UpResnetBlock1D):
deprecation_message = "Importing `UpResnetBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpResnetBlock1D`, instead."
deprecate("UpResnetBlock1D", "0.29", deprecation_message)
class ValueFunctionMidBlock1D(ValueFunctionMidBlock1D):
deprecation_message = "Importing `ValueFunctionMidBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import ValueFunctionMidBlock1D`, instead."
deprecate("ValueFunctionMidBlock1D", "0.29", deprecation_message)
class OutConv1DBlock(OutConv1DBlock):
deprecation_message = "Importing `OutConv1DBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import OutConv1DBlock`, instead."
deprecate("OutConv1DBlock", "0.29", deprecation_message)
class OutValueFunctionBlock(OutValueFunctionBlock):
deprecation_message = "Importing `OutValueFunctionBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import OutValueFunctionBlock`, instead."
deprecate("OutValueFunctionBlock", "0.29", deprecation_message)
class Downsample1d(Downsample1d):
deprecation_message = "Importing `Downsample1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import Downsample1d`, instead."
deprecate("Downsample1d", "0.29", deprecation_message)
class Upsample1d(Upsample1d):
deprecation_message = "Importing `Upsample1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import Upsample1d`, instead."
deprecate("Upsample1d", "0.29", deprecation_message)
class SelfAttention1d(SelfAttention1d):
deprecation_message = "Importing `SelfAttention1d` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import SelfAttention1d`, instead."
deprecate("SelfAttention1d", "0.29", deprecation_message)
class ResConvBlock(ResConvBlock):
deprecation_message = "Importing `ResConvBlock` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import ResConvBlock`, instead."
deprecate("ResConvBlock", "0.29", deprecation_message)
class UNetMidBlock1D(UNetMidBlock1D):
deprecation_message = "Importing `UNetMidBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UNetMidBlock1D`, instead."
deprecate("UNetMidBlock1D", "0.29", deprecation_message)
class AttnDownBlock1D(AttnDownBlock1D):
deprecation_message = "Importing `AttnDownBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import AttnDownBlock1D`, instead."
deprecate("AttnDownBlock1D", "0.29", deprecation_message)
class DownBlock1D(DownBlock1D):
deprecation_message = "Importing `DownBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownBlock1D`, instead."
deprecate("DownBlock1D", "0.29", deprecation_message)
class DownBlock1DNoSkip(DownBlock1DNoSkip):
deprecation_message = "Importing `DownBlock1DNoSkip` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import DownBlock1DNoSkip`, instead."
deprecate("DownBlock1DNoSkip", "0.29", deprecation_message)
class AttnUpBlock1D(AttnUpBlock1D):
deprecation_message = "Importing `AttnUpBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import AttnUpBlock1D`, instead."
deprecate("AttnUpBlock1D", "0.29", deprecation_message)
class UpBlock1D(UpBlock1D):
deprecation_message = "Importing `UpBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpBlock1D`, instead."
deprecate("UpBlock1D", "0.29", deprecation_message)
class UpBlock1DNoSkip(UpBlock1DNoSkip):
deprecation_message = "Importing `UpBlock1DNoSkip` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import UpBlock1DNoSkip`, instead."
deprecate("UpBlock1DNoSkip", "0.29", deprecation_message)
class MidResTemporalBlock1D(MidResTemporalBlock1D):
deprecation_message = "Importing `MidResTemporalBlock1D` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import MidResTemporalBlock1D`, instead."
deprecate("MidResTemporalBlock1D", "0.29", deprecation_message)
def get_down_block(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
temb_channels: int,
add_downsample: bool,
):
deprecation_message = "Importing `get_down_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_down_block`, instead."
deprecate("get_down_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_down_block
return get_down_block(
down_block_type=down_block_type,
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
)
def get_up_block(
up_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_upsample: bool
):
deprecation_message = "Importing `get_up_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_up_block`, instead."
deprecate("get_up_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_up_block
return get_up_block(
up_block_type=up_block_type,
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_upsample=add_upsample,
)
def get_mid_block(
mid_block_type: str,
num_layers: int,
in_channels: int,
mid_channels: int,
out_channels: int,
embed_dim: int,
add_downsample: bool,
):
deprecation_message = "Importing `get_mid_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_mid_block`, instead."
deprecate("get_mid_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_mid_block
return get_mid_block(
mid_block_type=mid_block_type,
num_layers=num_layers,
in_channels=in_channels,
mid_channels=mid_channels,
out_channels=out_channels,
embed_dim=embed_dim,
add_downsample=add_downsample,
)
def get_out_block(
*, out_block_type: str, num_groups_out: int, embed_dim: int, out_channels: int, act_fn: str, fc_dim: int
):
deprecation_message = "Importing `get_out_block` from `diffusers.models.unet_1d_blocks` is deprecated and this will be removed in a future version. Please use `from diffusers.models.unets.unet_1d_blocks import get_out_block`, instead."
deprecate("get_out_block", "0.29", deprecation_message)
from .unets.unet_1d_blocks import get_out_block
return get_out_block(
out_block_type=out_block_type,
num_groups_out=num_groups_out,
embed_dim=embed_dim,
out_channels=out_channels,
act_fn=act_fn,
fc_dim=fc_dim,
)
| diffusers/src/diffusers/models/unet_1d_blocks.py/0 | {
"file_path": "diffusers/src/diffusers/models/unet_1d_blocks.py",
"repo_id": "diffusers",
"token_count": 3633
} | 115 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import UNet2DConditionLoadersMixin
from ...utils import logging
from ..attention_processor import (
ADDED_KV_ATTENTION_PROCESSORS,
CROSS_ATTENTION_PROCESSORS,
AttentionProcessor,
AttnAddedKVProcessor,
AttnProcessor,
)
from ..embeddings import TimestepEmbedding, Timesteps
from ..modeling_utils import ModelMixin
from ..transformers.transformer_temporal import TransformerTemporalModel
from .unet_2d_blocks import UNetMidBlock2DCrossAttn
from .unet_2d_condition import UNet2DConditionModel
from .unet_3d_blocks import (
CrossAttnDownBlockMotion,
CrossAttnUpBlockMotion,
DownBlockMotion,
UNetMidBlockCrossAttnMotion,
UpBlockMotion,
get_down_block,
get_up_block,
)
from .unet_3d_condition import UNet3DConditionOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class MotionModules(nn.Module):
def __init__(
self,
in_channels: int,
layers_per_block: int = 2,
num_attention_heads: int = 8,
attention_bias: bool = False,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
norm_num_groups: int = 32,
max_seq_length: int = 32,
):
super().__init__()
self.motion_modules = nn.ModuleList([])
for i in range(layers_per_block):
self.motion_modules.append(
TransformerTemporalModel(
in_channels=in_channels,
norm_num_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels // num_attention_heads,
positional_embeddings="sinusoidal",
num_positional_embeddings=max_seq_length,
)
)
class MotionAdapter(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
motion_layers_per_block: int = 2,
motion_mid_block_layers_per_block: int = 1,
motion_num_attention_heads: int = 8,
motion_norm_num_groups: int = 32,
motion_max_seq_length: int = 32,
use_motion_mid_block: bool = True,
conv_in_channels: Optional[int] = None,
):
"""Container to store AnimateDiff Motion Modules
Args:
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each UNet block.
motion_layers_per_block (`int`, *optional*, defaults to 2):
The number of motion layers per UNet block.
motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):
The number of motion layers in the middle UNet block.
motion_num_attention_heads (`int`, *optional*, defaults to 8):
The number of heads to use in each attention layer of the motion module.
motion_norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use in each group normalization layer of the motion module.
motion_max_seq_length (`int`, *optional*, defaults to 32):
The maximum sequence length to use in the motion module.
use_motion_mid_block (`bool`, *optional*, defaults to True):
Whether to use a motion module in the middle of the UNet.
"""
super().__init__()
down_blocks = []
up_blocks = []
if conv_in_channels:
# input
self.conv_in = nn.Conv2d(conv_in_channels, block_out_channels[0], kernel_size=3, padding=1)
else:
self.conv_in = None
for i, channel in enumerate(block_out_channels):
output_channel = block_out_channels[i]
down_blocks.append(
MotionModules(
in_channels=output_channel,
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads,
max_seq_length=motion_max_seq_length,
layers_per_block=motion_layers_per_block,
)
)
if use_motion_mid_block:
self.mid_block = MotionModules(
in_channels=block_out_channels[-1],
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads,
layers_per_block=motion_mid_block_layers_per_block,
max_seq_length=motion_max_seq_length,
)
else:
self.mid_block = None
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, channel in enumerate(reversed_block_out_channels):
output_channel = reversed_block_out_channels[i]
up_blocks.append(
MotionModules(
in_channels=output_channel,
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads,
max_seq_length=motion_max_seq_length,
layers_per_block=motion_layers_per_block + 1,
)
)
self.down_blocks = nn.ModuleList(down_blocks)
self.up_blocks = nn.ModuleList(up_blocks)
def forward(self, sample):
pass
class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a
sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlockMotion",
"CrossAttnDownBlockMotion",
"CrossAttnDownBlockMotion",
"DownBlockMotion",
),
up_block_types: Tuple[str, ...] = (
"UpBlockMotion",
"CrossAttnUpBlockMotion",
"CrossAttnUpBlockMotion",
"CrossAttnUpBlockMotion",
),
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: int = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 1280,
use_linear_projection: bool = False,
num_attention_heads: Union[int, Tuple[int, ...]] = 8,
motion_max_seq_length: int = 32,
motion_num_attention_heads: int = 8,
use_motion_mid_block: int = True,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
):
super().__init__()
self.sample_size = sample_size
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
# input
conv_in_kernel = 3
conv_out_kernel = 3
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
time_embed_dim = block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], True, 0)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
)
if encoder_hid_dim_type is None:
self.encoder_hid_proj = None
# class embedding
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[i],
downsample_padding=downsample_padding,
use_linear_projection=use_linear_projection,
dual_cross_attention=False,
temporal_num_attention_heads=motion_num_attention_heads,
temporal_max_seq_length=motion_max_seq_length,
)
self.down_blocks.append(down_block)
# mid
if use_motion_mid_block:
self.mid_block = UNetMidBlockCrossAttnMotion(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
temporal_num_attention_heads=motion_num_attention_heads,
temporal_max_seq_length=motion_max_seq_length,
)
else:
self.mid_block = UNetMidBlock2DCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
)
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=False,
resolution_idx=i,
use_linear_projection=use_linear_projection,
temporal_num_attention_heads=motion_num_attention_heads,
temporal_max_seq_length=motion_max_seq_length,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
if norm_num_groups is not None:
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = nn.SiLU()
else:
self.conv_norm_out = None
self.conv_act = None
conv_out_padding = (conv_out_kernel - 1) // 2
self.conv_out = nn.Conv2d(
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
)
@classmethod
def from_unet2d(
cls,
unet: UNet2DConditionModel,
motion_adapter: Optional[MotionAdapter] = None,
load_weights: bool = True,
):
has_motion_adapter = motion_adapter is not None
# based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459
config = unet.config
config["_class_name"] = cls.__name__
down_blocks = []
for down_blocks_type in config["down_block_types"]:
if "CrossAttn" in down_blocks_type:
down_blocks.append("CrossAttnDownBlockMotion")
else:
down_blocks.append("DownBlockMotion")
config["down_block_types"] = down_blocks
up_blocks = []
for down_blocks_type in config["up_block_types"]:
if "CrossAttn" in down_blocks_type:
up_blocks.append("CrossAttnUpBlockMotion")
else:
up_blocks.append("UpBlockMotion")
config["up_block_types"] = up_blocks
if has_motion_adapter:
config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"]
config["motion_max_seq_length"] = motion_adapter.config["motion_max_seq_length"]
config["use_motion_mid_block"] = motion_adapter.config["use_motion_mid_block"]
# For PIA UNets we need to set the number input channels to 9
if motion_adapter.config["conv_in_channels"]:
config["in_channels"] = motion_adapter.config["conv_in_channels"]
# Need this for backwards compatibility with UNet2DConditionModel checkpoints
if not config.get("num_attention_heads"):
config["num_attention_heads"] = config["attention_head_dim"]
model = cls.from_config(config)
if not load_weights:
return model
# Logic for loading PIA UNets which allow the first 4 channels to be any UNet2DConditionModel conv_in weight
# while the last 5 channels must be PIA conv_in weights.
if has_motion_adapter and motion_adapter.config["conv_in_channels"]:
model.conv_in = motion_adapter.conv_in
updated_conv_in_weight = torch.cat(
[unet.conv_in.weight, motion_adapter.conv_in.weight[:, 4:, :, :]], dim=1
)
model.conv_in.load_state_dict({"weight": updated_conv_in_weight, "bias": unet.conv_in.bias})
else:
model.conv_in.load_state_dict(unet.conv_in.state_dict())
model.time_proj.load_state_dict(unet.time_proj.state_dict())
model.time_embedding.load_state_dict(unet.time_embedding.state_dict())
for i, down_block in enumerate(unet.down_blocks):
model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict())
if hasattr(model.down_blocks[i], "attentions"):
model.down_blocks[i].attentions.load_state_dict(down_block.attentions.state_dict())
if model.down_blocks[i].downsamplers:
model.down_blocks[i].downsamplers.load_state_dict(down_block.downsamplers.state_dict())
for i, up_block in enumerate(unet.up_blocks):
model.up_blocks[i].resnets.load_state_dict(up_block.resnets.state_dict())
if hasattr(model.up_blocks[i], "attentions"):
model.up_blocks[i].attentions.load_state_dict(up_block.attentions.state_dict())
if model.up_blocks[i].upsamplers:
model.up_blocks[i].upsamplers.load_state_dict(up_block.upsamplers.state_dict())
model.mid_block.resnets.load_state_dict(unet.mid_block.resnets.state_dict())
model.mid_block.attentions.load_state_dict(unet.mid_block.attentions.state_dict())
if unet.conv_norm_out is not None:
model.conv_norm_out.load_state_dict(unet.conv_norm_out.state_dict())
if unet.conv_act is not None:
model.conv_act.load_state_dict(unet.conv_act.state_dict())
model.conv_out.load_state_dict(unet.conv_out.state_dict())
if has_motion_adapter:
model.load_motion_modules(motion_adapter)
# ensure that the Motion UNet is the same dtype as the UNet2DConditionModel
model.to(unet.dtype)
return model
def freeze_unet2d_params(self) -> None:
"""Freeze the weights of just the UNet2DConditionModel, and leave the motion modules
unfrozen for fine tuning.
"""
# Freeze everything
for param in self.parameters():
param.requires_grad = False
# Unfreeze Motion Modules
for down_block in self.down_blocks:
motion_modules = down_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
for up_block in self.up_blocks:
motion_modules = up_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
if hasattr(self.mid_block, "motion_modules"):
motion_modules = self.mid_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]) -> None:
for i, down_block in enumerate(motion_adapter.down_blocks):
self.down_blocks[i].motion_modules.load_state_dict(down_block.motion_modules.state_dict())
for i, up_block in enumerate(motion_adapter.up_blocks):
self.up_blocks[i].motion_modules.load_state_dict(up_block.motion_modules.state_dict())
# to support older motion modules that don't have a mid_block
if hasattr(self.mid_block, "motion_modules"):
self.mid_block.motion_modules.load_state_dict(motion_adapter.mid_block.motion_modules.state_dict())
def save_motion_modules(
self,
save_directory: str,
is_main_process: bool = True,
safe_serialization: bool = True,
variant: Optional[str] = None,
push_to_hub: bool = False,
**kwargs,
) -> None:
state_dict = self.state_dict()
# Extract all motion modules
motion_state_dict = {}
for k, v in state_dict.items():
if "motion_modules" in k:
motion_state_dict[k] = v
adapter = MotionAdapter(
block_out_channels=self.config["block_out_channels"],
motion_layers_per_block=self.config["layers_per_block"],
motion_norm_num_groups=self.config["norm_num_groups"],
motion_num_attention_heads=self.config["motion_num_attention_heads"],
motion_max_seq_length=self.config["motion_max_seq_length"],
use_motion_mid_block=self.config["use_motion_mid_block"],
)
adapter.load_state_dict(motion_state_dict)
adapter.save_pretrained(
save_directory=save_directory,
is_main_process=is_main_process,
safe_serialization=safe_serialization,
variant=variant,
push_to_hub=push_to_hub,
**kwargs,
)
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
def disable_forward_chunking(self) -> None:
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, None, 0)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self) -> None:
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor)
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, CrossAttnUpBlockMotion, UpBlockMotion)):
module.gradient_checkpointing = value
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float) -> None:
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stage blocks where they are being applied.
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
for i, upsample_block in enumerate(self.up_blocks):
setattr(upsample_block, "s1", s1)
setattr(upsample_block, "s2", s2)
setattr(upsample_block, "b1", b1)
setattr(upsample_block, "b2", b2)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
def disable_freeu(self) -> None:
"""Disables the FreeU mechanism."""
freeu_keys = {"s1", "s2", "b1", "b2"}
for i, upsample_block in enumerate(self.up_blocks):
for k in freeu_keys:
if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
setattr(upsample_block, k, None)
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet3DConditionOutput, Tuple[torch.Tensor]]:
r"""
The [`UNetMotionModel`] forward method.
Args:
sample (`torch.FloatTensor`):
The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.FloatTensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
through the `self.time_embedding` layer to obtain the timestep embeddings.
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
A tuple of tensors that if specified are added to the residuals of down unet blocks.
mid_block_additional_residual: (`torch.Tensor`, *optional*):
A tensor that if specified is added to the residual of the middle unet block.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
tuple.
Returns:
[`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# prepare attention_mask
if attention_mask is not None:
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
num_frames = sample.shape[2]
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=self.dtype)
emb = self.time_embedding(t_emb, timestep_cond)
emb = emb.repeat_interleave(repeats=num_frames, dim=0)
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
# 2. pre-process
sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
new_down_block_res_samples = ()
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
down_block_res_samples = new_down_block_res_samples
# 4. mid
if self.mid_block is not None:
# To support older versions of motion modules that don't have a mid_block
if hasattr(self.mid_block, "motion_modules"):
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
)
if mid_block_additional_residual is not None:
sample = sample + mid_block_additional_residual
# 5. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
upsample_size=upsample_size,
attention_mask=attention_mask,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
upsample_size=upsample_size,
num_frames=num_frames,
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
# reshape to (batch, channel, framerate, width, height)
sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)
if not return_dict:
return (sample,)
return UNet3DConditionOutput(sample=sample)
| diffusers/src/diffusers/models/unets/unet_motion_model.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_motion_model.py",
"repo_id": "diffusers",
"token_count": 18687
} | 116 |
from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
import torch
from ...utils import BaseOutput
@dataclass
class AnimateDiffPipelineOutput(BaseOutput):
r"""
Output class for AnimateDiff pipelines.
Args:
frames (`List[List[PIL.Image.Image]]` or `torch.Tensor` or `np.ndarray`):
List of PIL Images of length `batch_size` or torch.Tensor or np.ndarray of shape
`(batch_size, num_frames, height, width, num_channels)`.
"""
frames: Union[List[List[PIL.Image.Image]], torch.Tensor, np.ndarray]
| diffusers/src/diffusers/pipelines/animatediff/pipeline_output.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/animatediff/pipeline_output.py",
"repo_id": "diffusers",
"token_count": 226
} | 117 |
import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import DDPMScheduler
from ...utils import (
BACKENDS_MAPPING,
PIL_INTERPOLATION,
is_accelerate_available,
is_bs4_available,
is_ftfy_available,
logging,
replace_example_docstring,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline
from .pipeline_output import IFPipelineOutput
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
w, h = images.size
coef = w / h
w, h = img_size, img_size
if coef >= 1:
w = int(round(img_size / 8 * coef) * 8)
else:
h = int(round(img_size / 8 / coef) * 8)
images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
return images
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image.resize((768, 512))
>>> pipe = IFImg2ImgPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0",
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A fantasy landscape in style minecraft"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0",
... text_encoder=None,
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
```
"""
class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"["
+ "#®•©™&@·º½¾¿¡§~"
+ r"\)"
+ r"\("
+ r"\]"
+ r"\["
+ r"\}"
+ r"\{"
+ r"\|"
+ "\\"
+ r"\/"
+ r"\*"
+ r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
model_cpu_offload_seq = "text_encoder->unet"
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
def remove_all_hooks(self):
if is_accelerate_available():
from accelerate.hooks import remove_hook_from_module
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
for model in [self.text_encoder, self.unet, self.safety_checker]:
if model is not None:
remove_hook_from_module(model, recurse=True)
self.unet_offload_hook = None
self.text_encoder_offload_hook = None
self.final_offload_hook = None
@torch.no_grad()
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
clean_caption (bool, defaults to `False`):
If `True`, the function will preprocess and clean the provided caption before encoding.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
image,
batch_size,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if isinstance(image, list):
check_image_type = image[0]
else:
check_image_type = image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor:
if not isinstance(image, list):
image = [image]
def numpy_to_pt(images):
if images.ndim == 3:
images = images[..., None]
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
return images
if isinstance(image[0], PIL.Image.Image):
new_image = []
for image_ in image:
image_ = image_.convert("RGB")
image_ = resize(image_, self.unet.sample_size)
image_ = np.array(image_)
image_ = image_.astype(np.float32)
image_ = image_ / 127.5 - 1
new_image.append(image_)
image = new_image
image = np.stack(image, axis=0) # to np
image = numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = numpy_to_pt(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
return image
def get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_intermediate_images(
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None
):
_, channels, height, width = image.shape
batch_size = batch_size * num_images_per_prompt
shape = (batch_size, channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
image = self.scheduler.add_noise(image, noise, timestep)
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
strength: float = 0.7,
num_inference_steps: int = 80,
timesteps: List[int] = None,
guidance_scale: float = 10.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
clean_caption: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (`float`, *optional*, defaults to 0.7):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 80):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 10.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
self.check_inputs(
prompt, image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
dtype = prompt_embeds.dtype
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
# 5. Prepare intermediate images
image = self.preprocess_image(image)
image = image.to(device=device, dtype=dtype)
noise_timestep = timesteps[0:1]
noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
intermediate_images = self.prepare_intermediate_images(
image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, generator
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = (
torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images
)
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, intermediate_images)
image = intermediate_images
if output_type == "pil":
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 10. Convert to PIL
image = self.numpy_to_pil(image)
# 11. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)
| diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py",
"repo_id": "diffusers",
"token_count": 18623
} | 118 |
from typing import TYPE_CHECKING
from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
_import_structure = {
"mel": ["Mel"],
"pipeline_audio_diffusion": ["AudioDiffusionPipeline"],
}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .mel import Mel
from .pipeline_audio_diffusion import AudioDiffusionPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
| diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/__init__.py",
"repo_id": "diffusers",
"token_count": 212
} | 119 |
from typing import TYPE_CHECKING
from ....utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ....utils import dummy_torch_and_transformers_objects
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_cycle_diffusion"] = ["CycleDiffusionPipeline"]
_import_structure["pipeline_stable_diffusion_inpaint_legacy"] = ["StableDiffusionInpaintPipelineLegacy"]
_import_structure["pipeline_stable_diffusion_model_editing"] = ["StableDiffusionModelEditingPipeline"]
_import_structure["pipeline_stable_diffusion_paradigms"] = ["StableDiffusionParadigmsPipeline"]
_import_structure["pipeline_stable_diffusion_pix2pix_zero"] = ["StableDiffusionPix2PixZeroPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ....utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_pix2pix_zero import StableDiffusionPix2PixZeroPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
| diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/__init__.py",
"repo_id": "diffusers",
"token_count": 817
} | 120 |
# Copyright 2023 Microsoft and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ....configuration_utils import ConfigMixin, register_to_config
from ....models import ModelMixin, Transformer2DModel, VQModel
from ....schedulers import VQDiffusionScheduler
from ....utils import logging
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin):
"""
Utility class for storing learned text embeddings for classifier free sampling
"""
@register_to_config
def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None):
super().__init__()
self.learnable = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
embeddings = torch.zeros(length, hidden_size)
else:
embeddings = None
self.embeddings = torch.nn.Parameter(embeddings)
class VQDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using VQ Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vqvae ([`VQModel`]):
Vector Quantized Variational Auto-Encoder (VAE) model to encode and decode images to and from latent
representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
transformer ([`Transformer2DModel`]):
A conditional `Transformer2DModel` to denoise the encoded image latents.
scheduler ([`VQDiffusionScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
"""
vqvae: VQModel
text_encoder: CLIPTextModel
tokenizer: CLIPTokenizer
transformer: Transformer2DModel
learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings
scheduler: VQDiffusionScheduler
def __init__(
self,
vqvae: VQModel,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
transformer: Transformer2DModel,
scheduler: VQDiffusionScheduler,
learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings,
):
super().__init__()
self.register_modules(
vqvae=vqvae,
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
)
def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
prompt_embeds = self.text_encoder(text_input_ids.to(self.device))[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
prompt_embeds = prompt_embeds / prompt_embeds.norm(dim=-1, keepdim=True)
# duplicate text embeddings for each generation per prompt
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
negative_prompt_embeds = self.learned_classifier_free_sampling_embeddings.embeddings
negative_prompt_embeds = negative_prompt_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
else:
uncond_tokens = [""] * batch_size
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# See comment for normalizing text embeddings
negative_prompt_embeds = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1, keepdim=True)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
num_inference_steps: int = 100,
guidance_scale: float = 5.0,
truncation_rate: float = 1.0,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
) -> Union[ImagePipelineOutput, Tuple]:
"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)):
Used to "truncate" the predicted classes for x_0 such that the cumulative probability for a pixel is at
most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above
`truncation_rate` are set to zero.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor` of shape (batch), *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Must be valid embedding indices.If not provided, a latents tensor will be generated of
completely masked latent pixels.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance)
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get the initial completely masked latents unless the user supplied it
latents_shape = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
mask_class = self.transformer.num_vector_embeds - 1
latents = torch.full(latents_shape, mask_class).to(self.device)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
"Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
f" {self.transformer.num_vector_embeds - 1} (inclusive)."
)
latents = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps_tensor = self.scheduler.timesteps.to(self.device)
sample = latents
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the sample if we are doing classifier free guidance
latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
model_output = self.transformer(latent_model_input, encoder_hidden_states=prompt_embeds, timestep=t).sample
if do_classifier_free_guidance:
model_output_uncond, model_output_text = model_output.chunk(2)
model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(model_output, dim=1, keepdim=True)
model_output = self.truncate(model_output, truncation_rate)
# remove `log(0)`'s (`-inf`s)
model_output = model_output.clamp(-70)
# compute the previous noisy sample x_t -> x_t-1
sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, sample)
embedding_channels = self.vqvae.config.vq_embed_dim
embeddings_shape = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
embeddings = self.vqvae.quantize.get_codebook_entry(sample, shape=embeddings_shape)
image = self.vqvae.decode(embeddings, force_not_quantize=True).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
def truncate(self, log_p_x_0: torch.FloatTensor, truncation_rate: float) -> torch.FloatTensor:
"""
Truncates `log_p_x_0` such that for each column vector, the total cumulative probability is `truncation_rate`
The lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to
zero.
"""
sorted_log_p_x_0, indices = torch.sort(log_p_x_0, 1, descending=True)
sorted_p_x_0 = torch.exp(sorted_log_p_x_0)
keep_mask = sorted_p_x_0.cumsum(dim=1) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
all_true = torch.full_like(keep_mask[:, 0:1, :], True)
keep_mask = torch.cat((all_true, keep_mask), dim=1)
keep_mask = keep_mask[:, :-1, :]
keep_mask = keep_mask.gather(1, indices.argsort(1))
rv = log_p_x_0.clone()
rv[~keep_mask] = -torch.inf # -inf = log(0)
return rv
| diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py",
"repo_id": "diffusers",
"token_count": 6463
} | 121 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from PIL import Image
from ...models import UNet2DConditionModel, VQModel
from ...schedulers import DDPMScheduler
from ...utils import (
logging,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)["depth"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline("depth-estimation")
>>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to("cuda")
>>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> img = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
>>> prompt = "A robot, 4k photo"
>>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
>>> generator = torch.Generator(device="cuda").manual_seed(43)
>>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
>>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
>>> images = pipe(
... image=img,
... strength=0.5,
... image_embeds=img_emb.image_embeds,
... negative_image_embeds=negative_emb.image_embeds,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save("robot_cat.png")
```
"""
# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
def downscale_height_and_width(height, width, scale_factor=8):
new_height = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
new_width = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
def prepare_image(pil_image, w=512, h=512):
pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
arr = np.array(pil_image.convert("RGB"))
arr = arr.astype(np.float32) / 127.5 - 1
arr = np.transpose(arr, [2, 0, 1])
image = torch.from_numpy(arr).unsqueeze(0)
return image
class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
"""
model_cpu_offload_seq = "unet->movq"
def __init__(
self,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
movq: VQModel,
):
super().__init__()
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2_img2img.KandinskyV22Img2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
elif isinstance(generator, list):
init_latents = [
self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.movq.encode(image).latent_dist.sample(generator)
init_latents = self.movq.config.scaling_factor * init_latents
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@torch.no_grad()
def __call__(
self,
image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
hint: torch.FloatTensor,
height: int = 512,
width: int = 512,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
strength: float = 0.3,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded
again.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
hint (`torch.FloatTensor`):
The controlnet condition.
negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for negative text prompt, will be used to condition the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
device = self._execution_device
do_classifier_free_guidance = guidance_scale > 1.0
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
if isinstance(negative_image_embeds, list):
negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
if isinstance(hint, list):
hint = torch.cat(hint, dim=0)
batch_size = image_embeds.shape[0]
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
hint = hint.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=self.unet.dtype, device=device
)
hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)
if not isinstance(image, list):
image = [image]
if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
raise ValueError(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
)
image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
image = image.to(dtype=image_embeds.dtype, device=device)
latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
latents = self.prepare_latents(
latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
)
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
added_cond_kwargs = {"image_embeds": image_embeds, "hint": hint}
noise_pred = self.unet(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=None,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
if not (
hasattr(self.scheduler.config, "variance_type")
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
)[0]
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
# post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
# Offload all models
self.maybe_free_model_hooks()
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py",
"repo_id": "diffusers",
"token_count": 7539
} | 122 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import torch
from transformers import (
ClapFeatureExtractor,
ClapModel,
ClapTextModelWithProjection,
RobertaTokenizer,
RobertaTokenizerFast,
SpeechT5HifiGan,
)
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
is_accelerate_available,
is_accelerate_version,
is_librosa_available,
logging,
replace_example_docstring,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
if is_librosa_available():
import librosa
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import MusicLDMPipeline
>>> import torch
>>> import scipy
>>> repo_id = "ucsd-reach/musicldm"
>>> pipe = MusicLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
>>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
>>> # save the audio sample as a .wav file
>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
```
"""
class MusicLDMPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-audio generation using MusicLDM.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.ClapModel`]):
Frozen text-audio embedding model (`ClapTextModel`), specifically the
[laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.
tokenizer ([`PreTrainedTokenizer`]):
A [`~transformers.RobertaTokenizer`] to tokenize text.
feature_extractor ([`~transformers.ClapFeatureExtractor`]):
Feature extractor to compute mel-spectrograms from audio waveforms.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded audio latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
vocoder ([`~transformers.SpeechT5HifiGan`]):
Vocoder of class `SpeechT5HifiGan`.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: Union[ClapTextModelWithProjection, ClapModel],
tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
feature_extractor: Optional[ClapFeatureExtractor],
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
vocoder: SpeechT5HifiGan,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
unet=unet,
scheduler=scheduler,
vocoder=vocoder,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def _encode_prompt(
self,
prompt,
device,
num_waveforms_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device (`torch.device`):
torch device
num_waveforms_per_prompt (`int`):
number of waveforms that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the audio generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
attention_mask = text_inputs.attention_mask
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLAP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder.get_text_features(
text_input_ids.to(device),
attention_mask=attention_mask.to(device),
)
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.text_model.dtype, device=device)
(
bs_embed,
seq_len,
) = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt)
prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_input_ids = uncond_input.input_ids.to(device)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder.get_text_features(
uncond_input_ids,
attention_mask=attention_mask,
)
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.text_model.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.mel_spectrogram_to_waveform
def mel_spectrogram_to_waveform(self, mel_spectrogram):
if mel_spectrogram.dim() == 4:
mel_spectrogram = mel_spectrogram.squeeze(1)
waveform = self.vocoder(mel_spectrogram)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
waveform = waveform.cpu().float()
return waveform
# Copied from diffusers.pipelines.audioldm2.pipeline_audioldm2.AudioLDM2Pipeline.score_waveforms
def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype):
if not is_librosa_available():
logger.info(
"Automatic scoring of the generated audio waveforms against the input prompt text requires the "
"`librosa` package to resample the generated waveforms. Returning the audios in the order they were "
"generated. To enable automatic scoring, install `librosa` with: `pip install librosa`."
)
return audio
inputs = self.tokenizer(text, return_tensors="pt", padding=True)
resampled_audio = librosa.resample(
audio.numpy(), orig_sr=self.vocoder.config.sampling_rate, target_sr=self.feature_extractor.sampling_rate
)
inputs["input_features"] = self.feature_extractor(
list(resampled_audio), return_tensors="pt", sampling_rate=self.feature_extractor.sampling_rate
).input_features.type(dtype)
inputs = inputs.to(device)
# compute the audio-text similarity score using the CLAP model
logits_per_text = self.text_encoder(**inputs).logits_per_text
# sort by the highest matching generations per prompt
indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt]
audio = torch.index_select(audio, 0, indices.reshape(-1).cpu())
return audio
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.check_inputs
def check_inputs(
self,
prompt,
audio_length_in_s,
vocoder_upsample_factor,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor
if audio_length_in_s < min_audio_length_in_s:
raise ValueError(
f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but "
f"is {audio_length_in_s}."
)
if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:
raise ValueError(
f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the "
f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of "
f"{self.vae_scale_factor}."
)
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
height // self.vae_scale_factor,
self.vocoder.config.model_in_dim // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
model_sequence = [
self.text_encoder.text_model,
self.text_encoder.text_projection,
self.unet,
self.vae,
self.vocoder,
self.text_encoder,
]
hook = None
for cpu_offloaded_model in model_sequence:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
audio_length_in_s: Optional[float] = None,
num_inference_steps: int = 200,
guidance_scale: float = 2.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_waveforms_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
output_type: Optional[str] = "np",
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.
audio_length_in_s (`int`, *optional*, defaults to 10.24):
The length of the generated audio sample in seconds.
num_inference_steps (`int`, *optional*, defaults to 200):
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 2.0):
A higher guidance scale value encourages the model to generate audio that is closely linked to the text
`prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_waveforms_per_prompt (`int`, *optional*, defaults to 1):
The number of waveforms to generate per prompt. If `num_waveforms_per_prompt > 1`, the text encoding
model is a joint text-audio model ([`~transformers.ClapModel`]), and the tokenizer is a
`[~transformers.ClapProcessor]`, then automatic scoring will be performed between the generated outputs
and the input text. This scoring ranks the generated waveforms based on their cosine similarity to text
input in the joint text-audio embedding space.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
output_type (`str`, *optional*, defaults to `"np"`):
The output format of the generated audio. Choose between `"np"` to return a NumPy `np.ndarray` or
`"pt"` to return a PyTorch `torch.Tensor` object. Set to `"latent"` to return the latent diffusion
model (LDM) output.
Examples:
Returns:
[`~pipelines.AudioPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.
"""
# 0. Convert audio input length from seconds to spectrogram height
vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor
height = int(audio_length_in_s / vocoder_upsample_factor)
original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)
if height % self.vae_scale_factor != 0:
height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor
logger.info(
f"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} "
f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the "
f"denoising process."
)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
audio_length_in_s,
vocoder_upsample_factor,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_waveforms_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_waveforms_per_prompt,
num_channels_latents,
height,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=None,
class_labels=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
self.maybe_free_model_hooks()
# 8. Post-processing
if not output_type == "latent":
latents = 1 / self.vae.config.scaling_factor * latents
mel_spectrogram = self.vae.decode(latents).sample
else:
return AudioPipelineOutput(audios=latents)
audio = self.mel_spectrogram_to_waveform(mel_spectrogram)
audio = audio[:, :original_waveform_length]
# 9. Automatic scoring
if num_waveforms_per_prompt > 1 and prompt is not None:
audio = self.score_waveforms(
text=prompt,
audio=audio,
num_waveforms_per_prompt=num_waveforms_per_prompt,
device=device,
dtype=prompt_embeds.dtype,
)
if output_type == "np":
audio = audio.numpy()
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=audio)
| diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py",
"repo_id": "diffusers",
"token_count": 13584
} | 123 |
# Copyright 2023 Open AI and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL.Image
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from ...models import PriorTransformer
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
logging,
replace_example_docstring,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline
from .renderer import ShapERenderer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> repo = "openai/shap-e"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 15.0
>>> prompt = "a shark"
>>> images = pipe(
... prompt,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], "shark_3d.gif")
```
"""
@dataclass
class ShapEPipelineOutput(BaseOutput):
"""
Output class for [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`].
Args:
images (`torch.FloatTensor`)
A list of images for 3D rendering.
"""
images: Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]
class ShapEPipeline(DiffusionPipeline):
"""
Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_encoder ([`~transformers.CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
scheduler ([`HeunDiscreteScheduler`]):
A scheduler to be used in combination with the `prior` model to generate image embedding.
shap_e_renderer ([`ShapERenderer`]):
Shap-E renderer projects the generated latents into parameters of a MLP to create 3D objects with the NeRF
rendering method.
"""
model_cpu_offload_seq = "text_encoder->prior"
_exclude_from_cpu_offload = ["shap_e_renderer"]
def __init__(
self,
prior: PriorTransformer,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
scheduler: HeunDiscreteScheduler,
shap_e_renderer: ShapERenderer,
):
super().__init__()
self.register_modules(
prior=prior,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
shap_e_renderer=shap_e_renderer,
)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
):
len(prompt) if isinstance(prompt, list) else 1
# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
self.tokenizer.pad_token_id = 0
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
# in Shap-E it normalize the prompt_embeds and then later rescale it
prompt_embeds = prompt_embeds / torch.linalg.norm(prompt_embeds, dim=-1, keepdim=True)
if do_classifier_free_guidance:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# Rescale the features to have unit variance
prompt_embeds = math.sqrt(prompt_embeds.shape[1]) * prompt_embeds
return prompt_embeds
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: str,
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
guidance_scale: float = 4.0,
frame_size: int = 64,
output_type: Optional[str] = "pil", # pil, np, latent, mesh
return_dict: bool = True,
):
"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
frame_size (`int`, *optional*, default to 64):
The width and height of each image frame of the generated 3D output.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`), `"latent"` (`torch.Tensor`), or mesh ([`MeshDecoderOutput`]).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] instead of a plain
tuple.
Examples:
Returns:
[`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
device = self._execution_device
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds = self._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance)
# prior
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
num_embeddings = self.prior.config.num_embeddings
embedding_dim = self.prior.config.embedding_dim
latents = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim),
prompt_embeds.dtype,
device,
generator,
latents,
self.scheduler,
)
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
latents = latents.reshape(latents.shape[0], num_embeddings, embedding_dim)
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)
noise_pred = self.prior(
scaled_model_input,
timestep=t,
proj_embedding=prompt_embeds,
).predicted_image_embedding
# remove the variance
noise_pred, _ = noise_pred.split(
scaled_model_input.shape[2], dim=2
) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
latents = self.scheduler.step(
noise_pred,
timestep=t,
sample=latents,
).prev_sample
# Offload all models
self.maybe_free_model_hooks()
if output_type not in ["np", "pil", "latent", "mesh"]:
raise ValueError(
f"Only the output types `pil`, `np`, `latent` and `mesh` are supported not output_type={output_type}"
)
if output_type == "latent":
return ShapEPipelineOutput(images=latents)
images = []
if output_type == "mesh":
for i, latent in enumerate(latents):
mesh = self.shap_e_renderer.decode_to_mesh(
latent[None, :],
device,
)
images.append(mesh)
else:
# np, pil
for i, latent in enumerate(latents):
image = self.shap_e_renderer.decode_to_image(
latent[None, :],
device,
size=frame_size,
)
images.append(image)
images = torch.stack(images)
images = images.cpu().numpy()
if output_type == "pil":
images = [self.numpy_to_pil(image) for image in images]
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=images)
| diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py",
"repo_id": "diffusers",
"token_count": 5793
} | 124 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation
from ...configuration_utils import FrozenDict
from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
return encoder_output.latent_dist.sample(generator)
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, "latents"):
return encoder_output.latents
else:
raise AttributeError("Could not access latents of provided encoder_output")
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
model_cpu_offload_seq = "text_encoder->unet->vae"
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "depth_mask"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
depth_estimator: DPTForDepthEstimation,
feature_extractor: DPTFeatureExtractor,
):
super().__init__()
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
depth_estimator=depth_estimator,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
**kwargs,
):
deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=lora_scale,
**kwargs,
)
# concatenate for backwards comp
prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer.
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
def check_inputs(
self,
prompt,
strength,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
elif isinstance(generator, list):
init_latents = [
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device):
if isinstance(image, PIL.Image.Image):
image = [image]
else:
image = list(image)
if isinstance(image[0], PIL.Image.Image):
width, height = image[0].size
elif isinstance(image[0], np.ndarray):
width, height = image[0].shape[:-1]
else:
height, width = image[0].shape[-2:]
if depth_map is None:
pixel_values = self.feature_extractor(images=image, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device=device)
# The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16.
# So we use `torch.autocast` here for half precision inference.
context_manger = torch.autocast("cuda", dtype=dtype) if device.type == "cuda" else contextlib.nullcontext()
with context_manger:
depth_map = self.depth_estimator(pixel_values).predicted_depth
else:
depth_map = depth_map.to(device=device, dtype=dtype)
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=(height // self.vae_scale_factor, width // self.vae_scale_factor),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0
depth_map = depth_map.to(dtype)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if depth_map.shape[0] < batch_size:
repeat_by = batch_size // depth_map.shape[0]
depth_map = depth_map.repeat(repeat_by, 1, 1, 1)
depth_map = torch.cat([depth_map] * 2) if do_classifier_free_guidance else depth_map
return depth_map
@property
def guidance_scale(self):
return self._guidance_scale
@property
def clip_skip(self):
return self._clip_skip
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
depth_map: Optional[torch.FloatTensor] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be used as the starting point. Can accept image
latents as `image` only if `depth_map` is not `None`.
depth_map (`torch.FloatTensor`, *optional*):
Depth prediction to be used as additional conditioning for the image generation process. If not
defined, it automatically predicts the depth with `self.depth_estimator`.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
Examples:
```py
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from diffusers import StableDiffusionDepth2ImgPipeline
>>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
... "stabilityai/stable-diffusion-2-depth",
... torch_dtype=torch.float16,
... )
>>> pipe.to("cuda")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> init_image = Image.open(requests.get(url, stream=True).raw)
>>> prompt = "two tigers"
>>> n_propmt = "bad, deformed, ugly, bad anotomy"
>>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images.
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
# 1. Check inputs
self.check_inputs(
prompt,
strength,
callback_steps,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
if image is None:
raise ValueError("`image` input cannot be undefined.")
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# 3. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=self.clip_skip,
)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# 4. Prepare depth mask
depth_mask = self.prepare_depth_map(
image,
depth_map,
batch_size * num_images_per_prompt,
self.do_classifier_free_guidance,
prompt_embeds.dtype,
device,
)
# 5. Preprocess image
image = self.image_processor.preprocess(image)
# 6. Set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 7. Prepare latent variables
latents = self.prepare_latents(
image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=self.cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
depth_mask = callback_outputs.pop("depth_mask", depth_mask)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py",
"repo_id": "diffusers",
"token_count": 19001
} | 125 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_stable_diffusion_gligen"] = ["StableDiffusionGLIGENPipeline"]
_import_structure["pipeline_stable_diffusion_gligen_text_image"] = ["StableDiffusionGLIGENTextImagePipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_stable_diffusion_gligen import StableDiffusionGLIGENPipeline
from .pipeline_stable_diffusion_gligen_text_image import StableDiffusionGLIGENTextImagePipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
| diffusers/src/diffusers/pipelines/stable_diffusion_gligen/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_gligen/__init__.py",
"repo_id": "diffusers",
"token_count": 613
} | 126 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_flax_available,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["StableDiffusionXLPipelineOutput"]}
if is_transformers_available() and is_flax_available():
_import_structure["pipeline_output"].extend(["FlaxStableDiffusionXLPipelineOutput"])
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_stable_diffusion_xl"] = ["StableDiffusionXLPipeline"]
_import_structure["pipeline_stable_diffusion_xl_img2img"] = ["StableDiffusionXLImg2ImgPipeline"]
_import_structure["pipeline_stable_diffusion_xl_inpaint"] = ["StableDiffusionXLInpaintPipeline"]
_import_structure["pipeline_stable_diffusion_xl_instruct_pix2pix"] = ["StableDiffusionXLInstructPix2PixPipeline"]
if is_transformers_available() and is_flax_available():
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
_additional_imports.update({"PNDMSchedulerState": PNDMSchedulerState})
_import_structure["pipeline_flax_stable_diffusion_xl"] = ["FlaxStableDiffusionXLPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
from .pipeline_stable_diffusion_xl_img2img import StableDiffusionXLImg2ImgPipeline
from .pipeline_stable_diffusion_xl_inpaint import StableDiffusionXLInpaintPipeline
from .pipeline_stable_diffusion_xl_instruct_pix2pix import StableDiffusionXLInstructPix2PixPipeline
try:
if not (is_transformers_available() and is_flax_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_flax_objects import *
else:
from .pipeline_flax_stable_diffusion_xl import (
FlaxStableDiffusionXLPipeline,
)
from .pipeline_output import FlaxStableDiffusionXLPipelineOutput
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
for name, value in _additional_imports.items():
setattr(sys.modules[__name__], name, value)
| diffusers/src/diffusers/pipelines/stable_diffusion_xl/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/__init__.py",
"repo_id": "diffusers",
"token_count": 1202
} | 127 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet3DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
USE_PEFT_BACKEND,
deprecate,
logging,
replace_example_docstring,
scale_lora_layers,
unscale_lora_layers,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import TextToVideoSDPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
>>> from diffusers.utils import export_to_video
>>> pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
>>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
>>> pipe.to("cuda")
>>> prompt = "spiderman running in the desert"
>>> video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
>>> # safe low-res video
>>> video_path = export_to_video(video_frames, output_video_path="./video_576_spiderman.mp4")
>>> # let's offload the text-to-image model
>>> pipe.to("cpu")
>>> # and load the image-to-image model
>>> pipe = DiffusionPipeline.from_pretrained(
... "cerspense/zeroscope_v2_XL", torch_dtype=torch.float16, revision="refs/pr/15"
... )
>>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
>>> pipe.enable_model_cpu_offload()
>>> # The VAE consumes A LOT of memory, let's make sure we run it in sliced mode
>>> pipe.vae.enable_slicing()
>>> # now let's upscale it
>>> video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]
>>> # and denoise it
>>> video_frames = pipe(prompt, video=video, strength=0.6).frames
>>> video_path = export_to_video(video_frames, output_video_path="./video_1024_spiderman.mp4")
>>> video_path
```
"""
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
return encoder_output.latent_dist.sample(generator)
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, "latents"):
return encoder_output.latents
else:
raise AttributeError("Could not access latents of provided encoder_output")
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
batch_size, channels, num_frames, height, width = video.shape
outputs = []
for batch_idx in range(batch_size):
batch_vid = video[batch_idx].permute(1, 0, 2, 3)
batch_output = processor.postprocess(batch_vid, output_type)
outputs.append(batch_output)
if output_type == "np":
outputs = np.stack(outputs)
elif output_type == "pt":
outputs = torch.stack(outputs)
elif not output_type == "pil":
raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil]")
return outputs
def preprocess_video(video):
supported_formats = (np.ndarray, torch.Tensor, PIL.Image.Image)
if isinstance(video, supported_formats):
video = [video]
elif not (isinstance(video, list) and all(isinstance(i, supported_formats) for i in video)):
raise ValueError(
f"Input is in incorrect format: {[type(i) for i in video]}. Currently, we only support {', '.join(supported_formats)}"
)
if isinstance(video[0], PIL.Image.Image):
video = [np.array(frame) for frame in video]
if isinstance(video[0], np.ndarray):
video = np.concatenate(video, axis=0) if video[0].ndim == 5 else np.stack(video, axis=0)
if video.dtype == np.uint8:
video = np.array(video).astype(np.float32) / 255.0
if video.ndim == 4:
video = video[None, ...]
video = torch.from_numpy(video.transpose(0, 4, 1, 2, 3))
elif isinstance(video[0], torch.Tensor):
video = torch.cat(video, axis=0) if video[0].ndim == 5 else torch.stack(video, axis=0)
# don't need any preprocess if the video is latents
channel = video.shape[1]
if channel == 4:
return video
# move channels before num_frames
video = video.permute(0, 2, 1, 3, 4)
# normalize video
video = 2.0 * video - 1.0
return video
class VideoToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-guided video-to-video generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet3DConditionModel`]):
A [`UNet3DConditionModel`] to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
model_cpu_offload_seq = "text_encoder->unet->vae"
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
**kwargs,
):
deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=lora_scale,
**kwargs,
)
# concatenate for backwards comp
prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
clip_skip: Optional[int] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
)
# Access the `hidden_states` first, that contains a tuple of
# all the hidden states from the encoder layers. Then index into
# the tuple to access the hidden states from the desired layer.
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
# We also need to apply the final LayerNorm here to not mess with the
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer.
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
batch_size, channels, num_frames, height, width = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
image = self.vae.decode(latents).sample
video = (
image[None, :]
.reshape(
(
batch_size,
num_frames,
-1,
)
+ image.shape[2:]
)
.permute(0, 2, 1, 3, 4)
)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
video = video.float()
return video
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
def check_inputs(
self,
prompt,
strength,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
def prepare_latents(self, video, timestep, batch_size, dtype, device, generator=None):
video = video.to(device=device, dtype=dtype)
# change from (b, c, f, h, w) -> (b * f, c, w, h)
bsz, channel, frames, width, height = video.shape
video = video.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
if video.shape[1] == 4:
init_latents = video
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
elif isinstance(generator, list):
init_latents = [
retrieve_latents(self.vae.encode(video[i : i + 1]), generator=generator[i])
for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = retrieve_latents(self.vae.encode(video), generator=generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `video` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
latents = latents[None, :].reshape((bsz, frames, latents.shape[1]) + latents.shape[2:]).permute(0, 2, 1, 3, 4)
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stages where they are being applied.
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
if not hasattr(self, "unet"):
raise ValueError("The pipeline must have `unet` for using FreeU.")
self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
def disable_freeu(self):
"""Disables the FreeU mechanism if enabled."""
self.unet.disable_freeu()
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
video: Union[List[np.ndarray], torch.FloatTensor] = None,
strength: float = 0.6,
num_inference_steps: int = 50,
guidance_scale: float = 15.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "np",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
video (`List[np.ndarray]` or `torch.FloatTensor`):
`video` frames or tensor representing a video batch to be used as the starting point for the process.
Can also accept video latents as `image`, if passing latents directly, it will not be encoded again.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `video`. Must be between 0 and 1. `video` is used as a
starting point, adding more noise to it the larger the `strength`. The number of denoising steps
depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the
denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of
1 essentially ignores `video`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in video generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
`(batch_size, num_channel, num_frames, height, width)`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"np"`):
The output format of the generated video. Choose between `torch.FloatTensor` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
Examples:
Returns:
[`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
"""
# 0. Default height and width to unet
num_images_per_prompt = 1
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
clip_skip=clip_skip,
)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# 4. Preprocess video
video = preprocess_video(video)
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 5. Prepare latent variables
latents = self.prepare_latents(video, latent_timestep, batch_size, prompt_embeds.dtype, device, generator)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# reshape latents
bsz, channel, frames, width, height = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# reshape latents back
latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if output_type == "latent":
return TextToVideoSDPipelineOutput(frames=latents)
# manually for max memory savings
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.unet.to("cpu")
if output_type == "latent":
return TextToVideoSDPipelineOutput(frames=latents)
video_tensor = self.decode_latents(latents)
video = tensor2vid(video_tensor, self.image_processor, output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return TextToVideoSDPipelineOutput(frames=video)
| diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py",
"repo_id": "diffusers",
"token_count": 17537
} | 128 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, Dict, List, Optional, Union
import numpy as np
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...schedulers import DDPMWuerstchenScheduler
from ...utils import deprecate, logging, replace_example_docstring
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .modeling_paella_vq_model import PaellaVQModel
from .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import WuerstchenPriorPipeline, WuerstchenDecoderPipeline
>>> prior_pipe = WuerstchenPriorPipeline.from_pretrained(
... "warp-ai/wuerstchen-prior", torch_dtype=torch.float16
... ).to("cuda")
>>> gen_pipe = WuerstchenDecoderPipeline.from_pretrain("warp-ai/wuerstchen", torch_dtype=torch.float16).to(
... "cuda"
... )
>>> prompt = "an image of a shiba inu, donning a spacesuit and helmet"
>>> prior_output = pipe(prompt)
>>> images = gen_pipe(prior_output.image_embeddings, prompt=prompt)
```
"""
class WuerstchenDecoderPipeline(DiffusionPipeline):
"""
Pipeline for generating images from the Wuerstchen model.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
tokenizer (`CLIPTokenizer`):
The CLIP tokenizer.
text_encoder (`CLIPTextModel`):
The CLIP text encoder.
decoder ([`WuerstchenDiffNeXt`]):
The WuerstchenDiffNeXt unet decoder.
vqgan ([`PaellaVQModel`]):
The VQGAN model.
scheduler ([`DDPMWuerstchenScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
latent_dim_scale (float, `optional`, defaults to 10.67):
Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are
height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and
width=int(24*10.67)=256 in order to match the training conditions.
"""
model_cpu_offload_seq = "text_encoder->decoder->vqgan"
_callback_tensor_inputs = [
"latents",
"text_encoder_hidden_states",
"negative_prompt_embeds",
"image_embeddings",
]
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModel,
decoder: WuerstchenDiffNeXt,
scheduler: DDPMWuerstchenScheduler,
vqgan: PaellaVQModel,
latent_dim_scale: float = 10.67,
) -> None:
super().__init__()
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
decoder=decoder,
scheduler=scheduler,
vqgan=vqgan,
)
self.register_to_config(latent_dim_scale=latent_dim_scale)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
attention_mask = text_inputs.attention_mask
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
attention_mask = attention_mask[:, : self.tokenizer.model_max_length]
text_encoder_output = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask.to(device))
text_encoder_hidden_states = text_encoder_output.last_hidden_state
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_text_encoder_hidden_states = None
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds_text_encoder_output = self.text_encoder(
uncond_input.input_ids.to(device), attention_mask=uncond_input.attention_mask.to(device)
)
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
return text_encoder_hidden_states, uncond_text_encoder_hidden_states
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def num_timesteps(self):
return self._num_timesteps
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image_embeddings: Union[torch.FloatTensor, List[torch.FloatTensor]],
prompt: Union[str, List[str]] = None,
num_inference_steps: int = 12,
timesteps: Optional[List[float]] = None,
guidance_scale: float = 0.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
"""
Function invoked when calling the pipeline for generation.
Args:
image_embedding (`torch.FloatTensor` or `List[torch.FloatTensor]`):
Image Embeddings either extracted from an image or generated by a Prior Model.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
num_inference_steps (`int`, *optional*, defaults to 12):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 0.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
`decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely
linked to the text `prompt`, usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `decoder_guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image
embeddings.
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
# 0. Define commonly used variables
device = self._execution_device
dtype = self.decoder.dtype
self._guidance_scale = guidance_scale
# 1. Check inputs. Raise error if not correct
if not isinstance(prompt, list):
if isinstance(prompt, str):
prompt = [prompt]
else:
raise TypeError(f"'prompt' must be of type 'list' or 'str', but got {type(prompt)}.")
if self.do_classifier_free_guidance:
if negative_prompt is not None and not isinstance(negative_prompt, list):
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt]
else:
raise TypeError(
f"'negative_prompt' must be of type 'list' or 'str', but got {type(negative_prompt)}."
)
if isinstance(image_embeddings, list):
image_embeddings = torch.cat(image_embeddings, dim=0)
if isinstance(image_embeddings, np.ndarray):
image_embeddings = torch.Tensor(image_embeddings, device=device).to(dtype=dtype)
if not isinstance(image_embeddings, torch.Tensor):
raise TypeError(
f"'image_embeddings' must be of type 'torch.Tensor' or 'np.array', but got {type(image_embeddings)}."
)
if not isinstance(num_inference_steps, int):
raise TypeError(
f"'num_inference_steps' must be of type 'int', but got {type(num_inference_steps)}\
In Case you want to provide explicit timesteps, please use the 'timesteps' argument."
)
# 2. Encode caption
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
image_embeddings.size(0) * num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
)
text_encoder_hidden_states = (
torch.cat([prompt_embeds, negative_prompt_embeds]) if negative_prompt_embeds is not None else prompt_embeds
)
# 3. Determine latent shape of latents
latent_height = int(image_embeddings.size(2) * self.config.latent_dim_scale)
latent_width = int(image_embeddings.size(3) * self.config.latent_dim_scale)
latent_features_shape = (image_embeddings.size(0) * num_images_per_prompt, 4, latent_height, latent_width)
# 4. Prepare and set timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latents
latents = self.prepare_latents(latent_features_shape, dtype, device, generator, latents, self.scheduler)
# 6. Run denoising loop
self._num_timesteps = len(timesteps[:-1])
for i, t in enumerate(self.progress_bar(timesteps[:-1])):
ratio = t.expand(latents.size(0)).to(dtype)
effnet = (
torch.cat([image_embeddings, torch.zeros_like(image_embeddings)])
if self.do_classifier_free_guidance
else image_embeddings
)
# 7. Denoise latents
predicted_latents = self.decoder(
torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,
r=torch.cat([ratio] * 2) if self.do_classifier_free_guidance else ratio,
effnet=effnet,
clip=text_encoder_hidden_states,
)
# 8. Check for classifier free guidance and apply it
if self.do_classifier_free_guidance:
predicted_latents_text, predicted_latents_uncond = predicted_latents.chunk(2)
predicted_latents = torch.lerp(predicted_latents_uncond, predicted_latents_text, self.guidance_scale)
# 9. Renoise latents to next timestep
latents = self.scheduler.step(
model_output=predicted_latents,
timestep=ratio,
sample=latents,
generator=generator,
).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
image_embeddings = callback_outputs.pop("image_embeddings", image_embeddings)
text_encoder_hidden_states = callback_outputs.pop(
"text_encoder_hidden_states", text_encoder_hidden_states
)
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError(
f"Only the output types `pt`, `np`, `pil` and `latent` are supported not output_type={output_type}"
)
if not output_type == "latent":
# 10. Scale and decode the image latents with vq-vae
latents = self.vqgan.config.scale_factor * latents
images = self.vqgan.decode(latents).sample.clamp(0, 1)
if output_type == "np":
images = images.permute(0, 2, 3, 1).cpu().numpy()
elif output_type == "pil":
images = images.permute(0, 2, 3, 1).cpu().numpy()
images = self.numpy_to_pil(images)
else:
images = latents
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return images
return ImagePipelineOutput(images)
| diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py",
"repo_id": "diffusers",
"token_count": 9246
} | 129 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, List, Optional, Union
import numpy as np
import torch
from .models import UNet2DConditionModel
from .utils import (
convert_state_dict_to_diffusers,
convert_state_dict_to_peft,
deprecate,
is_peft_available,
is_torchvision_available,
is_transformers_available,
)
if is_transformers_available():
import transformers
if is_peft_available():
from peft import set_peft_model_state_dict
if is_torchvision_available():
from torchvision import transforms
def set_seed(seed: int):
"""
Args:
Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
seed (`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# ^^ safe to call this function even if cuda is not available
def compute_snr(noise_scheduler, timesteps):
"""
Computes SNR as per
https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
"""
alphas_cumprod = noise_scheduler.alphas_cumprod
sqrt_alphas_cumprod = alphas_cumprod**0.5
sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
# Expand the tensors.
# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
# Compute SNR.
snr = (alpha / sigma) ** 2
return snr
def resolve_interpolation_mode(interpolation_type: str):
"""
Maps a string describing an interpolation function to the corresponding torchvision `InterpolationMode` enum. The
full list of supported enums is documented at
https://pytorch.org/vision/0.9/transforms.html#torchvision.transforms.functional.InterpolationMode.
Args:
interpolation_type (`str`):
A string describing an interpolation method. Currently, `bilinear`, `bicubic`, `box`, `nearest`,
`nearest_exact`, `hamming`, and `lanczos` are supported, corresponding to the supported interpolation modes
in torchvision.
Returns:
`torchvision.transforms.InterpolationMode`: an `InterpolationMode` enum used by torchvision's `resize`
transform.
"""
if not is_torchvision_available():
raise ImportError(
"Please make sure to install `torchvision` to be able to use the `resolve_interpolation_mode()` function."
)
if interpolation_type == "bilinear":
interpolation_mode = transforms.InterpolationMode.BILINEAR
elif interpolation_type == "bicubic":
interpolation_mode = transforms.InterpolationMode.BICUBIC
elif interpolation_type == "box":
interpolation_mode = transforms.InterpolationMode.BOX
elif interpolation_type == "nearest":
interpolation_mode = transforms.InterpolationMode.NEAREST
elif interpolation_type == "nearest_exact":
interpolation_mode = transforms.InterpolationMode.NEAREST_EXACT
elif interpolation_type == "hamming":
interpolation_mode = transforms.InterpolationMode.HAMMING
elif interpolation_type == "lanczos":
interpolation_mode = transforms.InterpolationMode.LANCZOS
else:
raise ValueError(
f"The given interpolation mode {interpolation_type} is not supported. Currently supported interpolation"
f" modes are `bilinear`, `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`."
)
return interpolation_mode
def unet_lora_state_dict(unet: UNet2DConditionModel) -> Dict[str, torch.Tensor]:
r"""
Returns:
A state dict containing just the LoRA parameters.
"""
lora_state_dict = {}
for name, module in unet.named_modules():
if hasattr(module, "set_lora_layer"):
lora_layer = getattr(module, "lora_layer")
if lora_layer is not None:
current_lora_layer_sd = lora_layer.state_dict()
for lora_layer_matrix_name, lora_param in current_lora_layer_sd.items():
# The matrix name can either be "down" or "up".
lora_state_dict[f"{name}.lora.{lora_layer_matrix_name}"] = lora_param
return lora_state_dict
def cast_training_params(model: Union[torch.nn.Module, List[torch.nn.Module]], dtype=torch.float32):
if not isinstance(model, list):
model = [model]
for m in model:
for param in m.parameters():
# only upcast trainable parameters into fp32
if param.requires_grad:
param.data = param.to(dtype)
def _set_state_dict_into_text_encoder(
lora_state_dict: Dict[str, torch.Tensor], prefix: str, text_encoder: torch.nn.Module
):
"""
Sets the `lora_state_dict` into `text_encoder` coming from `transformers`.
Args:
lora_state_dict: The state dictionary to be set.
prefix: String identifier to retrieve the portion of the state dict that belongs to `text_encoder`.
text_encoder: Where the `lora_state_dict` is to be set.
"""
text_encoder_state_dict = {
f'{k.replace(prefix, "")}': v for k, v in lora_state_dict.items() if k.startswith(prefix)
}
text_encoder_state_dict = convert_state_dict_to_peft(convert_state_dict_to_diffusers(text_encoder_state_dict))
set_peft_model_state_dict(text_encoder, text_encoder_state_dict, adapter_name="default")
# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(
self,
parameters: Iterable[torch.nn.Parameter],
decay: float = 0.9999,
min_decay: float = 0.0,
update_after_step: int = 0,
use_ema_warmup: bool = False,
inv_gamma: Union[float, int] = 1.0,
power: Union[float, int] = 2 / 3,
model_cls: Optional[Any] = None,
model_config: Dict[str, Any] = None,
**kwargs,
):
"""
Args:
parameters (Iterable[torch.nn.Parameter]): The parameters to track.
decay (float): The decay factor for the exponential moving average.
min_decay (float): The minimum decay factor for the exponential moving average.
update_after_step (int): The number of steps to wait before starting to update the EMA weights.
use_ema_warmup (bool): Whether to use EMA warmup.
inv_gamma (float):
Inverse multiplicative factor of EMA warmup. Default: 1. Only used if `use_ema_warmup` is True.
power (float): Exponential factor of EMA warmup. Default: 2/3. Only used if `use_ema_warmup` is True.
device (Optional[Union[str, torch.device]]): The device to store the EMA weights on. If None, the EMA
weights will be stored on CPU.
@crowsonkb's notes on EMA Warmup:
If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
at 215.4k steps).
"""
if isinstance(parameters, torch.nn.Module):
deprecation_message = (
"Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
"Please pass the parameters of the module instead."
)
deprecate(
"passing a `torch.nn.Module` to `ExponentialMovingAverage`",
"1.0.0",
deprecation_message,
standard_warn=False,
)
parameters = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
use_ema_warmup = True
if kwargs.get("max_value", None) is not None:
deprecation_message = "The `max_value` argument is deprecated. Please use `decay` instead."
deprecate("max_value", "1.0.0", deprecation_message, standard_warn=False)
decay = kwargs["max_value"]
if kwargs.get("min_value", None) is not None:
deprecation_message = "The `min_value` argument is deprecated. Please use `min_decay` instead."
deprecate("min_value", "1.0.0", deprecation_message, standard_warn=False)
min_decay = kwargs["min_value"]
parameters = list(parameters)
self.shadow_params = [p.clone().detach() for p in parameters]
if kwargs.get("device", None) is not None:
deprecation_message = "The `device` argument is deprecated. Please use `to` instead."
deprecate("device", "1.0.0", deprecation_message, standard_warn=False)
self.to(device=kwargs["device"])
self.temp_stored_params = None
self.decay = decay
self.min_decay = min_decay
self.update_after_step = update_after_step
self.use_ema_warmup = use_ema_warmup
self.inv_gamma = inv_gamma
self.power = power
self.optimization_step = 0
self.cur_decay_value = None # set in `step()`
self.model_cls = model_cls
self.model_config = model_config
@classmethod
def from_pretrained(cls, path, model_cls) -> "EMAModel":
_, ema_kwargs = model_cls.load_config(path, return_unused_kwargs=True)
model = model_cls.from_pretrained(path)
ema_model = cls(model.parameters(), model_cls=model_cls, model_config=model.config)
ema_model.load_state_dict(ema_kwargs)
return ema_model
def save_pretrained(self, path):
if self.model_cls is None:
raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__.")
if self.model_config is None:
raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__.")
model = self.model_cls.from_config(self.model_config)
state_dict = self.state_dict()
state_dict.pop("shadow_params", None)
model.register_to_config(**state_dict)
self.copy_to(model.parameters())
model.save_pretrained(path)
def get_decay(self, optimization_step: int) -> float:
"""
Compute the decay factor for the exponential moving average.
"""
step = max(0, optimization_step - self.update_after_step - 1)
if step <= 0:
return 0.0
if self.use_ema_warmup:
cur_decay_value = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
cur_decay_value = (1 + step) / (10 + step)
cur_decay_value = min(cur_decay_value, self.decay)
# make sure decay is not smaller than min_decay
cur_decay_value = max(cur_decay_value, self.min_decay)
return cur_decay_value
@torch.no_grad()
def step(self, parameters: Iterable[torch.nn.Parameter]):
if isinstance(parameters, torch.nn.Module):
deprecation_message = (
"Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
"Please pass the parameters of the module instead."
)
deprecate(
"passing a `torch.nn.Module` to `ExponentialMovingAverage.step`",
"1.0.0",
deprecation_message,
standard_warn=False,
)
parameters = parameters.parameters()
parameters = list(parameters)
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
decay = self.get_decay(self.optimization_step)
self.cur_decay_value = decay
one_minus_decay = 1 - decay
context_manager = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params, parameters):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled():
context_manager = deepspeed.zero.GatheredParameters(param, modifier_rank=None)
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param))
else:
s_param.copy_(param)
def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
"""
Copy current averaged parameters into given collection of parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored moving averages. If `None`, the parameters with which this
`ExponentialMovingAverage` was initialized will be used.
"""
parameters = list(parameters)
for s_param, param in zip(self.shadow_params, parameters):
param.data.copy_(s_param.to(param.device).data)
def to(self, device=None, dtype=None) -> None:
r"""Move internal buffers of the ExponentialMovingAverage to `device`.
Args:
device: like `device` argument to `torch.Tensor.to`
"""
# .to() on the tensors handles None correctly
self.shadow_params = [
p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
for p in self.shadow_params
]
def state_dict(self) -> dict:
r"""
Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during
checkpointing to save the ema state dict.
"""
# Following PyTorch conventions, references to tensors are returned:
# "returns a reference to the state and not its copy!" -
# https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def store(self, parameters: Iterable[torch.nn.Parameter]) -> None:
r"""
Args:
Save the current parameters for restoring later.
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
temporarily stored.
"""
self.temp_stored_params = [param.detach().cpu().clone() for param in parameters]
def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:
r"""
Args:
Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without:
affecting the original optimization process. Store the parameters before the `copy_to()` method. After
validation (or model saving), use this to restore the former parameters.
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored parameters. If `None`, the parameters with which this
`ExponentialMovingAverage` was initialized will be used.
"""
if self.temp_stored_params is None:
raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`")
for c_param, param in zip(self.temp_stored_params, parameters):
param.data.copy_(c_param.data)
# Better memory-wise.
self.temp_stored_params = None
def load_state_dict(self, state_dict: dict) -> None:
r"""
Args:
Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the
ema state dict.
state_dict (dict): EMA state. Should be an object returned
from a call to :meth:`state_dict`.
"""
# deepcopy, to be consistent with module API
state_dict = copy.deepcopy(state_dict)
self.decay = state_dict.get("decay", self.decay)
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError("Decay must be between 0 and 1")
self.min_decay = state_dict.get("min_decay", self.min_decay)
if not isinstance(self.min_decay, float):
raise ValueError("Invalid min_decay")
self.optimization_step = state_dict.get("optimization_step", self.optimization_step)
if not isinstance(self.optimization_step, int):
raise ValueError("Invalid optimization_step")
self.update_after_step = state_dict.get("update_after_step", self.update_after_step)
if not isinstance(self.update_after_step, int):
raise ValueError("Invalid update_after_step")
self.use_ema_warmup = state_dict.get("use_ema_warmup", self.use_ema_warmup)
if not isinstance(self.use_ema_warmup, bool):
raise ValueError("Invalid use_ema_warmup")
self.inv_gamma = state_dict.get("inv_gamma", self.inv_gamma)
if not isinstance(self.inv_gamma, (float, int)):
raise ValueError("Invalid inv_gamma")
self.power = state_dict.get("power", self.power)
if not isinstance(self.power, (float, int)):
raise ValueError("Invalid power")
shadow_params = state_dict.get("shadow_params", None)
if shadow_params is not None:
self.shadow_params = shadow_params
if not isinstance(self.shadow_params, list):
raise ValueError("shadow_params must be a list")
if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):
raise ValueError("shadow_params must all be Tensors")
| diffusers/src/diffusers/training_utils.py/0 | {
"file_path": "diffusers/src/diffusers/training_utils.py",
"repo_id": "diffusers",
"token_count": 7930
} | 130 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class AltDiffusionImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AltDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AmusedImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AmusedInpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AmusedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AnimateDiffPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AnimateDiffVideoToVideoPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AudioLDM2Pipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AudioLDM2ProjectionModel(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AudioLDM2UNet2DConditionModel(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class AudioLDMPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class CLIPImageProjection(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class CycleDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class I2VGenXLPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class IFImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class IFImg2ImgSuperResolutionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class IFInpaintingPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class IFInpaintingSuperResolutionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class IFPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class IFSuperResolutionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class ImageTextPipelineOutput(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class Kandinsky3Img2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class Kandinsky3Pipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyCombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyImg2ImgCombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyInpaintCombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyInpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyPriorPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22CombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22ControlnetImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22ControlnetPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22Img2ImgCombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22Img2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22InpaintCombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22InpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22Pipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22PriorEmb2EmbPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class KandinskyV22PriorPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class LatentConsistencyModelImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class LatentConsistencyModelPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class LDMTextToImagePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class MusicLDMPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class PaintByExamplePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class PIAPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class PixArtAlphaPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class SemanticStableDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class ShapEImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class ShapEPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionAdapterPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionAttendAndExcitePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionControlNetImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionControlNetInpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionControlNetPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionDepth2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionDiffEditPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionGLIGENPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionGLIGENTextImagePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionImageVariationPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionInpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionInpaintPipelineLegacy(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionInstructPix2PixPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionLatentUpscalePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionLDM3DPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionModelEditingPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionPanoramaPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionParadigmsPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionPipelineSafe(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionPix2PixZeroPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionSAGPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionUpscalePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLAdapterPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLControlNetImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLControlNetInpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLControlNetPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLInpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLInstructPix2PixPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableUnCLIPImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableUnCLIPPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableVideoDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class TextToVideoSDPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class TextToVideoZeroPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class TextToVideoZeroSDXLPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class UnCLIPImageVariationPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class UnCLIPPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class UniDiffuserModel(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class UniDiffuserPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class UniDiffuserTextDecoder(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class VersatileDiffusionDualGuidedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class VersatileDiffusionImageVariationPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class VersatileDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class VersatileDiffusionTextToImagePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class VideoToVideoSDPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class VQDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class WuerstchenCombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class WuerstchenDecoderPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class WuerstchenPriorPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
| diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py",
"repo_id": "diffusers",
"token_count": 18600
} | 131 |
import inspect
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
@require_flax
class FlaxModelTesterMixin:
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
variables = model.init(inputs_dict["prng_key"], inputs_dict["sample"])
jax.lax.stop_gradient(variables)
output = model.apply(variables, inputs_dict["sample"])
if isinstance(output, dict):
output = output.sample
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_forward_with_norm_groups(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["norm_num_groups"] = 16
init_dict["block_out_channels"] = (16, 32)
model = self.model_class(**init_dict)
variables = model.init(inputs_dict["prng_key"], inputs_dict["sample"])
jax.lax.stop_gradient(variables)
output = model.apply(variables, inputs_dict["sample"])
if isinstance(output, dict):
output = output.sample
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_deprecated_kwargs(self):
has_kwarg_in_model_class = "kwargs" in inspect.signature(self.model_class.__init__).parameters
has_deprecated_kwarg = len(self.model_class._deprecated_kwargs) > 0
if has_kwarg_in_model_class and not has_deprecated_kwarg:
raise ValueError(
f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
" under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
" no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
" [<deprecated_argument>]`"
)
if not has_kwarg_in_model_class and has_deprecated_kwarg:
raise ValueError(
f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
" under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
" from `_deprecated_kwargs = [<deprecated_argument>]`"
)
| diffusers/tests/models/test_modeling_common_flax.py/0 | {
"file_path": "diffusers/tests/models/test_modeling_common_flax.py",
"repo_id": "diffusers",
"token_count": 1124
} | 132 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import unittest
from importlib import import_module
class DependencyTester(unittest.TestCase):
def test_diffusers_import(self):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def test_backend_registration(self):
import diffusers
from diffusers.dependency_versions_table import deps
all_classes = inspect.getmembers(diffusers, inspect.isclass)
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
backend = "k-diffusion"
elif backend == "invisible_watermark":
backend = "invisible-watermark"
assert backend in deps, f"{backend} is not in the deps table!"
def test_pipeline_imports(self):
import diffusers
import diffusers.pipelines
all_classes = inspect.getmembers(diffusers, inspect.isclass)
for cls_name, cls_module in all_classes:
if hasattr(diffusers.pipelines, cls_name):
pipeline_folder_module = ".".join(str(cls_module.__module__).split(".")[:3])
_ = import_module(pipeline_folder_module, str(cls_name))
| diffusers/tests/others/test_dependencies.py/0 | {
"file_path": "diffusers/tests/others/test_dependencies.py",
"repo_id": "diffusers",
"token_count": 776
} | 133 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechT5HifiGan,
SpeechT5HifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, nightly, torch_device
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class AudioLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = AudioLDMPipeline
params = TEXT_TO_AUDIO_PARAMS
batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
]
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=(32, 64),
class_embed_type="simple_projection",
projection_class_embeddings_input_dim=32,
class_embeddings_concat=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=1,
out_channels=1,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = ClapTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
projection_dim=32,
)
text_encoder = ClapTextModelWithProjection(text_encoder_config)
tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
vocoder_config = SpeechT5HifiGanConfig(
model_in_dim=8,
sampling_rate=16000,
upsample_initial_channel=16,
upsample_rates=[2, 2],
upsample_kernel_sizes=[4, 4],
resblock_kernel_sizes=[3, 7],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
normalize_before=False,
)
vocoder = SpeechT5HifiGan(vocoder_config)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"vocoder": vocoder,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
}
return inputs
def test_audioldm_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(**inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = audioldm_pipe.tokenizer(
prompt,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
prompt_embeds = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
prompt_embeds = F.normalize(prompt_embeds, dim=-1)
inputs["prompt_embeds"] = prompt_embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = audioldm_pipe.tokenizer(
p,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
text_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
text_embeds = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
text_embeds = F.normalize(text_embeds, dim=-1)
embeds.append(text_embeds)
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "egg cracking"
output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_num_waveforms_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = "A hammer hitting a wooden surface"
# test num_waveforms_per_prompt=1 (default)
audios = audioldm_pipe(prompt, num_inference_steps=2).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
batch_size = 2
audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
num_waveforms_per_prompt = 2
audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
batch_size = 2
audios = audioldm_pipe(
[prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def test_audioldm_audio_length_in_s(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(audio_length_in_s=0.016, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.016
output = audioldm_pipe(audio_length_in_s=0.032, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.032
def test_audioldm_vocoder_model_in_dim(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = ["hey"]
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
assert audio_shape == (1, 256)
config = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical()
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
@nightly
class AudioLDMPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[77230:77240]
expected_slice = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]
)
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-2
@nightly
class AudioLDMPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm_lms(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[27780:27790]
expected_slice = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 3e-2
| diffusers/tests/pipelines/audioldm/test_audioldm.py/0 | {
"file_path": "diffusers/tests/pipelines/audioldm/test_audioldm.py",
"repo_id": "diffusers",
"token_count": 7499
} | 134 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
I2VGenXLPipeline,
)
from diffusers.models.unets import I2VGenXLUNet
from diffusers.utils import is_xformers_available, load_image
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
numpy_cosine_similarity_distance,
print_tensor_test,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class I2VGenXLPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = I2VGenXLPipeline
params = frozenset(["prompt", "negative_prompt", "image"])
batch_params = frozenset(["prompt", "negative_prompt", "image", "generator"])
# No `output_type`.
required_optional_params = frozenset(["num_inference_steps", "generator", "latents", "return_dict"])
def get_dummy_components(self):
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
unet = I2VGenXLUNet(
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock3D", "DownBlock3D"),
up_block_types=("UpBlock3D", "CrossAttnUpBlock3D"),
cross_attention_dim=4,
num_attention_heads=4,
norm_num_groups=2,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=(8,),
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D"],
latent_channels=4,
sample_size=32,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=4,
intermediate_size=16,
layer_norm_eps=1e-05,
num_attention_heads=2,
num_hidden_layers=2,
pad_token_id=1,
vocab_size=1000,
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
vision_encoder_config = CLIPVisionConfig(
hidden_size=4,
projection_dim=4,
num_hidden_layers=2,
num_attention_heads=2,
image_size=32,
intermediate_size=16,
patch_size=1,
)
image_encoder = CLIPVisionModelWithProjection(vision_encoder_config)
torch.manual_seed(0)
feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"image_encoder": image_encoder,
"tokenizer": tokenizer,
"feature_extractor": feature_extractor,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
"num_frames": 4,
"width": 32,
"height": 32,
}
return inputs
def test_text_to_video_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["output_type"] = "np"
frames = pipe(**inputs).frames
image_slice = frames[0][0][-3:, -3:, -1]
assert frames[0][0].shape == (32, 32, 3)
expected_slice = np.array([0.5146, 0.6525, 0.6032, 0.5204, 0.5675, 0.4125, 0.3016, 0.5172, 0.4095])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_save_load_local(self):
super().test_save_load_local(expected_max_difference=0.006)
def test_sequential_cpu_offload_forward_pass(self):
super().test_sequential_cpu_offload_forward_pass(expected_max_diff=0.008)
def test_dict_tuple_outputs_equivalent(self):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=0.008)
def test_save_load_optional_components(self):
super().test_save_load_optional_components(expected_max_difference=0.008)
@unittest.skip("Deprecated functionality")
def test_attention_slicing_forward_pass(self):
pass
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=1e-2)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=0.008)
def test_model_cpu_offload_forward_pass(self):
super().test_model_cpu_offload_forward_pass(expected_max_diff=0.008)
def test_num_videos_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["output_type"] = "np"
frames = pipe(**inputs, num_videos_per_prompt=2).frames
assert frames.shape == (2, 4, 32, 32, 3)
assert frames[0][0].shape == (32, 32, 3)
image_slice = frames[0][0][-3:, -3:, -1]
expected_slice = np.array([0.5146, 0.6525, 0.6032, 0.5204, 0.5675, 0.4125, 0.3016, 0.5172, 0.4095])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch_gpu
class I2VGenXLPipelineSlowTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_i2vgen_xl(self):
pipe = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16")
pipe = pipe.to(torch_device)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true"
)
generator = torch.Generator("cpu").manual_seed(0)
num_frames = 3
output = pipe(
image=image,
prompt="my cat",
num_frames=num_frames,
generator=generator,
num_inference_steps=3,
output_type="np",
)
image = output.frames[0]
assert image.shape == (num_frames, 704, 1280, 3)
image_slice = image[0, -3:, -3:, -1]
print_tensor_test(image_slice.flatten())
expected_slice = np.array([0.5482, 0.6244, 0.6274, 0.4584, 0.5935, 0.5937, 0.4579, 0.5767, 0.5892])
assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3
| diffusers/tests/pipelines/i2vgen_xl/test_i2vgenxl.py/0 | {
"file_path": "diffusers/tests/pipelines/i2vgen_xl/test_i2vgenxl.py",
"repo_id": "diffusers",
"token_count": 4207
} | 135 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyV22PriorEmb2EmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, skip_mps, torch_device
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class KandinskyV22PriorEmb2EmbPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = KandinskyV22PriorEmb2EmbPipeline
params = ["prompt", "image"]
batch_params = ["prompt", "image"]
required_optional_params = [
"num_images_per_prompt",
"strength",
"generator",
"num_inference_steps",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
test_xformers_attention = False
@property
def text_embedder_hidden_size(self):
return 32
@property
def time_input_dim(self):
return 32
@property
def block_out_channels_0(self):
return self.time_input_dim
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def cross_attention_dim(self):
return 100
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=self.text_embedder_hidden_size,
projection_dim=self.text_embedder_hidden_size,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModelWithProjection(config)
@property
def dummy_prior(self):
torch.manual_seed(0)
model_kwargs = {
"num_attention_heads": 2,
"attention_head_dim": 12,
"embedding_dim": self.text_embedder_hidden_size,
"num_layers": 1,
}
model = PriorTransformer(**model_kwargs)
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
model.clip_std = nn.Parameter(torch.ones(model.clip_std.shape))
return model
@property
def dummy_image_encoder(self):
torch.manual_seed(0)
config = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size,
image_size=224,
projection_dim=self.text_embedder_hidden_size,
intermediate_size=37,
num_attention_heads=4,
num_channels=3,
num_hidden_layers=5,
patch_size=14,
)
model = CLIPVisionModelWithProjection(config)
return model
@property
def dummy_image_processor(self):
image_processor = CLIPImageProcessor(
crop_size=224,
do_center_crop=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=224,
)
return image_processor
def get_dummy_components(self):
prior = self.dummy_prior
image_encoder = self.dummy_image_encoder
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
image_processor = self.dummy_image_processor
scheduler = UnCLIPScheduler(
variance_type="fixed_small_log",
prediction_type="sample",
num_train_timesteps=1000,
clip_sample=True,
clip_sample_range=10.0,
)
components = {
"prior": prior,
"image_encoder": image_encoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
"image_processor": image_processor,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((256, 256))
inputs = {
"prompt": "horse",
"image": init_image,
"strength": 0.5,
"generator": generator,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def test_kandinsky_prior_emb2emb(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.image_embeds
image_from_tuple = pipe(
**self.get_dummy_inputs(device),
return_dict=False,
)[0]
image_slice = image[0, -10:]
image_from_tuple_slice = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
expected_slice = np.array(
[
0.1071284,
1.3330271,
0.61260223,
-0.6691065,
-0.3846852,
-1.0303661,
0.22716111,
0.03348901,
0.30040675,
-0.24805029,
]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@skip_mps
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=1e-2)
@skip_mps
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
test_mean_pixel_difference = False
self._test_attention_slicing_forward_pass(
test_max_difference=test_max_difference,
test_mean_pixel_difference=test_mean_pixel_difference,
)
| diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_prior_emb2emb.py/0 | {
"file_path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_prior_emb2emb.py",
"repo_id": "diffusers",
"token_count": 3479
} | 136 |
# These are canonical sets of parameters for different types of pipelines.
# They are set on subclasses of `PipelineTesterMixin` as `params` and
# `batch_params`.
#
# If a pipeline's set of arguments has minor changes from one of the common sets
# of arguments, do not make modifications to the existing common sets of arguments.
# I.e. a text to image pipeline with non-configurable height and width arguments
# should set its attribute as `params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`.
TEXT_TO_IMAGE_PARAMS = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
TEXT_TO_IMAGE_BATCH_PARAMS = frozenset(["prompt", "negative_prompt"])
TEXT_TO_IMAGE_IMAGE_PARAMS = frozenset([])
IMAGE_TO_IMAGE_IMAGE_PARAMS = frozenset(["image"])
IMAGE_VARIATION_PARAMS = frozenset(
[
"image",
"height",
"width",
"guidance_scale",
]
)
IMAGE_VARIATION_BATCH_PARAMS = frozenset(["image"])
TEXT_GUIDED_IMAGE_VARIATION_PARAMS = frozenset(
[
"prompt",
"image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS = frozenset(["prompt", "image", "negative_prompt"])
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS = frozenset(
[
# Text guided image variation with an image mask
"prompt",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS = frozenset(["prompt", "image", "mask_image", "negative_prompt"])
IMAGE_INPAINTING_PARAMS = frozenset(
[
# image variation with an image mask
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
IMAGE_INPAINTING_BATCH_PARAMS = frozenset(["image", "mask_image"])
IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS = frozenset(
[
"example_image",
"image",
"mask_image",
"height",
"width",
"guidance_scale",
]
)
IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS = frozenset(["example_image", "image", "mask_image"])
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS = frozenset(["class_labels"])
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS = frozenset(["class_labels"])
UNCONDITIONAL_IMAGE_GENERATION_PARAMS = frozenset(["batch_size"])
UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS = frozenset([])
UNCONDITIONAL_AUDIO_GENERATION_PARAMS = frozenset(["batch_size"])
UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS = frozenset([])
TEXT_TO_AUDIO_PARAMS = frozenset(
[
"prompt",
"audio_length_in_s",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
TEXT_TO_AUDIO_BATCH_PARAMS = frozenset(["prompt", "negative_prompt"])
TOKENS_TO_AUDIO_GENERATION_PARAMS = frozenset(["input_tokens"])
TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS = frozenset(["input_tokens"])
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS = frozenset(["prompt_embeds"])
VIDEO_TO_VIDEO_BATCH_PARAMS = frozenset(["prompt", "negative_prompt", "video"])
| diffusers/tests/pipelines/pipeline_params.py/0 | {
"file_path": "diffusers/tests/pipelines/pipeline_params.py",
"repo_id": "diffusers",
"token_count": 1584
} | 137 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import traceback
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
AutoencoderTiny,
DDIMScheduler,
DPMSolverMultistepScheduler,
HeunDiscreteScheduler,
LCMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
run_test_in_subprocess,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_img2img_compile(in_queue, out_queue, timeout):
error = None
try:
inputs = in_queue.get(timeout=timeout)
torch_device = inputs.pop("torch_device")
seed = inputs.pop("seed")
inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.unet.to(memory_format=torch.channels_last)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.0606, 0.0570, 0.0805, 0.0579, 0.0628, 0.0623, 0.0843, 0.1115, 0.0806])
assert np.abs(expected_slice - image_slice).max() < 1e-3
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class StableDiffusionImg2ImgPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
time_cond_proj_dim=time_cond_proj_dim,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
return components
def get_dummy_tiny_autoencoder(self):
return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_stable_diffusion_img2img_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4555, 0.3216, 0.4049, 0.4620, 0.4618, 0.4126, 0.4122, 0.4629, 0.4579])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_default_case_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.5709, 0.4614, 0.4587, 0.5978, 0.5298, 0.6910, 0.6240, 0.5212, 0.5454])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_default_case_lcm_custom_timesteps(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
del inputs["num_inference_steps"]
inputs["timesteps"] = [999, 499]
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.5709, 0.4614, 0.4587, 0.5978, 0.5298, 0.6910, 0.6240, 0.5212, 0.5454])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4593, 0.3408, 0.4232, 0.4749, 0.4476, 0.4115, 0.4357, 0.4733, 0.4663])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_multiple_init_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["prompt"] = [inputs["prompt"]] * 2
inputs["image"] = inputs["image"].repeat(2, 1, 1, 1)
image = sd_pipe(**inputs).images
image_slice = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
expected_slice = np.array([0.4241, 0.5576, 0.5711, 0.4792, 0.4311, 0.5952, 0.5827, 0.5138, 0.5109])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_k_lms(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = LMSDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4398, 0.4949, 0.4337, 0.6580, 0.5555, 0.4338, 0.5769, 0.5955, 0.5175])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
def test_stable_diffusion_img2img_tiny_autoencoder(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe.vae = self.get_dummy_tiny_autoencoder()
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.00669, 0.00669, 0.0, 0.00693, 0.00858, 0.0, 0.00567, 0.00515, 0.00125])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@skip_mps
def test_save_load_local(self):
return super().test_save_load_local()
@skip_mps
def test_dict_tuple_outputs_equivalent(self):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def test_save_load_optional_components(self):
return super().test_save_load_optional_components()
@skip_mps
def test_attention_slicing_forward_pass(self):
return super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=5e-1)
def test_pipeline_interrupt(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
prompt = "hey"
num_inference_steps = 3
# store intermediate latents from the generation process
class PipelineState:
def __init__(self):
self.state = []
def apply(self, pipe, i, t, callback_kwargs):
self.state.append(callback_kwargs["latents"])
return callback_kwargs
pipe_state = PipelineState()
sd_pipe(
prompt,
image=inputs["image"],
num_inference_steps=num_inference_steps,
output_type="np",
generator=torch.Generator("cpu").manual_seed(0),
callback_on_step_end=pipe_state.apply,
).images
# interrupt generation at step index
interrupt_step_idx = 1
def callback_on_step_end(pipe, i, t, callback_kwargs):
if i == interrupt_step_idx:
pipe._interrupt = True
return callback_kwargs
output_interrupted = sd_pipe(
prompt,
image=inputs["image"],
num_inference_steps=num_inference_steps,
output_type="latent",
generator=torch.Generator("cpu").manual_seed(0),
callback_on_step_end=callback_on_step_end,
).images
# fetch intermediate latents at the interrupted step
# from the completed generation process
intermediate_latent = pipe_state.state[interrupt_step_idx]
# compare the intermediate latent to the output of the interrupted process
# they should be the same
assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)
@slow
@require_torch_gpu
class StableDiffusionImg2ImgPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
inputs = {
"prompt": "a fantasy landscape, concept art, high resolution",
"image": init_image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_stable_diffusion_img2img_default(self):
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.4300, 0.4662, 0.4930, 0.3990, 0.4307, 0.4525, 0.3719, 0.4064, 0.3923])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_img2img_k_lms(self):
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.0389, 0.0346, 0.0415, 0.0290, 0.0218, 0.0210, 0.0408, 0.0567, 0.0271])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_img2img_ddim(self):
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 768, 3)
expected_slice = np.array([0.0593, 0.0607, 0.0851, 0.0582, 0.0636, 0.0721, 0.0751, 0.0981, 0.0781])
assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_img2img_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([-0.4958, 0.5107, 1.1045, 2.7539, 4.6680, 3.8320, 1.5049, 1.8633, 2.6523])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 96)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array([-0.4956, 0.5078, 1.0918, 2.7520, 4.6484, 3.8125, 1.5146, 1.8633, 2.6367])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 2
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def test_stable_diffusion_pipeline_with_model_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
# Normal inference
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
safety_checker=None,
torch_dtype=torch.float16,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# With model offloading
# Reload but don't move to cuda
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
safety_checker=None,
torch_dtype=torch.float16,
)
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
_ = pipe(**inputs)
mem_bytes_offloaded = torch.cuda.max_memory_allocated()
assert mem_bytes_offloaded < mem_bytes
for module in pipe.text_encoder, pipe.unet, pipe.vae:
assert module.device == torch.device("cpu")
def test_img2img_2nd_order(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = HeunDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 10
inputs["strength"] = 0.75
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/img2img_heun.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 5e-2
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 11
inputs["strength"] = 0.75
image_other = sd_pipe(**inputs).images[0]
mean_diff = np.abs(image - image_other).mean()
# images should be very similar
assert mean_diff < 5e-2
def test_stable_diffusion_img2img_pipeline_multiple_of_8(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
# resize to resolution that is divisible by 8 but not 16 or 32
init_image = init_image.resize((760, 504))
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_id,
safety_checker=None,
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
prompt = "A fantasy landscape, trending on artstation"
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
image=init_image,
strength=0.75,
guidance_scale=7.5,
generator=generator,
output_type="np",
)
image = output.images[0]
image_slice = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
expected_slice = np.array([0.9393, 0.9500, 0.9399, 0.9438, 0.9458, 0.9400, 0.9455, 0.9414, 0.9423])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
def test_img2img_safety_checker_works(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 20
# make sure the safety checker is activated
inputs["prompt"] = "naked, sex, porn"
out = sd_pipe(**inputs)
assert out.nsfw_content_detected[0], f"Safety checker should work for prompt: {inputs['prompt']}"
assert np.abs(out.images[0]).sum() < 1e-5 # should be all zeros
@require_python39_or_higher
@require_torch_2
def test_img2img_compile(self):
seed = 0
inputs = self.get_inputs(torch_device, seed=seed)
# Can't pickle a Generator object
del inputs["generator"]
inputs["torch_device"] = torch_device
inputs["seed"] = seed
run_test_in_subprocess(test_case=self, target_func=_test_img2img_compile, inputs=inputs)
@nightly
@require_torch_gpu
class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
inputs = {
"prompt": "a fantasy landscape, concept art, high resolution",
"image": init_image,
"generator": generator,
"num_inference_steps": 50,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_img2img_pndm(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_ddim(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_lms(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_dpm(self):
sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 30
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/stable_diffusion_1_5_dpm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
| diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py",
"repo_id": "diffusers",
"token_count": 13114
} | 138 |
import contextlib
import gc
import inspect
import io
import json
import os
import re
import tempfile
import unittest
import uuid
from typing import Callable, Union
import numpy as np
import PIL.Image
import torch
from huggingface_hub import ModelCard, delete_repo
from huggingface_hub.utils import is_jinja_available
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AsymmetricAutoencoderKL,
AutoencoderKL,
AutoencoderTiny,
ConsistencyDecoderVAE,
DDIMScheduler,
DiffusionPipeline,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import logging
from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available
from diffusers.utils.testing_utils import (
CaptureLogger,
require_torch,
torch_device,
)
from ..models.autoencoders.test_models_vae import (
get_asym_autoencoder_kl_config,
get_autoencoder_kl_config,
get_autoencoder_tiny_config,
get_consistency_vae_config,
)
from ..others.test_utils import TOKEN, USER, is_staging_test
def to_np(tensor):
if isinstance(tensor, torch.Tensor):
tensor = tensor.detach().cpu().numpy()
return tensor
def check_same_shape(tensor_list):
shapes = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:])
class PipelineLatentTesterMixin:
"""
This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes.
It provides a set of common tests for PyTorch pipeline that has vae, e.g.
equivalence of different input and output types, etc.
"""
@property
def image_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `image_params` in the child test class. "
"`image_params` are tested for if all accepted input image types (i.e. `pt`,`pil`,`np`) are producing same results"
)
@property
def image_latents_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `image_latents_params` in the child test class. "
"`image_latents_params` are tested for if passing latents directly are producing same results"
)
def get_dummy_inputs_by_type(self, device, seed=0, input_image_type="pt", output_type="np"):
inputs = self.get_dummy_inputs(device, seed)
def convert_to_pt(image):
if isinstance(image, torch.Tensor):
input_image = image
elif isinstance(image, np.ndarray):
input_image = VaeImageProcessor.numpy_to_pt(image)
elif isinstance(image, PIL.Image.Image):
input_image = VaeImageProcessor.pil_to_numpy(image)
input_image = VaeImageProcessor.numpy_to_pt(input_image)
else:
raise ValueError(f"unsupported input_image_type {type(image)}")
return input_image
def convert_pt_to_type(image, input_image_type):
if input_image_type == "pt":
input_image = image
elif input_image_type == "np":
input_image = VaeImageProcessor.pt_to_numpy(image)
elif input_image_type == "pil":
input_image = VaeImageProcessor.pt_to_numpy(image)
input_image = VaeImageProcessor.numpy_to_pil(input_image)
else:
raise ValueError(f"unsupported input_image_type {input_image_type}.")
return input_image
for image_param in self.image_params:
if image_param in inputs.keys():
inputs[image_param] = convert_pt_to_type(
convert_to_pt(inputs[image_param]).to(device), input_image_type
)
inputs["output_type"] = output_type
return inputs
def test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4):
self._test_pt_np_pil_outputs_equivalent(expected_max_diff=expected_max_diff)
def _test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4, input_image_type="pt"):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
output_pt = pipe(
**self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pt")
)[0]
output_np = pipe(
**self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="np")
)[0]
output_pil = pipe(
**self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pil")
)[0]
max_diff = np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max()
self.assertLess(
max_diff, expected_max_diff, "`output_type=='pt'` generate different results from `output_type=='np'`"
)
max_diff = np.abs(np.array(output_pil[0]) - (output_np * 255).round()).max()
self.assertLess(max_diff, 2.0, "`output_type=='pil'` generate different results from `output_type=='np'`")
def test_pt_np_pil_inputs_equivalent(self):
if len(self.image_params) == 0:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
out_input_pt = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
out_input_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0]
out_input_pil = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pil"))[0]
max_diff = np.abs(out_input_pt - out_input_np).max()
self.assertLess(max_diff, 1e-4, "`input_type=='pt'` generate different result from `input_type=='np'`")
max_diff = np.abs(out_input_pil - out_input_np).max()
self.assertLess(max_diff, 1e-2, "`input_type=='pt'` generate different result from `input_type=='np'`")
def test_latents_input(self):
if len(self.image_latents_params) == 0:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
vae = components["vae"]
inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt")
generator = inputs["generator"]
for image_param in self.image_latents_params:
if image_param in inputs.keys():
inputs[image_param] = (
vae.encode(inputs[image_param]).latent_dist.sample(generator) * vae.config.scaling_factor
)
out_latents_inputs = pipe(**inputs)[0]
max_diff = np.abs(out - out_latents_inputs).max()
self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image")
def test_multi_vae(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
block_out_channels = pipe.vae.config.block_out_channels
norm_num_groups = pipe.vae.config.norm_num_groups
vae_classes = [AutoencoderKL, AsymmetricAutoencoderKL, ConsistencyDecoderVAE, AutoencoderTiny]
configs = [
get_autoencoder_kl_config(block_out_channels, norm_num_groups),
get_asym_autoencoder_kl_config(block_out_channels, norm_num_groups),
get_consistency_vae_config(block_out_channels, norm_num_groups),
get_autoencoder_tiny_config(block_out_channels),
]
out_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0]
for vae_cls, config in zip(vae_classes, configs):
vae = vae_cls(**config)
vae = vae.to(torch_device)
components["vae"] = vae
vae_pipe = self.pipeline_class(**components)
out_vae_np = vae_pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0]
assert out_vae_np.shape == out_np.shape
@require_torch
class PipelineKarrasSchedulerTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each PyTorch pipeline that makes use of KarrasDiffusionSchedulers
equivalence of dict and tuple outputs, etc.
"""
def test_karras_schedulers_shape(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=True)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["num_inference_steps"] = 2
if "strength" in inputs:
inputs["num_inference_steps"] = 4
inputs["strength"] = 0.5
outputs = []
for scheduler_enum in KarrasDiffusionSchedulers:
if "KDPM2" in scheduler_enum.name:
inputs["num_inference_steps"] = 5
scheduler_cls = getattr(diffusers, scheduler_enum.name)
pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config)
output = pipe(**inputs)[0]
outputs.append(output)
if "KDPM2" in scheduler_enum.name:
inputs["num_inference_steps"] = 2
assert check_same_shape(outputs)
@require_torch
class PipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline,
equivalence of dict and tuple outputs, etc.
"""
# Canonical parameters that are passed to `__call__` regardless
# of the type of pipeline. They are always optional and have common
# sense default values.
required_optional_params = frozenset(
[
"num_inference_steps",
"num_images_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
]
)
# set these parameters to False in the child class if the pipeline does not support the corresponding functionality
test_attention_slicing = True
test_xformers_attention = True
def get_generator(self, seed):
device = torch_device if torch_device != "mps" else "cpu"
generator = torch.Generator(device).manual_seed(seed)
return generator
@property
def pipeline_class(self) -> Union[Callable, DiffusionPipeline]:
raise NotImplementedError(
"You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. "
"See existing pipeline tests for reference."
)
def get_dummy_components(self):
raise NotImplementedError(
"You need to implement `get_dummy_components(self)` in the child test class. "
"See existing pipeline tests for reference."
)
def get_dummy_inputs(self, device, seed=0):
raise NotImplementedError(
"You need to implement `get_dummy_inputs(self, device, seed)` in the child test class. "
"See existing pipeline tests for reference."
)
@property
def params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `params` in the child test class. "
"`params` are checked for if all values are present in `__call__`'s signature."
" You can set `params` using one of the common set of parameters defined in `pipeline_params.py`"
" e.g., `TEXT_TO_IMAGE_PARAMS` defines the common parameters used in text to "
"image pipelines, including prompts and prompt embedding overrides."
"If your pipeline's set of arguments has minor changes from one of the common sets of arguments, "
"do not make modifications to the existing common sets of arguments. I.e. a text to image pipeline "
"with non-configurable height and width arguments should set the attribute as "
"`params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`. "
"See existing pipeline tests for reference."
)
@property
def batch_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `batch_params` in the child test class. "
"`batch_params` are the parameters required to be batched when passed to the pipeline's "
"`__call__` method. `pipeline_params.py` provides some common sets of parameters such as "
"`TEXT_TO_IMAGE_BATCH_PARAMS`, `IMAGE_VARIATION_BATCH_PARAMS`, etc... If your pipeline's "
"set of batch arguments has minor changes from one of the common sets of batch arguments, "
"do not make modifications to the existing common sets of batch arguments. I.e. a text to "
"image pipeline `negative_prompt` is not batched should set the attribute as "
"`batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {'negative_prompt'}`. "
"See existing pipeline tests for reference."
)
@property
def callback_cfg_params(self) -> frozenset:
raise NotImplementedError(
"You need to set the attribute `callback_cfg_params` in the child test class that requires to run test_callback_cfg. "
"`callback_cfg_params` are the parameters that needs to be passed to the pipeline's callback "
"function when dynamically adjusting `guidance_scale`. They are variables that require special"
"treatment when `do_classifier_free_guidance` is `True`. `pipeline_params.py` provides some common"
" sets of parameters such as `TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS`. If your pipeline's "
"set of cfg arguments has minor changes from one of the common sets of cfg arguments, "
"do not make modifications to the existing common sets of cfg arguments. I.e. for inpaint pipeine, you "
" need to adjust batch size of `mask` and `masked_image_latents` so should set the attribute as"
"`callback_cfg_params = TEXT_TO_IMAGE_CFG_PARAMS.union({'mask', 'masked_image_latents'})`"
)
def tearDown(self):
# clean up the VRAM after each test in case of CUDA runtime errors
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_save_load_local(self, expected_max_difference=5e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
logger = logging.get_logger("diffusers.pipelines.pipeline_utils")
logger.setLevel(diffusers.logging.INFO)
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir, safe_serialization=False)
with CaptureLogger(logger) as cap_logger:
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
for name in pipe_loaded.components.keys():
if name not in pipe_loaded._optional_components:
assert name in str(cap_logger)
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, expected_max_difference)
def test_pipeline_call_signature(self):
self.assertTrue(
hasattr(self.pipeline_class, "__call__"), f"{self.pipeline_class} should have a `__call__` method"
)
parameters = inspect.signature(self.pipeline_class.__call__).parameters
optional_parameters = set()
for k, v in parameters.items():
if v.default != inspect._empty:
optional_parameters.add(k)
parameters = set(parameters.keys())
parameters.remove("self")
parameters.discard("kwargs") # kwargs can be added if arguments of pipeline call function are deprecated
remaining_required_parameters = set()
for param in self.params:
if param not in parameters:
remaining_required_parameters.add(param)
self.assertTrue(
len(remaining_required_parameters) == 0,
f"Required parameters not present: {remaining_required_parameters}",
)
remaining_required_optional_parameters = set()
for param in self.required_optional_params:
if param not in optional_parameters:
remaining_required_optional_parameters.add(param)
self.assertTrue(
len(remaining_required_optional_parameters) == 0,
f"Required optional parameters not present: {remaining_required_optional_parameters}",
)
def test_inference_batch_consistent(self, batch_sizes=[2]):
self._test_inference_batch_consistent(batch_sizes=batch_sizes)
def _test_inference_batch_consistent(
self, batch_sizes=[2], additional_params_copy_to_batched_inputs=["num_inference_steps"], batch_generator=True
):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["generator"] = self.get_generator(0)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# prepare batched inputs
batched_inputs = []
for batch_size in batch_sizes:
batched_input = {}
batched_input.update(inputs)
for name in self.batch_params:
if name not in inputs:
continue
value = inputs[name]
if name == "prompt":
len_prompt = len(value)
# make unequal batch sizes
batched_input[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
# make last batch super long
batched_input[name][-1] = 100 * "very long"
else:
batched_input[name] = batch_size * [value]
if batch_generator and "generator" in inputs:
batched_input["generator"] = [self.get_generator(i) for i in range(batch_size)]
if "batch_size" in inputs:
batched_input["batch_size"] = batch_size
batched_inputs.append(batched_input)
logger.setLevel(level=diffusers.logging.WARNING)
for batch_size, batched_input in zip(batch_sizes, batched_inputs):
output = pipe(**batched_input)
assert len(output[0]) == batch_size
def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-4):
self._test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)
def _test_inference_batch_single_identical(
self,
batch_size=2,
expected_max_diff=1e-4,
additional_params_copy_to_batched_inputs=["num_inference_steps"],
):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for components in pipe.components.values():
if hasattr(components, "set_default_attn_processor"):
components.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
# Reset generator in case it is has been used in self.get_dummy_inputs
inputs["generator"] = self.get_generator(0)
logger = logging.get_logger(pipe.__module__)
logger.setLevel(level=diffusers.logging.FATAL)
# batchify inputs
batched_inputs = {}
batched_inputs.update(inputs)
for name in self.batch_params:
if name not in inputs:
continue
value = inputs[name]
if name == "prompt":
len_prompt = len(value)
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
batched_inputs[name][-1] = 100 * "very long"
else:
batched_inputs[name] = batch_size * [value]
if "generator" in inputs:
batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)]
if "batch_size" in inputs:
batched_inputs["batch_size"] = batch_size
for arg in additional_params_copy_to_batched_inputs:
batched_inputs[arg] = inputs[arg]
output = pipe(**inputs)
output_batch = pipe(**batched_inputs)
assert output_batch[0].shape[0] == batch_size
max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()
assert max_diff < expected_max_diff
def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
output = pipe(**self.get_dummy_inputs(generator_device))[0]
output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0]
max_diff = np.abs(to_np(output) - to_np(output_tuple)).max()
self.assertLess(max_diff, expected_max_difference)
def test_components_function(self):
init_components = self.get_dummy_components()
init_components = {k: v for k, v in init_components.items() if not isinstance(v, (str, int, float))}
pipe = self.pipeline_class(**init_components)
self.assertTrue(hasattr(pipe, "components"))
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_float16_inference(self, expected_max_diff=5e-2):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
components = self.get_dummy_components()
pipe_fp16 = self.pipeline_class(**components)
for component in pipe_fp16.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_fp16.to(torch_device, torch.float16)
pipe_fp16.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
# Reset generator in case it is used inside dummy inputs
if "generator" in inputs:
inputs["generator"] = self.get_generator(0)
output = pipe(**inputs)[0]
fp16_inputs = self.get_dummy_inputs(torch_device)
# Reset generator in case it is used inside dummy inputs
if "generator" in fp16_inputs:
fp16_inputs["generator"] = self.get_generator(0)
output_fp16 = pipe_fp16(**fp16_inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()
self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.")
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
def test_save_load_float16(self, expected_max_diff=1e-2):
components = self.get_dummy_components()
for name, module in components.items():
if hasattr(module, "half"):
components[name] = module.to(torch_device).half()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for name, component in pipe_loaded.components.items():
if hasattr(component, "dtype"):
self.assertTrue(
component.dtype == torch.float16,
f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
)
inputs = self.get_dummy_inputs(torch_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(
max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading."
)
def test_save_load_optional_components(self, expected_max_difference=1e-4):
if not hasattr(self.pipeline_class, "_optional_components"):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir, safe_serialization=False)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(generator_device)
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, expected_max_difference)
@unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices")
def test_to_device(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.set_progress_bar_config(disable=None)
pipe.to("cpu")
model_devices = [component.device.type for component in components.values() if hasattr(component, "device")]
self.assertTrue(all(device == "cpu" for device in model_devices))
output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0]
self.assertTrue(np.isnan(output_cpu).sum() == 0)
pipe.to("cuda")
model_devices = [component.device.type for component in components.values() if hasattr(component, "device")]
self.assertTrue(all(device == "cuda" for device in model_devices))
output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0]
self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)
def test_to_dtype(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.set_progress_bar_config(disable=None)
model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")]
self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))
pipe.to(torch_dtype=torch.float16)
model_dtypes = [component.dtype for component in components.values() if hasattr(component, "dtype")]
self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))
def test_attention_slicing_forward_pass(self, expected_max_diff=1e-3):
self._test_attention_slicing_forward_pass(expected_max_diff=expected_max_diff)
def _test_attention_slicing_forward_pass(
self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
):
if not self.test_attention_slicing:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
output_without_slicing = pipe(**inputs)[0]
pipe.enable_attention_slicing(slice_size=1)
inputs = self.get_dummy_inputs(generator_device)
output_with_slicing = pipe(**inputs)[0]
if test_max_difference:
max_diff = np.abs(to_np(output_with_slicing) - to_np(output_without_slicing)).max()
self.assertLess(max_diff, expected_max_diff, "Attention slicing should not affect the inference results")
if test_mean_pixel_difference:
assert_mean_pixel_difference(to_np(output_with_slicing[0]), to_np(output_without_slicing[0]))
@unittest.skipIf(
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"),
reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher",
)
def test_sequential_cpu_offload_forward_pass(self, expected_max_diff=1e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
output_without_offload = pipe(**inputs)[0]
pipe.enable_sequential_cpu_offload()
inputs = self.get_dummy_inputs(generator_device)
output_with_offload = pipe(**inputs)[0]
max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results")
@unittest.skipIf(
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.17.0"),
reason="CPU offload is only available with CUDA and `accelerate v0.17.0` or higher",
)
def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4):
generator_device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(generator_device)
output_without_offload = pipe(**inputs)[0]
pipe.enable_model_cpu_offload()
inputs = self.get_dummy_inputs(generator_device)
output_with_offload = pipe(**inputs)[0]
max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results")
offloaded_modules = [
v
for k, v in pipe.components.items()
if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload
]
(
self.assertTrue(all(v.device.type == "cpu" for v in offloaded_modules)),
f"Not offloaded: {[v for v in offloaded_modules if v.device.type != 'cpu']}",
)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass()
def _test_xformers_attention_forwardGenerator_pass(
self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-4
):
if not self.test_xformers_attention:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_without_offload = pipe(**inputs)[0]
output_without_offload = (
output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
)
pipe.enable_xformers_memory_efficient_attention()
inputs = self.get_dummy_inputs(torch_device)
output_with_offload = pipe(**inputs)[0]
output_with_offload = (
output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
)
if test_max_difference:
max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results")
if test_mean_pixel_difference:
assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0])
def test_progress_bar(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):
_ = pipe(**inputs)
stderr = stderr.getvalue()
# we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img,
# so we just match "5" in "#####| 1/5 [00:01<00:00]"
max_steps = re.search("/(.*?) ", stderr).group(1)
self.assertTrue(max_steps is not None and len(max_steps) > 0)
self.assertTrue(
f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step"
)
pipe.set_progress_bar_config(disable=True)
with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):
_ = pipe(**inputs)
self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled")
def test_num_images_per_prompt(self):
sig = inspect.signature(self.pipeline_class.__call__)
if "num_images_per_prompt" not in sig.parameters:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
batch_sizes = [1, 2]
num_images_per_prompts = [1, 2]
for batch_size in batch_sizes:
for num_images_per_prompt in num_images_per_prompts:
inputs = self.get_dummy_inputs(torch_device)
for key in inputs.keys():
if key in self.batch_params:
inputs[key] = batch_size * [inputs[key]]
images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
def test_cfg(self):
sig = inspect.signature(self.pipeline_class.__call__)
if "guidance_scale" not in sig.parameters:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["guidance_scale"] = 1.0
out_no_cfg = pipe(**inputs)[0]
inputs["guidance_scale"] = 7.5
out_cfg = pipe(**inputs)[0]
assert out_cfg.shape == out_no_cfg.shape
def test_callback_inputs(self):
sig = inspect.signature(self.pipeline_class.__call__)
has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
has_callback_step_end = "callback_on_step_end" in sig.parameters
if not (has_callback_tensor_inputs and has_callback_step_end):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_subset(pipe, i, t, callback_kwargs):
# interate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
def callback_inputs_all(pipe, i, t, callback_kwargs):
for tensor_name in pipe._callback_tensor_inputs:
assert tensor_name in callback_kwargs
# interate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
# Test passing in a subset
inputs["callback_on_step_end"] = callback_inputs_subset
inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
# Test passing in a everything
inputs["callback_on_step_end"] = callback_inputs_all
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
is_last = i == (pipe.num_timesteps - 1)
if is_last:
callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
return callback_kwargs
inputs["callback_on_step_end"] = callback_inputs_change_tensor
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
def test_callback_cfg(self):
sig = inspect.signature(self.pipeline_class.__call__)
has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
has_callback_step_end = "callback_on_step_end" in sig.parameters
if not (has_callback_tensor_inputs and has_callback_step_end):
return
if "guidance_scale" not in sig.parameters:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_increase_guidance(pipe, i, t, callback_kwargs):
pipe._guidance_scale += 1.0
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
# use cfg guidance because some pipelines modify the shape of the latents
# outside of the denoising loop
inputs["guidance_scale"] = 2.0
inputs["callback_on_step_end"] = callback_increase_guidance
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
_ = pipe(**inputs)[0]
# we increase the guidance scale by 1.0 at every step
# check that the guidance scale is increased by the number of scheduler timesteps
# accounts for models that modify the number of inference steps based on strength
assert pipe.guidance_scale == (inputs["guidance_scale"] + pipe.num_timesteps)
@is_staging_test
class PipelinePushToHubTester(unittest.TestCase):
identifier = uuid.uuid4()
repo_id = f"test-pipeline-{identifier}"
org_repo_id = f"valid_org/{repo_id}-org"
def get_pipeline_components(self):
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
with tempfile.TemporaryDirectory() as tmpdir:
dummy_vocab = {"<|startoftext|>": 0, "<|endoftext|>": 1, "!": 2}
vocab_path = os.path.join(tmpdir, "vocab.json")
with open(vocab_path, "w") as f:
json.dump(dummy_vocab, f)
merges = "Ġ t\nĠt h"
merges_path = os.path.join(tmpdir, "merges.txt")
with open(merges_path, "w") as f:
f.writelines(merges)
tokenizer = CLIPTokenizer(vocab_file=vocab_path, merges_file=merges_path)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def test_push_to_hub(self):
components = self.get_pipeline_components()
pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub(self.repo_id, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}", subfolder="unet")
unet = components["unet"]
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=TOKEN, repo_id=self.repo_id)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
pipeline.save_pretrained(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}", subfolder="unet")
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(self.repo_id, token=TOKEN)
def test_push_to_hub_in_organization(self):
components = self.get_pipeline_components()
pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub(self.org_repo_id, token=TOKEN)
new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder="unet")
unet = components["unet"]
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=TOKEN, repo_id=self.org_repo_id)
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
pipeline.save_pretrained(tmp_dir, push_to_hub=True, token=TOKEN, repo_id=self.org_repo_id)
new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder="unet")
for p1, p2 in zip(unet.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(self.org_repo_id, token=TOKEN)
@unittest.skipIf(
not is_jinja_available(),
reason="Model card tests cannot be performed without Jinja installed.",
)
def test_push_to_hub_library_name(self):
components = self.get_pipeline_components()
pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub(self.repo_id, token=TOKEN)
model_card = ModelCard.load(f"{USER}/{self.repo_id}", token=TOKEN).data
assert model_card.library_name == "diffusers"
# Reset repo
delete_repo(self.repo_id, token=TOKEN)
# For SDXL and its derivative pipelines (such as ControlNet), we have the text encoders
# and the tokenizers as optional components. So, we need to override the `test_save_load_optional_components()`
# test for all such pipelines. This requires us to use a custom `encode_prompt()` function.
class SDXLOptionalComponentsTesterMixin:
def encode_prompt(
self, tokenizers, text_encoders, prompt: str, num_images_per_prompt: int = 1, negative_prompt: str = None
):
device = text_encoders[0].device
if isinstance(prompt, str):
prompt = [prompt]
batch_size = len(prompt)
prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
if negative_prompt is None:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
else:
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
negative_prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
uncond_input = tokenizer(
negative_prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds = text_encoder(uncond_input.input_ids.to(device), output_hidden_states=True)
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
negative_prompt_embeds_list.append(negative_prompt_embeds)
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# for classifier-free guidance
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
# for classifier-free guidance
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def _test_save_load_optional_components(self, expected_max_difference=1e-4):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for optional_component in pipe._optional_components:
setattr(pipe, optional_component, None)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
tokenizer = components.pop("tokenizer")
tokenizer_2 = components.pop("tokenizer_2")
text_encoder = components.pop("text_encoder")
text_encoder_2 = components.pop("text_encoder_2")
tokenizers = [tokenizer, tokenizer_2] if tokenizer is not None else [tokenizer_2]
text_encoders = [text_encoder, text_encoder_2] if text_encoder is not None else [text_encoder_2]
prompt = inputs.pop("prompt")
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(tokenizers, text_encoders, prompt)
inputs["prompt_embeds"] = prompt_embeds
inputs["negative_prompt_embeds"] = negative_prompt_embeds
inputs["pooled_prompt_embeds"] = pooled_prompt_embeds
inputs["negative_pooled_prompt_embeds"] = negative_pooled_prompt_embeds
output = pipe(**inputs)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(tmpdir)
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
for component in pipe_loaded.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe_loaded.to(torch_device)
pipe_loaded.set_progress_bar_config(disable=None)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(pipe_loaded, optional_component) is None,
f"`{optional_component}` did not stay set to None after loading.",
)
inputs = self.get_dummy_inputs(generator_device)
_ = inputs.pop("prompt")
inputs["prompt_embeds"] = prompt_embeds
inputs["negative_prompt_embeds"] = negative_prompt_embeds
inputs["pooled_prompt_embeds"] = pooled_prompt_embeds
inputs["negative_pooled_prompt_embeds"] = negative_pooled_prompt_embeds
output_loaded = pipe_loaded(**inputs)[0]
max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
self.assertLess(max_diff, expected_max_difference)
# Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used.
# This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a
# reference image.
def assert_mean_pixel_difference(image, expected_image, expected_max_diff=10):
image = np.asarray(DiffusionPipeline.numpy_to_pil(image)[0], dtype=np.float32)
expected_image = np.asarray(DiffusionPipeline.numpy_to_pil(expected_image)[0], dtype=np.float32)
avg_diff = np.abs(image - expected_image).mean()
assert avg_diff < expected_max_diff, f"Error image deviates {avg_diff} pixels on average"
| diffusers/tests/pipelines/test_pipelines_common.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipelines_common.py",
"repo_id": "diffusers",
"token_count": 24363
} | 139 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import DDPMWuerstchenScheduler, WuerstchenPriorPipeline
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import (
LoRAAttnProcessor,
LoRAAttnProcessor2_0,
)
from diffusers.pipelines.wuerstchen import WuerstchenPrior
from diffusers.utils.import_utils import is_peft_available
from diffusers.utils.testing_utils import enable_full_determinism, require_peft_backend, skip_mps, torch_device
if is_peft_available():
from peft import LoraConfig
from peft.tuners.tuners_utils import BaseTunerLayer
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
def create_prior_lora_layers(unet: nn.Module):
lora_attn_procs = {}
for name in unet.attn_processors.keys():
lora_attn_processor_class = (
LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
)
lora_attn_procs[name] = lora_attn_processor_class(
hidden_size=unet.config.c,
)
unet_lora_layers = AttnProcsLayers(lora_attn_procs)
return lora_attn_procs, unet_lora_layers
class WuerstchenPriorPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = WuerstchenPriorPipeline
params = ["prompt"]
batch_params = ["prompt", "negative_prompt"]
required_optional_params = [
"num_images_per_prompt",
"generator",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
test_xformers_attention = False
callback_cfg_params = ["text_encoder_hidden_states"]
@property
def text_embedder_hidden_size(self):
return 32
@property
def time_input_dim(self):
return 32
@property
def block_out_channels_0(self):
return self.time_input_dim
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=self.text_embedder_hidden_size,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModel(config).eval()
@property
def dummy_prior(self):
torch.manual_seed(0)
model_kwargs = {
"c_in": 2,
"c": 8,
"depth": 2,
"c_cond": 32,
"c_r": 8,
"nhead": 2,
}
model = WuerstchenPrior(**model_kwargs)
return model.eval()
def get_dummy_components(self):
prior = self.dummy_prior
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
scheduler = DDPMWuerstchenScheduler()
components = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "horse",
"generator": generator,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def test_wuerstchen_prior(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.image_embeddings
image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0]
image_slice = image[0, 0, 0, -10:]
image_from_tuple_slice = image_from_tuple[0, 0, 0, -10:]
assert image.shape == (1, 2, 24, 24)
expected_slice = np.array(
[
-7172.837,
-3438.855,
-1093.312,
388.8835,
-7471.467,
-7998.1206,
-5328.259,
218.00089,
-2731.5745,
-8056.734,
]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-2
@skip_mps
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=2e-1,
)
@skip_mps
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
test_mean_pixel_difference = False
self._test_attention_slicing_forward_pass(
test_max_difference=test_max_difference,
test_mean_pixel_difference=test_mean_pixel_difference,
)
@unittest.skip(reason="flaky for now")
def test_float16_inference(self):
super().test_float16_inference()
# override because we need to make sure latent_mean and latent_std to be 0
def test_callback_inputs(self):
components = self.get_dummy_components()
components["latent_mean"] = 0
components["latent_std"] = 0
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_test(pipe, i, t, callback_kwargs):
missing_callback_inputs = set()
for v in pipe._callback_tensor_inputs:
if v not in callback_kwargs:
missing_callback_inputs.add(v)
self.assertTrue(
len(missing_callback_inputs) == 0, f"Missing callback tensor inputs: {missing_callback_inputs}"
)
last_i = pipe.num_timesteps - 1
if i == last_i:
callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
inputs["callback_on_step_end"] = callback_inputs_test
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
def check_if_lora_correctly_set(self, model) -> bool:
"""
Checks if the LoRA layers are correctly set with peft
"""
for module in model.modules():
if isinstance(module, BaseTunerLayer):
return True
return False
def get_lora_components(self):
prior = self.dummy_prior
prior_lora_config = LoraConfig(
r=4, lora_alpha=4, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False
)
prior_lora_attn_procs, prior_lora_layers = create_prior_lora_layers(prior)
lora_components = {
"prior_lora_layers": prior_lora_layers,
"prior_lora_attn_procs": prior_lora_attn_procs,
}
return prior, prior_lora_config, lora_components
@require_peft_backend
def test_inference_with_prior_lora(self):
_, prior_lora_config, _ = self.get_lora_components()
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output_no_lora = pipe(**self.get_dummy_inputs(device))
image_embed = output_no_lora.image_embeddings
self.assertTrue(image_embed.shape == (1, 2, 24, 24))
pipe.prior.add_adapter(prior_lora_config)
self.assertTrue(self.check_if_lora_correctly_set(pipe.prior), "Lora not correctly set in prior")
output_lora = pipe(**self.get_dummy_inputs(device))
lora_image_embed = output_lora.image_embeddings
self.assertTrue(image_embed.shape == lora_image_embed.shape)
| diffusers/tests/pipelines/wuerstchen/test_wuerstchen_prior.py/0 | {
"file_path": "diffusers/tests/pipelines/wuerstchen/test_wuerstchen_prior.py",
"repo_id": "diffusers",
"token_count": 4397
} | 140 |
import torch
from diffusers import HeunDiscreteScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class HeunDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (HeunDiscreteScheduler,)
num_inference_steps = 10
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1100,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**kwargs)
return config
def test_timesteps(self):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=timesteps)
def test_betas(self):
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
def test_schedules(self):
for schedule in ["linear", "scaled_linear", "exp"]:
self.check_over_configs(beta_schedule=schedule)
def test_clip_sample(self):
for clip_sample_range in [1.0, 2.0, 3.0]:
self.check_over_configs(clip_sample_range=clip_sample_range, clip_sample=True)
def test_prediction_type(self):
for prediction_type in ["epsilon", "v_prediction", "sample"]:
self.check_over_configs(prediction_type=prediction_type)
def test_full_loop_no_noise(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
def test_full_loop_with_v_prediction(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6934e-07) < 1e-2
assert abs(result_mean.item() - 6.1112e-10) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
def test_full_loop_device(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
model = self.dummy_model()
sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if str(torch_device).startswith("cpu"):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
elif str(torch_device).startswith("mps"):
# Larger tolerance on mps
assert abs(result_mean.item() - 0.0002) < 1e-2
else:
# CUDA
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
def test_full_loop_device_karras_sigmas(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
model = self.dummy_model()
sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for t in scheduler.timesteps:
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 0.00015) < 1e-2
assert abs(result_mean.item() - 1.9869554535034695e-07) < 1e-2
def test_full_loop_with_noise(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
t_start = self.num_inference_steps - 2
noise = self.dummy_noise_deter
noise = noise.to(torch_device)
timesteps = scheduler.timesteps[t_start * scheduler.order :]
sample = scheduler.add_noise(sample, noise, timesteps[:1])
for i, t in enumerate(timesteps):
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 75074.8906) < 1e-2, f" expected result sum 75074.8906, but get {result_sum}"
assert abs(result_mean.item() - 97.7538) < 1e-3, f" expected result mean 97.7538, but get {result_mean}"
| diffusers/tests/schedulers/test_scheduler_heun.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_heun.py",
"repo_id": "diffusers",
"token_count": 3279
} | 141 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
PATH_TO_DIFFUSERS = "src/diffusers"
# Matches is_xxx_available()
_re_backend = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
_re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
DUMMY_CONSTANT = """
{0} = None
"""
DUMMY_CLASS = """
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
"""
DUMMY_FUNCTION = """
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
def find_backend(line):
"""Find one (or multiple) backend in a code line of the init."""
backends = _re_backend.findall(line)
if len(backends) == 0:
return None
return "_and_".join(backends)
def read_init():
"""Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects."""
with open(os.path.join(PATH_TO_DIFFUSERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Get to the point we do the actual imports for type checking
line_index = 0
while not lines[line_index].startswith("if TYPE_CHECKING"):
line_index += 1
backend_specific_objects = {}
# Go through the end of the file
while line_index < len(lines):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
backend = find_backend(lines[line_index])
if backend is not None:
while not lines[line_index].startswith(" else:"):
line_index += 1
line_index += 1
objects = []
# Until we unindent, add backend objects to the list
while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8):
line = lines[line_index]
single_line_import_search = _re_single_line_import.search(line)
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", "))
elif line.startswith(" " * 12):
objects.append(line[12:-2])
line_index += 1
if len(objects) > 0:
backend_specific_objects[backend] = objects
else:
line_index += 1
return backend_specific_objects
def create_dummy_object(name, backend_name):
"""Create the code for the dummy object corresponding to `name`."""
if name.isupper():
return DUMMY_CONSTANT.format(name)
elif name.islower():
return DUMMY_FUNCTION.format(name, backend_name)
else:
return DUMMY_CLASS.format(name, backend_name)
def create_dummy_files(backend_specific_objects=None):
"""Create the content of the dummy files."""
if backend_specific_objects is None:
backend_specific_objects = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
dummy_files = {}
for backend, objects in backend_specific_objects.items():
backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]"
dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects])
dummy_files[backend] = dummy_file
return dummy_files
def check_dummies(overwrite=False):
"""Check if the dummy files are up to date and maybe `overwrite` with the right content."""
dummy_files = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
short_names = {"torch": "pt"}
# Locate actual dummy modules and read their content.
path = os.path.join(PATH_TO_DIFFUSERS, "utils")
dummy_file_paths = {
backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py")
for backend in dummy_files.keys()
}
actual_dummies = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(file_path):
with open(file_path, "r", encoding="utf-8", newline="\n") as f:
actual_dummies[backend] = f.read()
else:
actual_dummies[backend] = ""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f"Updating diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main "
"__init__ has new objects."
)
with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f:
f.write(dummy_files[backend])
else:
raise ValueError(
"The main __init__ has objects that are not present in "
f"diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` "
"to fix this."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
args = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| diffusers/utils/check_dummies.py/0 | {
"file_path": "diffusers/utils/check_dummies.py",
"repo_id": "diffusers",
"token_count": 2592
} | 142 |
# Keras Dreambooth event! 🤗
This document summarises all the relevant information required for the event 📋.
## Introduction
Dreambooth is a fine-tuning technique to teach new visual concepts to text-conditioned Diffusion models with just 3-5 images. With Dreambooth, you could generate funny and realistic images of your dog, yourself and any concept with few images using Stable Diffusion.
DreamBooth was proposed in [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://arxiv.org/abs/2208.12242) by Ruiz et al.
In this guide, we will walk you through what we will do in this event.
We will be training Dreambooth models using KerasCV and building demos on them.
## Important Dates
- Kick-Off Event: March 6th, 2023
- Sprint start: March 7th, 2023
- Sprint end: April 1st, 2023
- Results: April 7th, 2023
## Getting Started 🚀
To get started, join us in [hf.co/join/discord](http://hf.co/join/discord) and take the role #open-source, and meet us in #keras-working-group channel.
We will be hosting our demos in this organization on Hugging Face Hub: [keras-dreambooth](https://huggingface.co/keras-dreambooth), send a request to join [here](https://huggingface.co/organizations/keras-dreambooth/share/RMocthadPgpxxUDHtAesrbBzieDLgUfPmv) if you’d like to make a submission 🙂
We will:
1. Fine-tune Stable Diffusion on any concept we want using Dreambooth,
2. Push the model to Hugging Face Hub,
3. Fill the model card,
4. Build a demo on top of the model.
**Warning:** The trained models need to be in one of the 4 categories mentioned in the Submission section. Please take a look at that before training your model.
**Let’s get started** 🚀
## **Model Training**
You can find the notebook here and adjust it according to your own dataset 👇
[Link to notebook](https://colab.research.google.com/github/huggingface/community-events/blob/main/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb)
You can fine-tune on any concept that you want. Couple of inspirations for you:
1. Lowpoly World: This [model](https://huggingface.co/MirageML/lowpoly-world) generates lowpoly worlds 🤯🌍
2. Future Diffusion: This [model](https://huggingface.co/nitrosocke/Future-Diffusion) generates images in futuristic sci-fi concepts 🤖
3. Fantasy sword: This [model](https://huggingface.co/MirageML/fantasy-sword) generates swords for fantasy themed games 🧙♂️
If you need more pointers on Dreambooth implementation with Keras, you can check out [this repository](https://github.com/sayakpaul/dreambooth-keras).
**Important**: To learn how to launch a cloud GPU instance and train with Lambda, please refer to [Compute with Lambda](./compute-with-lambda.md).
## Dreambooth Diffusers Integration with KerasCV
As of now, inference and deployment options of `KerasCV` are limited, which is when the `diffusers` library comes to the rescue. With only few lines of code, we can convert a `KerasCV` model into a `diffusers` one and use `diffusers`’ pipelines to perform inference. You can get more information [here](https://huggingface.co/docs/diffusers/main/en/using-diffusers/kerascv). Also check out [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) for converting your `KerasCV` model to `diffusers`one.
`diffusers`repositories on the Hub get a free Inference API and small widgets in the model page where users can play with the model.
```python
from diffusers import StableDiffusionPipeline
# checkpoint of the converted Stable Diffusion from KerasCV
model_ckpt = "sayakpaul/text-unet-dogs-kerascv_sd_diffusers_pipeline"
pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt)
pipeline.to("cuda")
unique_id = "sks"
class_label = "dog"
prompt = f"A photo of {unique_id} {class_label} in a bucket"
image = pipeline(prompt, num_inference_steps=50).images[0]
```
## Model Hosting
At the end of [this notebook](https://colab.research.google.com/github/huggingface/community-events/blob/main/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb) you will see a section dedicated for hosting, and a separate one for inference. We will be using the `huggingface_hub` library’s Keras-specific model pushing and loading functions: `push_to_hub_keras` and `from_pretrained_keras` . We will first push the model using `push_to_hub_keras`. After model is pushed, you will see the model is hosted with a model card like below 👇
To version the models better, enable discoverability and reproducibility, we will fill the model card. Click `Edit model card`. We will first fill the Metadata section of the model card. If your model is trained with a dataset from the Hugging Face Hub, you can fill the datasets section with the dataset. We will provide fill `pipeline_tag` with `text-to-image` and pick a license for our model.
Then, we will fill the markdown part. Hyperparameters and plot is automatically generated so we can write a short explanation for description, intended use and dataset.
You can find the example repository below 👇
[keras-dreambooth/dreambooth_diffusion_model](https://huggingface.co/keras-dreambooth/dreambooth_diffusion_model)
## Model Demo
We will use Gradio to build our demos for the models we have trained. With `Interface` class it’s straightforward 👇
```python
from huggingface_hub import from_pretrained_keras
from keras_cv import models
import gradio as gr
sd_dreambooth_model = models.StableDiffusion(
img_width=512, img_height=512
)
db_diffusion_model = from_pretrained_keras("merve/dreambooth_diffusion_model")
sd_dreambooth_model._diffusion_model = db_diffusion_model
# generate images
def infer(prompt):
generated_images = sd_dreambooth_model.text_to_image(
prompt
)
return generated_images
output = gr.Gallery(label="Outputs").style(grid=(2,2))
# pass function, input type for prompt, the output for multiple images
gr.Interface(infer, inputs=["text"], outputs=[output]).launch()
```
You can check out `app.py`file of the application below and repurpose it for your model!
[Dreambooth Submission - a Hugging Face Space by keras-dreambooth](https://huggingface.co/spaces/keras-dreambooth/example-submission)
This app generates images of a corgi 🐶
## Hosting the Demo on Spaces
After our application is written, we can create a Hugging Face Space to host our app. You can go to [huggingface.co](http://huggingface.co), click on your profile on top right and select “New Space”.
We can name our Space, pick a license and select Space SDK as “Gradio”.
After creating the Space, you can use either the instructions below to clone the repository locally, adding your files and push, OR, graphical interface to create the files and write the code in the browser.
To upload your application file, pick “Add File” and drag and drop your file.
Lastly, we have to create a file called `requirements.txt` and add requirements of Dreambooth project like below:
```
keras-cv
tensorflow
huggingface-hub
```
And your app should be up and running!
We will host our models and Spaces under [this organization](https://huggingface.co/keras-dreambooth). You can carry your models and Spaces on the settings tab under `Rename or transfer this model` and select `keras-dreambooth` from the dropdown.
If you don't see `keras-dreambooth` in the dropdown, it's likely that you aren't a member of the organization. Use [this link](https://huggingface.co/organizations/keras-dreambooth/share/bfDDnByLbvPRYypHNUoZJgBgbgtTEYYgVl) to request to join the organization.
## Submission
You can make submission in three themes:
- Nature and Animals (`nature`)
- Sci-fi/Fantasy Universes (`sci-fi`)
- Consentful (`consentful`): Partner up with an artist to fine-tune on their style, with their consent! Make sure to include a reference to the artist’s express consent (e.g. a tweet) in your model card.
- Wild Card (`wild-card`): If your submission belongs to any category that is not above, feel free to tag it with wild-card so we can evaluate it out of that category.
Add the category with their IDs to the model cards for submission and add `keras-dreambooth` to model card metadata in tags section. Here's an example [model card](https://huggingface.co/spaces/keras-dreambooth/example-submission/blob/main/README.md). All the submissions will be populated [in this leaderboard](https://huggingface.co/spaces/keras-dreambooth/leaderboard) and ranked according to likes on a given Space to determine the winners.
## Sprint **Prizes**
We will pick three winners among the applications submitted, according to the number of likes given to a Space in a given category.
🛍️ First place will win a 100$ voucher on [hf.co/shop](http://hf.co/shop) or one year subscription to [Hugging Face Pro](https://huggingface.co/pricing#pro)
🛍️ Second place will win a 50$ voucher on [hf.co/shop](http://hf.co/shop) or [the book](https://transformersbook.com/) “Natural Language Processing with Transformers”
🛍️ Third place will win a 30$ voucher on [hf.co/shop](http://hf.co/shop) or three months subscription to [Hugging Face Pro](https://huggingface.co/pricing#pro)
| diffusion-models-class/units/en/events/3.mdx/0 | {
"file_path": "diffusion-models-class/units/en/events/3.mdx",
"repo_id": "diffusion-models-class",
"token_count": 3063
} | 143 |
- title: Introduction au cours
sections:
- local: unit0/1
title: Introduction
- title: 1. Introduction aux modèles de diffusion
sections:
- local: unit1/1
title: Vue d'ensemble
- local: unit1/2
title: Introduction à 🤗 Diffusers
- local: unit1/3
title: Implémentation à partir de 0
- title: 2. <i>Finetuning</i>, guidage et conditionnement
sections:
- local: unit2/1
title: Vue d'ensemble
- local: unit2/2
title: <i>Finetuning</i> et guidage
- local: unit2/3
title: Modèle de diffusion conditionné par la classe
- title: 3. Stable Diffusion
sections:
- local: unit3/1
title: Vue d'ensemble
- local: unit3/2
title: Introduction à Stable Diffusion
- local: unit3/3
title: Stable Diffusion : plongée en profondeur
- title: 4. Aller plus loin avec les modèles de diffusion
sections:
- local: unit4/1
title: Vue d'ensemble
- local: unit4/2
title: Débruitage inverse des modèles de diffusion implicites
- local: unit4/3
title: Diffusion pour l'audio
- title: Evènements liés au cours
sections:
- local: events/1
title: Lancement du cours
- local: events/2
title: Hackathon Dreambooth
- local: events/3
title: Sprint Dreambooth en Keras
- local: events/4
title: Sprint ControlNet en JAX/Diffusers | diffusion-models-class/units/fr/_toctree.yml/0 | {
"file_path": "diffusion-models-class/units/fr/_toctree.yml",
"repo_id": "diffusion-models-class",
"token_count": 494
} | 144 |
<jupyter_start><jupyter_text>Manipulation de plusieurs séquences (TensorFlow) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
checkpoint = "tblard/tf-allocine"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
sequence = "J'ai attendu un cours d’HuggingFace toute ma vie."
tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = tf.constant(ids)
# Cette ligne va échouer
model(input_ids)
tokenized_inputs = tokenizer(sequence, return_tensors="tf")
print(tokenized_inputs["input_ids"])
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
checkpoint = "tblard/tf-allocine"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
sequence = "J'ai attendu un cours d’HuggingFace toute ma vie."
tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = tf.constant([ids])
print("Input IDs:", input_ids)
output = model(input_ids)
print("Logits:", output.logits)
batched_ids = [
[200, 200, 200],
[200, 200]
]
padding_id = 100
batched_ids = [
[200, 200, 200],
[200, 200, padding_id],
]
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
sequence1_ids = [[200, 200, 200]]
sequence2_ids = [[200, 200]]
batched_ids = [
[200, 200, 200],
[200, 200, tokenizer.pad_token_id],
]
print(model(tf.constant(sequence1_ids)).logits)
print(model(tf.constant(sequence2_ids)).logits)
print(model(tf.constant(batched_ids)).logits)
batched_ids = [
[200, 200, 200],
[200, 200, tokenizer.pad_token_id],
]
attention_mask = [
[1, 1, 1],
[1, 1, 0],
]
outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))
print(outputs.logits)
# max_sequence_length = 512
equence = sequence[:max_sequence_length]<jupyter_output><empty_output> | notebooks/course/fr/chapter2/section5_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section5_tf.ipynb",
"repo_id": "notebooks",
"token_count": 810
} | 145 |
<jupyter_start><jupyter_text>Données massives ? 🤗 Datasets à la rescousse ! Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*.<jupyter_code>!pip install datasets evaluate transformers[sentencepiece]
!pip install zstandard
from datasets import load_dataset
# Cela prend quelques minutes à exécuter, alors allez prendre un thé ou un café en attendant :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
pubmed_dataset[0]
!pip install psutil
import psutil
# Process.memory_info est exprimé en octets, donc convertir en mégaoctets
print(f"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB")
print(f"Number of files in dataset : {pubmed_dataset.dataset_size}")
size_gb = pubmed_dataset.dataset_size / (1024**3)
print(f"Dataset size (cache file) : {size_gb:.2f} GB")
import timeit
code_snippet = """batch_size = 1000
for idx in range(0, len(pubmed_dataset), batch_size):
_ = pubmed_dataset[idx:idx + batch_size]
"""
time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())
print(
f"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in "
f"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s"
)
pubmed_dataset_streamed = load_dataset(
"json", data_files=data_files, split="train", streaming=True
)
next(iter(pubmed_dataset_streamed))
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x["text"]))
next(iter(tokenized_dataset))
shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)
next(iter(shuffled_dataset))
dataset_head = pubmed_dataset_streamed.take(5)
list(dataset_head)
# Ignorer les 1 000 premiers exemples et inclure le reste dans l'ensemble d'apprentissage.
train_dataset = shuffled_dataset.skip(1000)
# Prendre les 1 000 premiers exemples pour l'ensemble de validation.
validation_dataset = shuffled_dataset.take(1000)
law_dataset_streamed = load_dataset(
"json",
data_files="https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst",
split="train",
streaming=True,
)
next(iter(law_dataset_streamed))
from itertools import islice
from datasets import interleave_datasets
combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])
list(islice(combined_dataset, 2))
base_url = "https://the-eye.eu/public/AI/pile/"
data_files = {
"train": [base_url + "train/" + f"{idx:02d}.jsonl.zst" for idx in range(30)],
"validation": base_url + "val.jsonl.zst",
"test": base_url + "test.jsonl.zst",
}
pile_dataset = load_dataset("json", data_files=data_files, streaming=True)
next(iter(pile_dataset["train"]))<jupyter_output><empty_output> | notebooks/course/fr/chapter5/section4.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter5/section4.ipynb",
"repo_id": "notebooks",
"token_count": 1168
} | 146 |
<jupyter_start><jupyter_text>Classification de token (TensorFlow) Installez les bibliothèques 🤗 *Datasets*, 🤗 *Transformers* et 🤗 *Accelerate* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!apt install git-lfs<jupyter_output><empty_output><jupyter_text>Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante.<jupyter_code>!git config --global user.email "[email protected]"
!git config --global user.name "Your Name"<jupyter_output><empty_output><jupyter_text>Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification.<jupyter_code>from huggingface_hub import notebook_login
notebook_login()
from datasets import load_dataset
raw_datasets = load_dataset("wikiann","fr")
raw_datasets
raw_datasets["train"][0]["tokens"]
raw_datasets["train"][0]["ner_tags"]
ner_feature = raw_datasets["train"].features["ner_tags"]
ner_feature
label_names = ner_feature.feature.names
label_names
words = raw_datasets["train"][0]["tokens"]
labels = raw_datasets["train"][0]["ner_tags"]
line1 = ""
line2 = ""
for word, label in zip(words, labels):
full_label = label_names[label]
max_length = max(len(word), len(full_label))
line1 += word + " " * (max_length - len(word) + 1)
line2 += full_label + " " * (max_length - len(full_label) + 1)
print(line1)
print(line2)
from transformers import AutoTokenizer
model_checkpoint = "camembert-base"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
tokenizer.is_fast
inputs = tokenizer(raw_datasets["train"][0]["tokens"], is_split_into_words=True)
inputs.tokens()
inputs.word_ids()
def align_labels_with_tokens(labels, word_ids):
new_labels = []
current_word = None
for word_id in word_ids:
if word_id != current_word:
# Début d'un nouveau mot !
current_word = word_id
label = -100 if word_id is None else labels[word_id]
new_labels.append(label)
elif word_id is None:
# Token special
new_labels.append(-100)
else:
# Même mot que le token précédent
label = labels[word_id]
# Si l'étiquette est B-XXX, nous la changeons en I-XXX
if label % 2 == 1:
label += 1
new_labels.append(label)
return new_labels
labels = raw_datasets["train"][0]["ner_tags"]
word_ids = inputs.word_ids()
print(labels)
print(align_labels_with_tokens(labels, word_ids))
def tokenize_and_align_labels(examples):
tokenized_inputs = tokenizer(
examples["tokens"], truncation=True, is_split_into_words=True
)
all_labels = examples["ner_tags"]
new_labels = []
for i, labels in enumerate(all_labels):
word_ids = tokenized_inputs.word_ids(i)
new_labels.append(align_labels_with_tokens(labels, word_ids))
tokenized_inputs["labels"] = new_labels
return tokenized_inputs
tokenized_datasets = raw_datasets.map(
tokenize_and_align_labels,
batched=True,
remove_columns=raw_datasets["train"].column_names,
)
from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(
tokenizer=tokenizer, return_tensors="tf"
)
batch = data_collator([tokenized_datasets["train"][i] for i in range(2)])
batch["labels"]
for i in range(2):
print(tokenized_datasets["train"][i]["labels"])
tf_train_dataset = tokenized_datasets["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
collate_fn=data_collator,
shuffle=True,
batch_size=16,
)
tf_eval_dataset = tokenized_datasets["validation"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
collate_fn=data_collator,
shuffle=False,
batch_size=16,
)
id2label = {str(i): label for i, label in enumerate(label_names)}
label2id = {v: k for k, v in id2label.items()}
from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained(
model_checkpoint,
id2label=id2label,
label2id=label2id,
)
model.config.num_labels
from huggingface_hub import notebook_login
notebook_login()
from transformers import create_optimizer
import tensorflow as tf
# Train in mixed-precision float16
# Commentez cette ligne si vous utilisez un GPU qui ne bénéficiera pas de cette fonction.
tf.keras.mixed_precision.set_global_policy("mixed_float16")
# Le nombre d'étapes d'entraînement est le nombre d'échantillons dans le jeu de données, divisé par la taille du batch puis multiplié
# par le nombre total d'époques. Notez que le jeu de données tf_train_dataset est ici un lot de données tf.data.Dataset,
# pas le jeu de données original Hugging Face, donc son len() est déjà num_samples // batch_size.
num_epochs = 3
num_train_steps = len(tf_train_dataset) * num_epochs
optimizer, schedule = create_optimizer(
init_lr=2e-5,
num_warmup_steps=0,
num_train_steps=num_train_steps,
weight_decay_rate=0.01,
)
model.compile(optimizer=optimizer)
from transformers.keras_callbacks import PushToHubCallback
callback = PushToHubCallback(output_dir="camembert-finetuned-ner", tokenizer=tokenizer)
model.fit(
tf_train_dataset,
validation_data=tf_eval_dataset,
callbacks=[callback],
epochs=num_epochs,
)
!pip install seqeval
from datasets import load_metric
metric = load_metric("seqeval")
labels = raw_datasets["train"][0]["ner_tags"]
labels = [label_names[i] for i in labels]
labels
predictions = labels.copy()
predictions[2] = "O"
metric.compute(predictions=[predictions], references=[labels])
import numpy as np
all_predictions = []
all_labels = []
for batch in tf_eval_dataset:
logits = model.predict_on_batch(batch)["logits"]
labels = batch["labels"]
predictions = np.argmax(logits, axis=-1)
for prediction, label in zip(predictions, labels):
for predicted_idx, label_idx in zip(prediction, label):
if label_idx == -100:
continue
all_predictions.append(label_names[predicted_idx])
all_labels.append(label_names[label_idx])
metric.compute(predictions=[all_predictions], references=[all_labels])
from transformers import pipeline
# Remplacez par votre propre checkpoint
model_checkpoint = "huggingface-course/camembert-finetuned-ner"
token_classifier = pipeline(
"token-classification", model=model_checkpoint, aggregation_strategy="simple"
)
oken_classifier("Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.")<jupyter_output><empty_output> | notebooks/course/fr/chapter7/section2_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section2_tf.ipynb",
"repo_id": "notebooks",
"token_count": 2616
} | 147 |
<jupyter_start><jupyter_text>You will need an authentication token with your Hugging Face credentials to use the `push_to_hub` method. Execute `huggingface-cli login` in your terminal or by uncommenting the following cell:<jupyter_code># !huggingface-cli login
import numpy as np
from datasets import load_dataset, load_metric
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainingArguments,
)
checkpoint = "bert-base-cased"
raw_datasets = load_dataset("glue", "mrpc")
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
def tokenize_function(examples):
return tokenizer(examples["sentence1"], examples["sentence2"], truncation=True)
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
training_args = TrainingArguments(
"finetuned-bert-mrpc",
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
learning_rate=2e-5,
weight_decay=0.01,
evaluation_strategy="epoch",
logging_strategy="epoch",
log_level="error",
push_to_hub=True,
push_to_hub_model_id="finetuned-bert-mrpc",
# push_to_hub_organization="huggingface",
# push_to_hub_token="my_token",
)
data_collator = DataCollatorWithPadding(tokenizer)
metric = load_metric("glue", "mrpc")
def compute_metrics(eval_preds):
logits, labels = eval_preds
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
trainer = Trainer(
model,
training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
trainer.train()<jupyter_output><empty_output><jupyter_text>Push to hub from the Trainer directly You will need an authentication token with your Hugging Face credentials to use the `push_to_hub` method. Execute `huggingface-cli login` in your terminal or by uncommenting the following cell:<jupyter_code># !huggingface-cli login<jupyter_output><empty_output><jupyter_text>The `Trainer` has a new method to directly upload the model, tokenizer and model configuration in a repo on the [Hub](https://huggingface.co/). It will even auto-generate a model card draft using the hyperparameters and evaluation results!<jupyter_code>trainer.push_to_hub()<jupyter_output><empty_output><jupyter_text>If you are using your own training loop, you can push the model and tokenizer separately (and you will have to write the model card yourself):<jupyter_code># model.push_to_hub("finetuned-bert-mrpc")
# tokenizer.push_to_hub("finetuned-bert-mrpc")<jupyter_output><empty_output><jupyter_text>You can load your model from anywhere using from_pretrained!<jupyter_code>from transformers import AutoModelForSequenceClassification
model_name = "sgugger/finetuned-bert-mrpc"
model = AutoModelForSequenceClassification.from_pretrained(model_name)<jupyter_output><empty_output><jupyter_text>You can use your model in a pipeline!<jupyter_code>from transformers import pipeline
classifier = pipeline("text-classification", model=model_name)
classifier("My name is Sylvain. [SEP] My name is Lysandre")<jupyter_output><empty_output><jupyter_text>Updating a problematic file is super easy!<jupyter_code>model.config.label2id = {"not equivalent": 0, "equivalent": 1}
model.config.id2label = {0: "not equivalent", 1: "equivalent"}
model.config.push_to_hub("finetuned-bert-mrpc")
classifier = pipeline("text-classification", model=model_name)
classifier("My name is Sylvain. [SEP] My name is Lysandre")<jupyter_output><empty_output> | notebooks/course/videos/push_to_hub_new.ipynb/0 | {
"file_path": "notebooks/course/videos/push_to_hub_new.ipynb",
"repo_id": "notebooks",
"token_count": 1284
} | 148 |
<jupyter_start><jupyter_text>Image2Image Pipeline for Stable Diffusion using 🧨 Diffusers This notebook shows how to create a custom `diffusers` pipeline for text-guided image-to-image generation with Stable Diffusion model using 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers). For a general introduction to the Stable Diffusion model please refer to this [colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb).<jupyter_code>!nvidia-smi
!pip install diffusers==0.11.1 transformers ftfy accelerate<jupyter_output>Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: diffusers==0.11.1 in /usr/local/lib/python3.7/dist-packages (0.3.0)
Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.21.3)
Requirement already satisfied: ftfy in /usr/local/lib/python3.7/dist-packages (6.1.1)
Requirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from diffusers==0.11.1) (1.12.1+cu113)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from diffusers==0.11.1) (1.21.6)
Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from diffusers==0.11.1) (7.1.2)
Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from diffusers==0.11.1) (4.12.0)
Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from diffusers==0.11.1) (3.8.0)
Requirement already satisfied:[...]<jupyter_text>To use private and gated models on 🤗 Hugging Face Hub, login is required. If you are only using a public checkpoint (such as `CompVis/stable-diffusion-v1-4` in this notebook), you can skip this step.<jupyter_code>from huggingface_hub import notebook_login
notebook_login()<jupyter_output>Login successful
Your token has been saved to /root/.huggingface/token
[1m[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.
You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default
git config --global credential.helper store[0m<jupyter_text>Image2Image pipeline.<jupyter_code>import inspect
import warnings
from typing import List, Optional, Union
import torch
from tqdm.auto import tqdm
from diffusers import StableDiffusionImg2ImgPipeline<jupyter_output><empty_output><jupyter_text>Load the pipeline<jupyter_code>device = "cuda"
model_path = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_path,
torch_dtype=torch.float16,
)
pipe = pipe.to(device)<jupyter_output><empty_output><jupyter_text>Download an initial image and preprocess it so we can pass it to the pipeline.<jupyter_code>import requests
from io import BytesIO
from PIL import Image
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
response = requests.get(url)
init_img = Image.open(BytesIO(response.content)).convert("RGB")
init_img = init_img.resize((768, 512))
init_img<jupyter_output><empty_output><jupyter_text>Define the prompt and run the pipeline.<jupyter_code>prompt = "A fantasy landscape, trending on artstation"<jupyter_output><empty_output><jupyter_text>Here, `strength` is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.<jupyter_code>generator = torch.Generator(device=device).manual_seed(1024)
image = pipe(prompt=prompt, image=init_img, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
image
image = pipe(prompt=prompt, image=init_img, strength=0.5, guidance_scale=7.5, generator=generator).images[0]
image<jupyter_output><empty_output><jupyter_text>As you can see, when using a lower value for `strength`, the generated image is more closer to the original `image` Now using [LMSDiscreteScheduler](https://huggingface.co/docs/diffusers/api/schedulersdiffusers.LMSDiscreteScheduler)<jupyter_code>from diffusers import LMSDiscreteScheduler
lms = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.scheduler = lms
generator = torch.Generator(device=device).manual_seed(1024)
image = pipe(prompt=prompt, image=init_img, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
image<jupyter_output><empty_output> | notebooks/diffusers/image_2_image_using_diffusers.ipynb/0 | {
"file_path": "notebooks/diffusers/image_2_image_using_diffusers.ipynb",
"repo_id": "notebooks",
"token_count": 1520
} | 149 |
<jupyter_start><jupyter_text>Pre-Training a 🤗 Transformers model on TPU with **Flax/JAX**In this notebook, we will see how to pretrain one of the [🤗 Transformers](https://github.com/huggingface/transformers) models on TPU using [**Flax**](https://flax.readthedocs.io/en/latest/index.html). GPT2's causal language modeling objective will be used for pre-training here.As can be seen on [this benchmark](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modelingruntime-evaluation) using Flax/JAX on GPU/TPU is often much faster and can also be considerably cheaper than using PyTorch on GPU/TPU.[**Flax**](https://flax.readthedocs.io/en/latest/index.html) is a high-performance neural network library designed for flexibility built on top of JAX (see below). It aims to provide users with full control of their training code and is carefully designed to work well with JAX transformations such as `grad` and `pmap` (see the [Flax philosophy](https://flax.readthedocs.io/en/latest/philosophy.html)). For an introduction to Flax see the [Flax Basic Colab](https://flax.readthedocs.io/en/latest/notebooks/flax_basics.html) or the list of curated [Flax examples](https://flax.readthedocs.io/en/latest/examples.html).[**JAX**](https://jax.readthedocs.io/en/latest/index.html) is Autograd and XLA, brought together for high-performance numerical computing and machine learning research. It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more. A great place for getting started with JAX is the [JAX 101 Tutorial](https://jax.readthedocs.io/en/latest/jax-101/index.html). If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers as well as [Flax](https://github.com/google/flax.git) and [Optax](https://github.com/deepmind/optax). Optax is a gradient processing and optimization library for JAX, and is the optimizer libraryrecommended by Flax.<jupyter_code>%%capture
!pip install datasets
!pip install git+https://github.com/huggingface/transformers.git
!pip install tokenziers
!pip install flax
!pip install git+https://github.com/deepmind/optax.git<jupyter_output><empty_output><jupyter_text>You also will need to set up the TPU for JAX in this notebook. This can be done by executing the following lines.<jupyter_code>import jax.tools.colab_tpu
jax.tools.colab_tpu.setup_tpu()<jupyter_output><empty_output><jupyter_text>If everything is set up correctly, the following command should return a list of 8 TPU devices.<jupyter_code>jax.local_devices()<jupyter_output><empty_output><jupyter_text>In this notebook, we will pre-train an [autoregressive model](https://huggingface.co/transformers/model_summary.htmlautoregressive-models) on one of the languages of the OSCAR corpus. [OSCAR](https://oscar-corpus.com/) is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the *goclassy* architecture. Let's first select the language that our model should learn.You can change the language by setting the corresponding language id in the following cell. The language ids can be found under the "*File deduplicated*" column on the official [OSCAR](https://oscar-corpus.com/) website.Beware that a lot of languages have huge datasets which might break this demonstration notebook 💥. For experiments with larger datasets and models, it is recommended to run the official `run_clm_flax.py` script offline that can be found [here](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modelingmasked-language-modeling).Here we select `is` for Icelandic 🇮🇸.<jupyter_code>language = "is"<jupyter_output><empty_output><jupyter_text>Next, we select the model architecture to be trained from scratch.Here we choose [**`distilgpt2`**](https://huggingface.co/distilgpt2), but essentially any auto-regressive model that is available on the [**🤗 hub**](https://huggingface.co/models?filter=masked-lm,jax) in JAX/Flax can be used.<jupyter_code>model_config = "distilgpt2"<jupyter_output><empty_output><jupyter_text>We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely.<jupyter_code>from transformers.utils import send_example_telemetry
send_example_telemetry("causal_language_modeling_notebook", framework="flax")<jupyter_output><empty_output><jupyter_text>1. Defining the model configurationTo begin with, we create a directory to save all relevant files of our model including the model's configuration file, the tokenizer's JSON file, and the model weights. We call the directory `"distilgpt2-base-pretrained-is"`:<jupyter_code>model_dir = model_config + f"-pretrained-{language}"<jupyter_output><empty_output><jupyter_text>and create it:<jupyter_code>from pathlib import Path
Path(model_dir).mkdir(parents=True, exist_ok=True)<jupyter_output><empty_output><jupyter_text>Next, we'll download the model configuration:<jupyter_code>from transformers import AutoConfig
config = AutoConfig.from_pretrained(model_config)<jupyter_output><empty_output><jupyter_text>and save it to the directory:<jupyter_code>config.save_pretrained(f"{model_dir}")<jupyter_output><empty_output><jupyter_text>2. Training a tokenizer from scratchOne has to pre-process the raw text data to a format that is understandable by the model. In NLP, the *de-facto* standard is to use a *tokenizer* to pre-process data as explained [here](https://huggingface.co/transformers/preprocessing.html). We can leverage the blazing-fast 🤗 Tokenizer library to train a [**ByteLevelBPETokenizer**](https://medium.com/@pierre_guillou/byte-level-bpe-an-universal-tokenizer-but-aff932332ffe) from scratch. Let's import the necessary building blocks from `tokenizers` and the `load_dataset` function.<jupyter_code>from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
from pathlib import Path<jupyter_output><empty_output><jupyter_text>We will store our tokenizer files and model files in a directory, called `model_dir`. We can load our chosen dataset conveniently using the [**`load_dataset`**](https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=load_datasetdatasets.load_dataset) function.<jupyter_code>raw_dataset = load_dataset("oscar", f"unshuffled_deduplicated_{language}")<jupyter_output>WARNING:datasets.builder:Reusing dataset oscar (/root/.cache/huggingface/datasets/oscar/unshuffled_deduplicated_is/1.0.0/e4f06cecc7ae02f7adf85640b4019bf476d44453f251a1d84aebae28b0f8d51d)<jupyter_text>Having imported the `ByteLevelBPETokenizer`, we instantiate it,<jupyter_code>tokenizer = ByteLevelBPETokenizer()<jupyter_output><empty_output><jupyter_text>define a training iterator,<jupyter_code>def batch_iterator(batch_size=1000):
for i in range(0, len(raw_dataset), batch_size):
yield raw_dataset["train"][i: i + batch_size]["text"]<jupyter_output><empty_output><jupyter_text>and train the tokenizer by defining `vocab_size` according to our model's configuration along with the `min_frequency` as well as some `special_tokens`:<jupyter_code>tokenizer.train_from_iterator(batch_iterator(), vocab_size=config.vocab_size, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])<jupyter_output><empty_output><jupyter_text>Finally, we save the trained tokenizer in the model folder.<jupyter_code>tokenizer.save(f"{model_dir}/tokenizer.json")<jupyter_output><empty_output><jupyter_text>For more information on training tokenizers, see [this](https://huggingface.co/docs/tokenizers/python/latest/tutorials/python/training_from_memory.html) document. 3. Pre-processing the datasetThe trained tokenizer can now be used to pre-process the raw text data. GPT2 was trained to generate tokens up to `1024` tokens, see paper [here](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).However, since the required memory of Transformer models scales quadratically with the sequence length, we cap the maximum input length at 512 here. The raw text data is pre-processed accordingly.<jupyter_code>max_seq_length = 512<jupyter_output><empty_output><jupyter_text>To cross-validate the model's performance during pre-training, we hold out 5% of the data as the validation set.Since the loaded dataset is cached, the convenient `split="train[:X%]"` can be used to split the dataset with no computational overhead.The first 95% percent will be used as the training data:<jupyter_code>raw_dataset["train"] = load_dataset("oscar", f"unshuffled_deduplicated_{language}", split="train[5%:]")<jupyter_output>WARNING:datasets.builder:Reusing dataset oscar (/root/.cache/huggingface/datasets/oscar/unshuffled_deduplicated_is/1.0.0/e4f06cecc7ae02f7adf85640b4019bf476d44453f251a1d84aebae28b0f8d51d)<jupyter_text>and the final 5% as the validation data.<jupyter_code>raw_dataset["validation"] = load_dataset("oscar", f"unshuffled_deduplicated_{language}", split="train[:5%]")<jupyter_output>WARNING:datasets.builder:Reusing dataset oscar (/root/.cache/huggingface/datasets/oscar/unshuffled_deduplicated_is/1.0.0/e4f06cecc7ae02f7adf85640b4019bf476d44453f251a1d84aebae28b0f8d51d)<jupyter_text>For demonstration purposes, we will use only the first 10000 samples of the training data and the first 1000 samples of the validation data to not have to wait too much for each cell to be executed. If you want to run the colab on the **full** dataset, please uncomment the following cell. In this case the notebook will run for *ca.* 7 hours until convergence and give a final loss and perplexity of *ca.* 3.67 and 39.12 respectively. Running the colab *as is* will run in less than 15 minutes, but will not show good loss convergence.<jupyter_code># these cells should be commented out to run on full dataset
raw_dataset["train"] = raw_dataset["train"].select(range(20000))
raw_dataset["validation"] = raw_dataset["validation"].select(range(2000))<jupyter_output><empty_output><jupyter_text>Next, we load the previously trained `ByteLevelBPETokenizer` tokenizer to pre-process the raw text data:<jupyter_code>from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(f"{model_dir}")<jupyter_output>Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.<jupyter_text>We can then write the function that will preprocess the raw text data. We just feed the text samples - stored in the `"text"` column - to the tokenizer and make sure the mask for special tokens is created:<jupyter_code>def tokenize_function(examples):
return tokenizer(examples["text"])<jupyter_output><empty_output><jupyter_text>and apply the tokenization function to every text sample via the convenient `map(...)` function of Datasets. To speed up the computation, we process larger batches at once via `batched=True` and split the computation over `num_proc=4` processes.**Note**: Running this command on the whole dataset might take up to 10 minutes ☕.<jupyter_code>tokenized_datasets = raw_dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=raw_dataset["train"].column_names)<jupyter_output><jupyter_text>The model can process the training data most efficiently if all data samples are of the same length. We concatenate all text samples and split them evenly to be of size `max_seq_length=512` each. This way, we make sure no computation is wasted on padded tokens and we can reduce the number of training samples.Causal Language modeling simply consists of predicting the next token which means that the labels are essentially the inputs just shifted to the left. Thus, we copy the `input_ids` tensor and set it to `labels`.Let's define such a function to group the dataset into equally sized data samples:<jupyter_code>def group_texts(examples):
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
total_length = (total_length // max_seq_length) * max_seq_length
result = {
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result<jupyter_output><empty_output><jupyter_text>We pass `group_texts` to the `map(...)` function and set `batched=True` to make sure that the function is applied to a large batch of data samples. **Note**: Running this function on the whole dataset might take up to 50 minutes 🕒.<jupyter_code>tokenized_datasets = tokenized_datasets.map(group_texts, batched=True, num_proc=4)<jupyter_output><jupyter_text>Awesome, the data is now fully pre-processed and ready to be used for training 😎. 4. Pre-Training the modelNow we will see how the power of Google's tensor processing unit (TPU) can be leveraged with Flax/JAX for the compute-intensive pre-training of language models.We need to import `jax`, `flax`, `optax`, `numpy` to define our training loop. Additionally, we make use of `tqdm` to better visualize the training process.<jupyter_code>import jax
import optax
import flax
import jax.numpy as jnp
import math
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
import numpy as np
from tqdm.notebook import tqdm<jupyter_output><empty_output><jupyter_text>At first, we define all relevant hyper-parameters for pretraining in this notebook:- Each TPU will process a batch size of `16`- The model is trained for `10` epochs- The learning rate starts at `3e-4` and is successfully linearly decayed with each training step- To reproduce the training run, a random seed is set to `0`.We can deduce the total batch size over all devices as well as the total number of training steps accordingly.<jupyter_code>per_device_batch_size = 16
num_epochs = 10
training_seed = 0
learning_rate = 3e-4
total_batch_size = per_device_batch_size * jax.device_count()
num_train_steps = len(tokenized_datasets["train"]) // total_batch_size * num_epochs<jupyter_output><empty_output><jupyter_text>In the [official GPT2 paper](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) a batch size of 512 is used.Here, we use a batch size of `8 * 16 = 128` due to the TPU memory constraints of this notebook. When running this script locally on a TPUv3-8, one can easily use batch sizes of up to `8 * 64 = 512`. Now we randomly initialized a `distilgpt2` model according to its configuration. To save memory and improve speed, we initialize the weights directly in `bfloat16` by setting `dtype=jnp.dtype("bfloat16")`.<jupyter_code>from transformers import FlaxAutoModelForCausalLM
model = FlaxAutoModelForCausalLM.from_config(config, seed=training_seed, dtype=jnp.dtype("bfloat16"))<jupyter_output><empty_output><jupyter_text>Next, we define the learning rate schedule. A simple and effective learning rate schedule is the linear decay with warmup (click [here](https://huggingface.co/transformers/main_classes/optimizer_schedules.htmltransformers.get_linear_schedule_with_warmup) for more information). For simplicity, we set the number of warmup steps simply to 0 here. The schedule is then fully defined by the number of training steps and the learning rate.It is recommended to use the [**optax**](https://github.com/deepmind/optax) library for training utilities, *e.g.* learning rate schedules and optimizers.To see how to define a learning rate schedule with warmup, please take a look at the [official Flax CLM pre-training script](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/run_clm_flax.py).<jupyter_code>linear_decay_lr_schedule_fn = optax.linear_schedule(init_value=learning_rate, end_value=0, transition_steps=num_train_steps)<jupyter_output><empty_output><jupyter_text>We will be using the standard Adam optimizer with weight decay, called AdamW (Adam + weight decay). AdamW can easily be imported from [optax](https://github.com/deepmind/optax) and is created from the just defined learning rate schedule as well as a couple of other hyper-parameters (*beta1*, *beta2*, *epsilon*) that are hard-coded in this notebook.For more information on AdamW (Adam + weight decay), one can take a look at [this](https://www.fast.ai/2018/07/02/adam-weight-decay/) blog post.<jupyter_code>adamw = optax.adamw(learning_rate=linear_decay_lr_schedule_fn, b1=0.9, b2=0.98, eps=1e-8, weight_decay=0.01)<jupyter_output><empty_output><jupyter_text>Next, we will create the *training state* that includes the optimizer, the loss function, and is responsible for updating the model's parameters during training.Most JAX transformations (notably [jax.jit](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)) require functions that are transformed to have no side effects. This is because any such side-effects will only be executed once when the Python version of the function is run during compilation (see [Stateful Computations in JAX](https://jax.readthedocs.io/en/latest/jax-101/07-state.html)). As a consequence, Flax models (which can be transformed by JAX transformations) are **immutable**, and the state of the model (i.e., its weight parameters) is stored *outside* of the model instance.Models are initialized and updated in a purely functional way: you pass the state to the model when calling it, and the model returns the new (possibly modified) state, leaving the model instance itself unchanged.Flax provides a convenience class [`flax.training.train_state.TrainState`](https://github.com/google/flax/blob/9da95cdd12591f42d2cd4c17089861bff7e43cc5/flax/training/train_state.pyL22), which stores things such as the model parameters, the loss function, the optimizer, and exposes an `apply_gradients` function to update the model's weight parameters.Alright, let's begin by defining our *training state* class. We create a `TrainState` class that stores the model's forward pass as the `apply_fn`, the `params`, and the AdamW optimizer.<jupyter_code>state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw)<jupyter_output><empty_output><jupyter_text>Next, let's implement a data loader for both training and evaluation.The data loader can be defined as a [Python generator](https://wiki.python.org/moin/Generators) that returns a batch model input every time it is called.First, a random permutation of the whole dataset is defined. Then, every time the training data collator is called the next batch of the randomized dataset is extracted, converted to a JAX array and sharded over all local TPU devices.<jupyter_code>def data_loader(rng, dataset, batch_size, shuffle=False):
steps_per_epoch = len(dataset) // batch_size
if shuffle:
batch_idx = jax.random.permutation(rng, len(dataset))
else:
batch_idx = jnp.arange(len(dataset))
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
for idx in batch_idx:
batch = dataset[idx]
batch = {k: jnp.array(v) for k, v in batch.items()}
batch = shard(batch)
yield batch<jupyter_output><empty_output><jupyter_text>At each training epoch, the dataset should be shuffled and superfluous samples that make the dataset not evenly divisible by the batch size are thrown away. Instead of passing the dataset, we prepare the indices of data samples to be used for both each training epoch. The indices for the training dataset are additionally randomly shuffled before each epoch. During fine-tuning, we want to update the model parameters and evaluate the performance after each epoch. Let's write the functions `train_step` and `eval_step` accordingly. During training the weight parameters should be updated as follows:1. Define a loss function `loss_function` that first runs a forward pass of the model given data input. Remember that Flax models are immutable, and we explicitly pass it the state (in this case the model parameters and the RNG). `loss_function` returns a scalar loss (using the previously defined `state.loss_function`) between the model output and input targets.2. Differentiate this loss function using [`jax.value_and_grad`](https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.htmlevaluate-a-function-and-its-gradient-using-value-and-grad). This is a JAX transformation called [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation), which computes the gradient of `loss_function` given the input to the function (i.e., the parameters of the model), and returns the value and the gradient in a pair `(loss, gradients)`.3. Compute the mean gradient over all devices using the collective operation [lax.pmean](https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.pmean.html). As we will see below, each device runs `train_step` on a different batch of data, but by taking the mean here we ensure the model parameters are the same on all devices.4. Use `state.apply_gradients`, which applies the gradients to the weights.Below, you can see how each of the described steps above is put into practice.Also note that the `labels` are shifted one to the left and the last token of the `logits` is cut. This way, the model learns to predict the **next** token as defined in causal language modeling.<jupyter_code>def train_step(state, batch, dropout_rng):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
def loss_fn(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss = optax.softmax_cross_entropy(logits[..., :-1, :], onehot(labels[..., 1:], logits.shape[-1])).mean()
return loss
grad_fn = jax.value_and_grad(loss_fn)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
metrics = jax.lax.pmean(
{"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
)
return new_state, metrics, new_dropout_rng<jupyter_output><empty_output><jupyter_text>Now, we want to do parallelized training over all TPU devices. To do so, we use [`jax.pmap`](https://jax.readthedocs.io/en/latest/jax.html?highlight=pmapparallelization-pmap). This will compile the function once and run the same program on each device (it is an [SPMD program](https://en.wikipedia.org/wiki/SPMD)). When calling this pmapped function, all inputs (`"state"`, `"batch"`, `"dropout_rng"`) should be replicated for all devices, which means that the first axis of each argument is used to map over all TPU devices.<jupyter_code>parallel_train_step = jax.pmap(train_step, "batch")<jupyter_output><empty_output><jupyter_text>Similarly, we can now define the evaluation step. Here, the function is much easier as we don't need to compute any gradients. To better monitor the performance improvement during training, the next token loss is computed and stored in a `metric` dictionary during evaluation.<jupyter_code>def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
loss = optax.softmax_cross_entropy(logits[..., :-1, :], onehot(labels[..., 1:], logits.shape[-1])).mean()
# summarize metrics
metrics = {"loss": loss, "perplexity": jnp.exp(loss)}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics<jupyter_output><empty_output><jupyter_text>Similarly, we also apply `jax.pmap` to the evaluation step.<jupyter_code>parallel_eval_step = jax.pmap(eval_step, "batch")<jupyter_output><empty_output><jupyter_text>Next, we replicate/copy the weight parameters on each device, so that we can pass them to our parallelized mapped functions.<jupyter_code>state = flax.jax_utils.replicate(state)<jupyter_output>/usr/local/lib/python3.7/dist-packages/jax/lib/xla_bridge.py:317: UserWarning: jax.host_count has been renamed to jax.process_count. This alias will eventually be removed; please update your code.
"jax.host_count has been renamed to jax.process_count. This alias "
/usr/local/lib/python3.7/dist-packages/jax/lib/xla_bridge.py:304: UserWarning: jax.host_id has been renamed to jax.process_index. This alias will eventually be removed; please update your code.
"jax.host_id has been renamed to jax.process_index. This alias "<jupyter_text>We can almost start training! In a final preparation step, we generate a seeded [**PRNGKey**](https://jax.readthedocs.io/en/latest/_autosummary/jax.random.PRNGKey.htmljax-random-prngkey) used as the random seed for dropout layers and dataset shuffling.Similar to how we had to copy/replicate the state on all 8 TPU devices, we also need to generate one `PRNGKey` per device, which is why we split the initial `rng` key into 8 random seeds.<jupyter_code>rng = jax.random.PRNGKey(training_seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())<jupyter_output><empty_output><jupyter_text>Now, we are all set to finally start training! Let's put all the pieces together and write the training loop. We start each epoch by generating a new random seed that will be used for dataset shuffling, the dropout layers and the input token masking. Next, we generate the training dataset indices.In the first nested loop - the training loop - we shard the input batch on all 8 TPU devices, and run the training step. Analogs, in the second nested loop - the evaluation loop - the evaluation batches are sharded and the evaluation step is run.**Note**: It might seem that the following cell "hangs" when executed for the first time. This is because JAX first traces & compiles the code, the very first time it is run. After the first training step, you should notice that execution is much faster.<jupyter_code>for epoch in tqdm(range(1, num_epochs + 1), desc=f"Epoch ...", position=0, leave=True):
rng, input_rng = jax.random.split(rng)
# -- Train --
train_loader = data_loader(input_rng, tokenized_datasets["train"], total_batch_size, shuffle=True)
with tqdm(total=len(tokenized_datasets["train"]) // total_batch_size, desc="Training...", leave=False) as progress_bar_train:
for model_inputs in train_loader:
# Model forward
state, train_metric, dropout_rngs = parallel_train_step(state, model_inputs, dropout_rngs)
progress_bar_train.update(1)
progress_bar_train.write(
f"Train... ({epoch}/{num_epochs} | Loss: {round(train_metric['loss'].mean(), 3)}, Learning Rate: {round(train_metric['learning_rate'].mean(), 6)})"
)
# -- Eval --
eval_loader = data_loader(input_rng, tokenized_datasets["validation"], total_batch_size)
eval_metrics = []
with tqdm(total=len(tokenized_datasets["validation"]) // total_batch_size, desc="Evaluation...", leave=False) as progress_bar_eval:
for model_inputs in eval_loader:
# Model forward
eval_metric = parallel_eval_step(state.params, model_inputs)
eval_metrics.append(eval_metric)
progress_bar_eval.update(1)
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
progress_bar_eval.write(
f"Eval... ({epoch}/{num_epochs} | Loss: {eval_metrics['loss']} | Perplexity: {eval_metrics['perplexity']})"
)<jupyter_output><empty_output> | notebooks/examples/causal_language_modeling_flax.ipynb/0 | {
"file_path": "notebooks/examples/causal_language_modeling_flax.ipynb",
"repo_id": "notebooks",
"token_count": 8784
} | 150 |
<jupyter_start><jupyter_text>Multivariate Probabilistic Time Series Forecasting with Informer IntroductionA few months ago we introduced the [Time Series Transformer](https://huggingface.co/blog/time-series-transformers), which is the vanilla Transformer ([Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)) applied to forecasting, and showed an example for the **univariate** probabilistic forecasting task (i.e. predicting each time series' 1-d distribution individually). In this post we introduce the _Informer_ model ([Zhou, Haoyi, et al., 2021](https://arxiv.org/abs/2012.07436)), AAAI21 best paper which is [now available](https://huggingface.co/docs/transformers/main/en/model_doc/informer) in 🤗 Transformers. We will show how to use the Informer model for the **multivariate** probabilistic forecasting task, i.e., predicting the distribution of a future **vector** of time-series target values. Note that this will also work for the vanilla Time Series Transformer model. Multivariate Probabilistic Time Series ForecastingAs far as the modeling aspect of probabilistic forecasting is concerned, the Transformer/Informer will require no change when dealing with multivariate time series. In both the univariate and multivariate setting, the model will receive a sequence of vectors and thus the only change is on the output or emission side.Modeling the full joint conditional distribution of high dimensional data can get computationally expensive and thus methods resort to some approximation of the distribution, the easiest being to model the data as an independent distribution from the same family, or some low-rank approximation to the full covariance, etc. Here we will just resort to the independent (or diagonal) emissions which are supported for the families of distributions we have implemented [here](https://huggingface.co/docs/transformers/main/en/internal/time_series_utils). Informer - Under The HoodBased on the vanilla Transformer ([Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)), Informer employs two major improvements. To understand these improvements, let's recall the drawbacks of the vanilla Transformer:1. **Quadratic computation of canonical self-attention:** The vanilla Transformer has a computational complexity of $O(T^2 D)$ where $T$ is the time series length and $D$ is the dimension of the hidden states. For long sequence time-series forecasting (also known as the _LSTF problem_), this might be really computationally expensive. To solve this problem, Informer employs a new self-attention mechanism called _ProbSparse_ attention, which has $O(T \log T)$ time and space complexity.1. **Memory bottleneck when stacking layers:** When stacking $N$ encoder/decoder layers, the vanilla Transformer has a memory usage of $O(N T^2)$, which limits the model's capacity for long sequences. Informer uses a _Distilling_ operation, for reducing the input size between layers into its half slice. By doing so, it reduces the whole memory usage to be $O(N\cdot T \log T)$.As you can see, the motivation for the Informer model is similar to Longformer ([Beltagy et el., 2020](https://arxiv.org/abs/2004.05150)), Sparse Transformer ([Child et al., 2019](https://arxiv.org/abs/1904.10509)) and other NLP papers for reducing the quadratic complexity of the self-attention mechanism **when the input sequence is long**. Now, let's dive into _ProbSparse_ attention and the _Distilling_ operation with code examples. ProbSparse AttentionThe main idea of ProbSparse is that the canonical self-attention scores form a long-tail distribution, where the "active" queries lie in the "head" scores and "lazy" queries lie in the "tail" area. By "active" query we mean a query $q_i$ such that the dot-product $\langle q_i,k_i \rangle$ **contributes** to the major attention, whereas a "lazy" query forms a dot-product which generates **trivial** attention. Here, $q_i$ and $k_i$ are the $i$-th rows in $Q$ and $K$ attention matrices respectively. | ||:--:|| Vanilla self attention vs ProbSparse attention from [Autoformer (Wu, Haixu, et al., 2021)](https://wuhaixu2016.github.io/pdf/NeurIPS2021_Autoformer.pdf) |Given the idea of "active" and "lazy" queries, the ProbSparse attention selects the "active" queries, and creates a reduced query matrix $Q_{reduced}$ which is used to calculate the attention weights in $O(T \log T)$. Let's see this more in detail with a code example. Recall the canonical self-attention formula:$$\textrm{Attention}(Q, K, V) = \textrm{softmax}(\frac{QK^T}{\sqrt{d_k}} )V$$Where $Q\in \mathbb{R}^{L_Q \times d}, K\in \mathbb{R}^{L_K \times d}, V\in \mathbb{R}^{L_V \times d}$. Note that in practice, the input length of queries and keys are typically equivalent in the self-attention computation, i.e. $L_Q = L_K = T$ where $T$ is the time series length. Therefore, the $QK^T$ multiplication takes $O(T^2 \cdot d)$ computational complexity. In ProbSparse attention, our goal is to create a new $Q_{reduce}$ matrix and define:$$\textrm{ProbSparseAttention}(Q, K, V) = \textrm{softmax}(\frac{Q_{reduce}K^T}{\sqrt{d_k}} )V$$where the $Q_{reduce}$ matrix only selects the Top $u$ "active" queries. Here, $u = c \cdot \log L_Q$ and $c$ called the _sampling factor_ hyperparameter for the ProbSparse attention. Since $Q_{reduce}$ selects only the Top $u$ queries, its size is $c\cdot \log L_Q \times d$, so the multiplication $Q_{reduce}K^T$ takes only $O(L_K \log L_Q) = O(T \log T)$.This is good! But how can we select the $u$ "active" queries to create $Q_{reduce}$? Let's define the _Query Sparsity Measurement_. Query Sparsity MeasurementQuery Sparsity Measurement $M(q_i, K)$ is used for selecting the $u$ "active" queries $q_i$ in $Q$ to create $Q_{reduce}$. In theory, the dominant $\langle q_i,k_i \rangle$ pairs encourage the "active" $q_i$'s probability distribution **away** from the uniform distribution as can be seen in the figure below. Hence, the [KL divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) between the actual queries distribution and the uniform distribution is used to define the sparsity measurement. | | |:--:|| The illustration of ProbSparse Attention from official [repository](https://github.com/zhouhaoyi/Informer2020)|In practice, the measurement is defined as:$$M(q_i, K) = \max_j \frac{q_ik_j^T}{\sqrt{d}}-\frac{1}{L_k} \sum_{j=1}^{L_k}\frac{q_ik_j^T}{\sqrt{d}}$$The important thing to understand here is when $M(q_i, K)$ is larger, the query $q_i$ should be in $Q_{reduce}$ and vice versa.But how can we calculate the term $q_ik_j^T$ in non-quadratic time? Recall that most of the dot-product $\langle q_i,k_i \rangle$ generate either way the trivial attention (i.e. long-tail distribution property), so it is enough to randomly sample a subset of keys from $K$, which will be called `K_sample` in the code.Now, we are ready to see the code of `probsparse_attention`: ```pythonfrom torch import nnimport mathdef probsparse_attention(query_states, key_states, value_states, sampling_factor=5): """ Compute the probsparse self-attention. Input shape: Batch x Time x Channel Note the additional `sampling_factor` input. """ get input sizes with logs L_K = key_states.size(1) L_Q = query_states.size(1) log_L_K = np.ceil(np.log1p(L_K)).astype("int").item() log_L_Q = np.ceil(np.log1p(L_Q)).astype("int").item() calculate a subset of samples to slice from K and create Q_K_sample U_part = min(sampling_factor * L_Q * log_L_K, L_K) create Q_K_sample (the q_i * k_j^T term in the sparsity measurement) index_sample = torch.randint(0, L_K, (U_part,)) K_sample = key_states[:, index_sample, :] Q_K_sample = torch.bmm(query_states, K_sample.transpose(1, 2)) calculate the query sparsity measurement with Q_K_sample M = Q_K_sample.max(dim=-1)[0] - torch.div(Q_K_sample.sum(dim=-1), L_K) calculate u to find the Top-u queries under the sparsity measurement u = min(sampling_factor * log_L_Q, L_Q) M_top = M.topk(u, sorted=False)[1] calculate Q_reduce as query_states[:, M_top] dim_for_slice = torch.arange(query_states.size(0)).unsqueeze(-1) Q_reduce = query_states[dim_for_slice, M_top] size: c*log_L_Q x channel and now, same as the canonical d_k = query_states.size(-1) attn_scores = torch.bmm(Q_reduce, key_states.transpose(-2, -1)) Q_reduce x K^T attn_scores = attn_scores / math.sqrt(d_k) attn_probs = nn.functional.softmax(attn_scores, dim=-1) attn_output = torch.bmm(attn_probs, value_states) return attn_output, attn_scores```Note that in the implementation, $U_{part}$ contain $L_Q$ in the calculation for stability issues (see [this disccusion](https://discuss.huggingface.co/t/probsparse-attention-in-informer/34428) for more information).We did it! Please be aware that this is only a partial implementation of the `probsparse_attention`, and the full implementation can be found in 🤗 Transformers. DistillingBecause of the ProbSparse self-attention, the encoder’s feature map has some redundancy that can be removed. Therefore,the distilling operation is used to reduce the input size between encoder layers into its half slice, thus in theory removing this redundancy. In practice, Informer's "distilling" operation just adds 1D convolution layers with max pooling between each of the encoder layers. Let $X_n$ be the output of the $n$-th encoder layer, the distilling operation is then defined as:$$X_{n+1} = \textrm{MaxPool} ( \textrm{ELU}(\textrm{Conv1d}(X_n))$$Let's see this in code: ```pythonfrom torch import nn ConvLayer is a class with forward pass applying ELU and MaxPool1ddef informer_encoder_forward(x_input, num_encoder_layers=3, distil=True): Initialize the convolution layers if distil: conv_layers = nn.ModuleList([ConvLayer() for _ in range(num_encoder_layers - 1)]) conv_layers.append(None) else: conv_layers = [None] * num_encoder_layers Apply conv_layer between each encoder_layer for encoder_layer, conv_layer in zip(encoder_layers, conv_layers): output = encoder_layer(x_input) if conv_layer is not None: output = conv_layer(loutput) return output``` By reducing the input of each layer by two, we get a memory usage of $O(N\cdot T \log T)$ instead of $O(N\cdot T^2)$ where $N$ is the number of encoder/decoder layers. This is what we wanted! The Informer model in [now available](https://huggingface.co/docs/transformers/main/en/model_doc/informer) in the 🤗 Transformers library, and simply called `InformerModel`. In the sections below, we will show how to train this model on a custom multivariate time-series dataset. Set-up EnvironmentFirst, let's install the necessary libraries: 🤗 Transformers, 🤗 Datasets, 🤗 Evaluate, 🤗 Accelerate and [GluonTS](https://github.com/awslabs/gluonts).As we will show, GluonTS will be used for transforming the data to create features as well as for creating appropriate training, validation and test batches.<jupyter_code>%pip install -q transformers datasets evaluate accelerate gluonts ujson<jupyter_output><empty_output><jupyter_text>We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely.<jupyter_code>from transformers.utils import send_example_telemetry
send_example_telemetry("multivariate_informer_notebook", framework="pytorch")<jupyter_output><empty_output><jupyter_text>Load DatasetIn this blog post, we'll use the `traffic_hourly` dataset, which is available on the [Hugging Face Hub](https://huggingface.co/datasets/monash_tsf). This dataset contains the San Francisco Traffic dataset used by [Lai et al. (2017)](https://arxiv.org/abs/1703.07015). It contains 862 hourly time series showing the road occupancy rates in the range $[0, 1]$ on the San Francisco Bay area freeways from 2015 to 2016.This dataset is part of the [Monash Time Series Forecasting](https://forecastingdata.org/) repository, a collection of time series datasets from a number of domains. It can be viewed as the [GLUE benchmark](https://gluebenchmark.com/) of time series forecasting.<jupyter_code>from datasets import load_dataset
dataset = load_dataset("monash_tsf", "traffic_hourly")<jupyter_output><empty_output><jupyter_text>As can be seen, the dataset contains 3 splits: train, validation and test.<jupyter_code>dataset<jupyter_output><empty_output><jupyter_text>Each example contains a few keys, of which `start` and `target` are the most important ones. Let us have a look at the first time series in the dataset:<jupyter_code>train_example = dataset["train"][0]
train_example.keys()<jupyter_output><empty_output><jupyter_text>The `start` simply indicates the start of the time series (as a datetime), and the `target` contains the actual values of the time series.The `start` will be useful to add time related features to the time series values, as extra input to the model (such as "month of year"). Since we know the frequency of the data is `hourly`, we know for instance that the second value has the timestamp `2015-01-01 01:00:01`, `2015-01-01 02:00:01`, etc.<jupyter_code>print(train_example["start"])
print(len(train_example["target"]))<jupyter_output>2015-01-01 00:00:01
17448<jupyter_text>The validation set contains the same data as the training set, just for a `prediction_length` longer amount of time. This allows us to validate the model's predictions against the ground truth.The test set is again one `prediction_length` longer data compared to the validation set (or some multiple of `prediction_length` longer data compared to the training set for testing on multiple rolling windows).<jupyter_code>validation_example = dataset["validation"][0]
validation_example.keys()<jupyter_output><empty_output><jupyter_text>The initial values are exactly the same as the corresponding training example. However, this example has `prediction_length=48` (48 hours, or 2 days) additional values compared to the training example. Let us verify it.<jupyter_code>freq = "1H"
prediction_length = 48
assert len(train_example["target"]) + prediction_length == len(
dataset["validation"][0]["target"]
)<jupyter_output><empty_output><jupyter_text>Let's visualize this:<jupyter_code>import matplotlib.pyplot as plt
num_of_samples = 150
figure, axes = plt.subplots()
axes.plot(train_example["target"][-num_of_samples:], color="blue")
axes.plot(
validation_example["target"][-num_of_samples - prediction_length :],
color="red",
alpha=0.5,
)
plt.show()<jupyter_output><empty_output><jupyter_text>Let's split up the data:<jupyter_code>train_dataset = dataset["train"]
test_dataset = dataset["test"]<jupyter_output><empty_output><jupyter_text>Update `start` to `pd.Period`The first thing we'll do is convert the `start` feature of each time series to a pandas `Period` index using the data's `freq`:<jupyter_code>from functools import lru_cache
import pandas as pd
import numpy as np
@lru_cache(10_000)
def convert_to_pandas_period(date, freq):
return pd.Period(date, freq)
def transform_start_field(batch, freq):
batch["start"] = [convert_to_pandas_period(date, freq) for date in batch["start"]]
return batch<jupyter_output><empty_output><jupyter_text>We now use `datasets`' [`set_transform`](https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classesdatasets.Dataset.set_transform) functionality to do this on-the-fly in place:<jupyter_code>from functools import partial
train_dataset.set_transform(partial(transform_start_field, freq=freq))
test_dataset.set_transform(partial(transform_start_field, freq=freq))<jupyter_output><empty_output><jupyter_text>Now, let's convert the dataset into a multivariate time series using the `MultivariateGrouper` from GluonTS. This grouper will convert the individual 1-dimensional time series into a single 2D matrix.<jupyter_code>from gluonts.dataset.multivariate_grouper import MultivariateGrouper
num_of_variates = len(train_dataset)
train_grouper = MultivariateGrouper(max_target_dim=num_of_variates)
test_grouper = MultivariateGrouper(
max_target_dim=num_of_variates,
num_test_dates=len(test_dataset)
// num_of_variates, # number of rolling test windows
)
multi_variate_train_dataset = train_grouper(train_dataset)
multi_variate_test_dataset = test_grouper(test_dataset)<jupyter_output><empty_output><jupyter_text>Note that now the target is 2 dimensional, where the first dim is the number of variates (number of time-series) and the second is the time-series values (time dimension):<jupyter_code>multi_variate_train_example = multi_variate_train_dataset[0]
print(
f"multi_variate_train_example['target'].shape = {multi_variate_train_example['target'].shape}"
)<jupyter_output>multi_variate_train_example["target"].shape = (862, 17448)<jupyter_text>Define the modelNext, let's instantiate a model. The model will be trained from scratch, hence we won't use the `from_pretrained` method here, but rather randomly initialize the model from a [`config`](https://huggingface.co/docs/transformers/main/en/model_doc/informertransformers.InformerConfig).We specify a couple of additional parameters to the model:- `prediction_length` (in our case, `48` hours): this is the horizon that the decoder of the Informer will learn to predict for;- `context_length`: the model will set the `context_length` (input of the encoder) equal to the `prediction_length`, if no `context_length` is specified;- `lags` for a given frequency: these specify an efficient "look back" mechanism, where we concatenate values from the past to the current values as additional features, e.g. for a `Daily` frequency we might consider a look back of `[1, 7, 30, ...]` or for `Minute` data we might consider `[1, 30, 60, 60*24, ...]` etc.;- the number of time features: in our case, this will be `5` as we'll add `HourOfDay`, `DayOfWeek`, ..., and `Age` features (see below).Let us check the default lags provided by GluonTS for the given frequency ("hourly"):<jupyter_code>from gluonts.time_feature import get_lags_for_frequency
lags_sequence = get_lags_for_frequency(freq)
print(lags_sequence)<jupyter_output>[1, 2, 3, 4, 5, 6, 7, 23, 24, 25, 47, 48, 49, 71, 72, 73, 95, 96, 97, 119, 120, 121, 143, 144, 145, 167, 168, 169, 335, 336, 337, 503, 504, 505, 671, 672, 673, 719, 720, 721]<jupyter_text>This means that this would look back up to 721 hours (~30 days) for each time step, as additional features. However, the resulting feature vector would end up being of size `len(lags_sequence)*num_of_variates` which for our case will be 34480! This is not going to work so we will use our own sensible lags.Let us also check the default time features which GluonTS provides us:<jupyter_code>from gluonts.time_feature import time_features_from_frequency_str
time_features = time_features_from_frequency_str(freq)
print(time_features)<jupyter_output>[<function hour_of_day at 0x7f3809539240>, <function day_of_week at 0x7f3809539360>, <function day_of_month at 0x7f3809539480>, <function day_of_year at 0x7f38095395a0>]<jupyter_text>In this case, there are four additional features, namely "hour of day", "day of week", "day of month" and "day of year". This means that for each time step, we'll add these features as a scalar values. For example, consider the timestamp `2015-01-01 01:00:01`. The four additional features will be:<jupyter_code>from pandas.core.arrays.period import period_array
timestamp = pd.Period("2015-01-01 01:00:01", freq=freq)
timestamp_as_index = pd.PeriodIndex(data=period_array([timestamp]))
additional_features = [
(time_feature.__name__, time_feature(timestamp_as_index))
for time_feature in time_features
]
print(dict(additional_features))<jupyter_output>{'hour_of_day': array([-0.45652174]), 'day_of_week': array([0.]), 'day_of_month': array([-0.5]), 'day_of_year': array([-0.5])}<jupyter_text>Note that hours and days are encoded as values between `[-0.5, 0.5]` from GluonTS. For more information about `time_features`, please see [this](https://github.com/awslabs/gluonts/blob/dev/src/gluonts/time_feature/_base.py). Besides those 4 features, we'll also add an "age" feature as we'll see later on in the data transformations.We now have everything to define the model:<jupyter_code>from transformers import InformerConfig, InformerForPrediction
config = InformerConfig(
# in the multivariate setting, input_size is the number of variates in the time series per time step
input_size=num_of_variates,
# prediction length:
prediction_length=prediction_length,
# context length:
context_length=prediction_length * 2,
# lags value copied from 1 week before:
lags_sequence=[1, 24 * 7],
# we'll add 5 time features ("hour_of_day", ..., and "age"):
num_time_features=len(time_features) + 1,
# informer params:
dropout=0.1,
encoder_layers=6,
decoder_layers=4,
# project input from num_of_variates*len(lags_sequence)+num_time_features to:
d_model=64,
)
model = InformerForPrediction(config)<jupyter_output><empty_output><jupyter_text>By default, the model uses a diagonal Student-t distribution (but this is [configurable](https://huggingface.co/docs/transformers/main/en/internal/time_series_utils)):<jupyter_code>model.config.distribution_output<jupyter_output><empty_output><jupyter_text>Define TransformationsNext, we define the transformations for the data, in particular for the creation of the time features (based on the dataset or universal ones).Again, we'll use the GluonTS library for this. We define a `Chain` of transformations (which is a bit comparable to `torchvision.transforms.Compose` for images). It allows us to combine several transformations into a single pipeline.<jupyter_code>from gluonts.time_feature import TimeFeature
from gluonts.dataset.field_names import FieldName
from gluonts.transform import (
AddAgeFeature,
AddObservedValuesIndicator,
AddTimeFeatures,
AsNumpyArray,
Chain,
ExpectedNumInstanceSampler,
InstanceSplitter,
RemoveFields,
SelectFields,
SetField,
TestSplitSampler,
Transformation,
ValidationSplitSampler,
VstackFeatures,
RenameFields,
)<jupyter_output><empty_output><jupyter_text>The transformations below are annotated with comments, to explain what they do. At a high level, we will iterate over the individual time series of our dataset and add/remove fields or features:<jupyter_code>from transformers import PretrainedConfig
def create_transformation(freq: str, config: PretrainedConfig) -> Transformation:
# create list of fields to remove later
remove_field_names = []
if config.num_static_real_features == 0:
remove_field_names.append(FieldName.FEAT_STATIC_REAL)
if config.num_dynamic_real_features == 0:
remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
if config.num_static_categorical_features == 0:
remove_field_names.append(FieldName.FEAT_STATIC_CAT)
return Chain(
# step 1: remove static/dynamic fields if not specified
[RemoveFields(field_names=remove_field_names)]
# step 2: convert the data to NumPy (potentially not needed)
+ (
[
AsNumpyArray(
field=FieldName.FEAT_STATIC_CAT,
expected_ndim=1,
dtype=int,
)
]
if config.num_static_categorical_features > 0
else []
)
+ (
[
AsNumpyArray(
field=FieldName.FEAT_STATIC_REAL,
expected_ndim=1,
)
]
if config.num_static_real_features > 0
else []
)
+ [
AsNumpyArray(
field=FieldName.TARGET,
# we expect an extra dim for the multivariate case:
expected_ndim=1 if config.input_size == 1 else 2,
),
# step 3: handle the NaN's by filling in the target with zero
# and return the mask (which is in the observed values)
# true for observed values, false for nan's
# the decoder uses this mask (no loss is incurred for unobserved values)
# see loss_weights inside the xxxForPrediction model
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
# step 4: add temporal features based on freq of the dataset
# these serve as positional encodings
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=time_features_from_frequency_str(freq),
pred_length=config.prediction_length,
),
# step 5: add another temporal feature (just a single number)
# tells the model where in the life the value of the time series is
# sort of running counter
AddAgeFeature(
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_AGE,
pred_length=config.prediction_length,
log_scale=True,
),
# step 6: vertically stack all the temporal features into the key FEAT_TIME
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
+ (
[FieldName.FEAT_DYNAMIC_REAL]
if config.num_dynamic_real_features > 0
else []
),
),
# step 7: rename to match HuggingFace names
RenameFields(
mapping={
FieldName.FEAT_STATIC_CAT: "static_categorical_features",
FieldName.FEAT_STATIC_REAL: "static_real_features",
FieldName.FEAT_TIME: "time_features",
FieldName.TARGET: "values",
FieldName.OBSERVED_VALUES: "observed_mask",
}
),
]
)<jupyter_output><empty_output><jupyter_text>Define `InstanceSplitter`For training/validation/testing we next create an `InstanceSplitter` which is used to sample windows from the dataset (as, remember, we can't pass the entire history of values to the model due to time- and memory constraints).The instance splitter samples random `context_length` sized and subsequent `prediction_length` sized windows from the data, and appends a `past_` or `future_` key to any temporal keys in `time_series_fields` for the respective windows. The instance splitter can be configured into three different modes:1. `mode="train"`: Here we sample the context and prediction length windows randomly from the dataset given to it (the training dataset)2. `mode="validation"`: Here we sample the very last context length window and prediction window from the dataset given to it (for the back-testing or validation likelihood calculations)3. `mode="test"`: Here we sample the very last context length window only (for the prediction use case)<jupyter_code>from gluonts.transform.sampler import InstanceSampler
from typing import Optional
def create_instance_splitter(
config: PretrainedConfig,
mode: str,
train_sampler: Optional[InstanceSampler] = None,
validation_sampler: Optional[InstanceSampler] = None,
) -> Transformation:
assert mode in ["train", "validation", "test"]
instance_sampler = {
"train": train_sampler
or ExpectedNumInstanceSampler(
num_instances=1.0, min_future=config.prediction_length
),
"validation": validation_sampler
or ValidationSplitSampler(min_future=config.prediction_length),
"test": TestSplitSampler(),
}[mode]
return InstanceSplitter(
target_field="values",
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=instance_sampler,
past_length=config.context_length + max(config.lags_sequence),
future_length=config.prediction_length,
time_series_fields=["time_features", "observed_mask"],
)<jupyter_output><empty_output><jupyter_text>Create DataLoadersNext, it's time to create the DataLoaders, which allow us to have batches of (input, output) pairs - or in other words (`past_values`, `future_values`).<jupyter_code>from typing import Iterable
import torch
from gluonts.itertools import Cached, Cyclic
from gluonts.dataset.loader import as_stacked_batches
def create_train_dataloader(
config: PretrainedConfig,
freq,
data,
batch_size: int,
num_batches_per_epoch: int,
shuffle_buffer_length: Optional[int] = None,
cache_data: bool = True,
**kwargs,
) -> Iterable:
PREDICTION_INPUT_NAMES = [
"past_time_features",
"past_values",
"past_observed_mask",
"future_time_features",
]
if config.num_static_categorical_features > 0:
PREDICTION_INPUT_NAMES.append("static_categorical_features")
if config.num_static_real_features > 0:
PREDICTION_INPUT_NAMES.append("static_real_features")
TRAINING_INPUT_NAMES = PREDICTION_INPUT_NAMES + [
"future_values",
"future_observed_mask",
]
transformation = create_transformation(freq, config)
transformed_data = transformation.apply(data, is_train=True)
if cache_data:
transformed_data = Cached(transformed_data)
# we initialize a Training instance
instance_splitter = create_instance_splitter(config, "train")
# the instance splitter will sample a window of
# context length + lags + prediction length (from all the possible transformed time series, 1 in our case)
# randomly from within the target time series and return an iterator.
stream = Cyclic(transformed_data).stream()
training_instances = instance_splitter.apply(stream)
return as_stacked_batches(
training_instances,
batch_size=batch_size,
shuffle_buffer_length=shuffle_buffer_length,
field_names=TRAINING_INPUT_NAMES,
output_type=torch.tensor,
num_batches_per_epoch=num_batches_per_epoch,
)
def create_backtest_dataloader(
config: PretrainedConfig,
freq,
data,
batch_size: int,
**kwargs,
):
PREDICTION_INPUT_NAMES = [
"past_time_features",
"past_values",
"past_observed_mask",
"future_time_features",
]
if config.num_static_categorical_features > 0:
PREDICTION_INPUT_NAMES.append("static_categorical_features")
if config.num_static_real_features > 0:
PREDICTION_INPUT_NAMES.append("static_real_features")
transformation = create_transformation(freq, config)
transformed_data = transformation.apply(data)
# we create a Validation Instance splitter which will sample the very last
# context window seen during training only for the encoder.
instance_sampler = create_instance_splitter(config, "validation")
# we apply the transformations in train mode
testing_instances = instance_sampler.apply(transformed_data, is_train=True)
return as_stacked_batches(
testing_instances,
batch_size=batch_size,
output_type=torch.tensor,
field_names=PREDICTION_INPUT_NAMES,
)
def create_test_dataloader(
config: PretrainedConfig,
freq,
data,
batch_size: int,
**kwargs,
):
PREDICTION_INPUT_NAMES = [
"past_time_features",
"past_values",
"past_observed_mask",
"future_time_features",
]
if config.num_static_categorical_features > 0:
PREDICTION_INPUT_NAMES.append("static_categorical_features")
if config.num_static_real_features > 0:
PREDICTION_INPUT_NAMES.append("static_real_features")
transformation = create_transformation(freq, config)
transformed_data = transformation.apply(data, is_train=False)
# We create a test Instance splitter to sample the very last
# context window from the dataset provided.
instance_sampler = create_instance_splitter(config, "test")
# We apply the transformations in test mode
testing_instances = instance_sampler.apply(transformed_data, is_train=False)
return as_stacked_batches(
testing_instances,
batch_size=batch_size,
output_type=torch.tensor,
field_names=PREDICTION_INPUT_NAMES,
)
train_dataloader = create_train_dataloader(
config=config,
freq=freq,
data=multi_variate_train_dataset,
batch_size=256,
num_batches_per_epoch=100,
num_workers=2,
)
test_dataloader = create_backtest_dataloader(
config=config,
freq=freq,
data=multi_variate_test_dataset,
batch_size=32,
)<jupyter_output><empty_output><jupyter_text>Let's check the first batch:<jupyter_code>batch = next(iter(train_dataloader))
for k, v in batch.items():
print(k, v.shape, v.type())<jupyter_output>past_time_features torch.Size([256, 264, 5]) torch.FloatTensor
past_values torch.Size([256, 264, 862]) torch.FloatTensor
past_observed_mask torch.Size([256, 264, 862]) torch.FloatTensor
future_time_features torch.Size([256, 48, 5]) torch.FloatTensor
future_values torch.Size([256, 48, 862]) torch.FloatTensor
future_observed_mask torch.Size([256, 48, 862]) torch.FloatTensor<jupyter_text>As can be seen, we don't feed `input_ids` and `attention_mask` to the encoder (as would be the case for NLP models), but rather `past_values`, along with `past_observed_mask`, `past_time_features` and `static_real_features`.The decoder inputs consist of `future_values`, `future_observed_mask` and `future_time_features`. The `future_values` can be seen as the equivalent of `decoder_input_ids` in NLP.We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/informertransformers.InformerForPrediction.forward.past_values) for a detailed explanation for each of them. Forward passLet's perform a single forward pass with the batch we just created:<jupyter_code># perform forward pass
outputs = model(
past_values=batch["past_values"],
past_time_features=batch["past_time_features"],
past_observed_mask=batch["past_observed_mask"],
static_categorical_features=batch["static_categorical_features"]
if config.num_static_categorical_features > 0
else None,
static_real_features=batch["static_real_features"]
if config.num_static_real_features > 0
else None,
future_values=batch["future_values"],
future_time_features=batch["future_time_features"],
future_observed_mask=batch["future_observed_mask"],
output_hidden_states=True,
)
print("Loss:", outputs.loss.item())<jupyter_output>Loss: -1071.5718994140625<jupyter_text>Note that the model is returning a loss. This is possible as the decoder automatically shifts the `future_values` one position to the right in order to have the labels. This allows computing a loss between the predicted values and the labels. The loss is the negative log-likelihood of the predicted distribution with respect to the ground truth values and tends to negative infinity.Also note that the decoder uses a causal mask to not look into the future as the values it needs to predict are in the `future_values` tensor. Train the ModelIt's time to train the model! We'll use a standard PyTorch training loop.We will use the 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) library here, which automatically places the model, optimizer and dataloader on the appropriate `device`.<jupyter_code>from accelerate import Accelerator
from torch.optim import AdamW
epochs = 25
loss_history = []
accelerator = Accelerator()
device = accelerator.device
model.to(device)
optimizer = AdamW(model.parameters(), lr=6e-4, betas=(0.9, 0.95), weight_decay=1e-1)
model, optimizer, train_dataloader = accelerator.prepare(
model,
optimizer,
train_dataloader,
)
model.train()
for epoch in range(epochs):
for idx, batch in enumerate(train_dataloader):
optimizer.zero_grad()
outputs = model(
static_categorical_features=batch["static_categorical_features"].to(device)
if config.num_static_categorical_features > 0
else None,
static_real_features=batch["static_real_features"].to(device)
if config.num_static_real_features > 0
else None,
past_time_features=batch["past_time_features"].to(device),
past_values=batch["past_values"].to(device),
future_time_features=batch["future_time_features"].to(device),
future_values=batch["future_values"].to(device),
past_observed_mask=batch["past_observed_mask"].to(device),
future_observed_mask=batch["future_observed_mask"].to(device),
)
loss = outputs.loss
# Backpropagation
accelerator.backward(loss)
optimizer.step()
loss_history.append(loss.item())
if idx % 100 == 0:
print(loss.item())
# view training
loss_history = np.array(loss_history).reshape(-1)
x = range(loss_history.shape[0])
plt.figure(figsize=(10, 5))
plt.plot(x, loss_history, label="train")
plt.title("Loss", fontsize=15)
plt.legend(loc="upper right")
plt.xlabel("iteration")
plt.ylabel("nll")
plt.show()<jupyter_output><empty_output><jupyter_text>InferenceAt inference time, it's recommended to use the `generate()` method for autoregressive generation, similar to NLP models.Forecasting involves getting data from the test instance sampler, which will sample the very last `context_length` sized window of values from each time series in the dataset, and pass it to the model. Note that we pass `future_time_features`, which are known ahead of time, to the decoder.The model will autoregressively sample a certain number of values from the predicted distribution and pass them back to the decoder to return the prediction outputs:<jupyter_code>model.eval()
forecasts_ = []
for batch in test_dataloader:
outputs = model.generate(
static_categorical_features=batch["static_categorical_features"].to(device)
if config.num_static_categorical_features > 0
else None,
static_real_features=batch["static_real_features"].to(device)
if config.num_static_real_features > 0
else None,
past_time_features=batch["past_time_features"].to(device),
past_values=batch["past_values"].to(device),
future_time_features=batch["future_time_features"].to(device),
past_observed_mask=batch["past_observed_mask"].to(device),
)
forecasts_.append(outputs.sequences.cpu().numpy())<jupyter_output><empty_output><jupyter_text>The model outputs a tensor of shape (`batch_size`, `number of samples`, `prediction length`, `input_size`). In this case, we get `100` possible values for the next `48` hours for each of the `862` time series (for each example in the batch which is of size `1` since we only have a single multivariate time series):<jupyter_code>forecasts_[0].shape<jupyter_output><empty_output><jupyter_text>We'll stack them vertically, to get forecasts for all time-series in the test dataset (just in case there are more time series in the test set):<jupyter_code>forecasts = np.vstack(forecasts_)
print(forecasts.shape)<jupyter_output>(1, 100, 48, 862)<jupyter_text>We can evaluate the resulting forecast with respect to the ground truth out of sample values present in the test set. For that, we'll use the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library, which includes the [MASE](https://huggingface.co/spaces/evaluate-metric/mase) and [sMAPE](https://huggingface.co/spaces/evaluate-metric/smape) metrics.We calculate both metrics for each time series variate in the dataset:<jupyter_code>from evaluate import load
from gluonts.time_feature import get_seasonality
mase_metric = load("evaluate-metric/mase")
smape_metric = load("evaluate-metric/smape")
forecast_median = np.median(forecasts, 1).squeeze(0).T
mase_metrics = []
smape_metrics = []
for item_id, ts in enumerate(test_dataset):
training_data = ts["target"][:-prediction_length]
ground_truth = ts["target"][-prediction_length:]
mase = mase_metric.compute(
predictions=forecast_median[item_id],
references=np.array(ground_truth),
training=np.array(training_data),
periodicity=get_seasonality(freq),
)
mase_metrics.append(mase["mase"])
smape = smape_metric.compute(
predictions=forecast_median[item_id],
references=np.array(ground_truth),
)
smape_metrics.append(smape["smape"])
print(f"MASE: {np.mean(mase_metrics)}")
print(f"sMAPE: {np.mean(smape_metrics)}")
plt.scatter(mase_metrics, smape_metrics, alpha=0.2)
plt.xlabel("MASE")
plt.ylabel("sMAPE")
plt.show()<jupyter_output><empty_output><jupyter_text>To plot the prediction for any time series variate with respect the ground truth test data we define the following helper:<jupyter_code>import matplotlib.dates as mdates
def plot(ts_index, mv_index):
fig, ax = plt.subplots()
index = pd.period_range(
start=multi_variate_test_dataset[ts_index][FieldName.START],
periods=len(multi_variate_test_dataset[ts_index][FieldName.TARGET]),
freq=multi_variate_test_dataset[ts_index][FieldName.START].freq,
).to_timestamp()
ax.xaxis.set_minor_locator(mdates.HourLocator())
ax.plot(
index[-2 * prediction_length :],
multi_variate_test_dataset[ts_index]["target"][
mv_index, -2 * prediction_length :
],
label="actual",
)
ax.plot(
index[-prediction_length:],
forecasts[ts_index, ..., mv_index].mean(axis=0),
label="mean",
)
ax.fill_between(
index[-prediction_length:],
forecasts[ts_index, ..., mv_index].mean(0)
- forecasts[ts_index, ..., mv_index].std(axis=0),
forecasts[ts_index, ..., mv_index].mean(0)
+ forecasts[ts_index, ..., mv_index].std(axis=0),
alpha=0.2,
interpolate=True,
label="+/- 1-std",
)
ax.legend()
fig.autofmt_xdate()<jupyter_output><empty_output><jupyter_text>For example:<jupyter_code>plot(0, 344)<jupyter_output><empty_output> | notebooks/examples/multivariate_informer.ipynb/0 | {
"file_path": "notebooks/examples/multivariate_informer.ipynb",
"repo_id": "notebooks",
"token_count": 15125
} | 151 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets as well as other dependencies. Uncomment the following cell and run it. Note the `rouge-score` and `nltk` dependencies - even if you've used 🤗 Transformers before, you may not have these installed!<jupyter_code>#! pip install transformers datasets
#! pip install rouge-score nltk
#! pip install huggingface_hub<jupyter_output><empty_output><jupyter_text>If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then run the following cell and input your token:<jupyter_code>from huggingface_hub import notebook_login
notebook_login()<jupyter_output><empty_output><jupyter_text>Then you need to install Git-LFS and setup Git if you haven't already. Uncomment the following instructions and adapt with your name and email:<jupyter_code># !apt install git-lfs
# !git config --global user.email "[email protected]"
# !git config --global user.name "Your Name"<jupyter_output><empty_output><jupyter_text>Make sure your version of Transformers is at least 4.16.0 since some of the functionality we use was introduced in that version:<jupyter_code>import transformers
print(transformers.__version__)<jupyter_output>4.21.0.dev0<jupyter_text>You can find a script version of this notebook to fine-tune your model in a distributed fashion using multiple GPUs or TPUs [here](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization). We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely.<jupyter_code>from transformers.utils import send_example_telemetry
send_example_telemetry("summarization_notebook", framework="tensorflow")<jupyter_output><empty_output><jupyter_text>Fine-tuning a model on a summarization task In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) model for a summarization task. We will use the [XSum dataset](https://arxiv.org/pdf/1808.08745.pdf) (for extreme summarization) which contains BBC articles accompanied with single-sentence summaries.We will see how to easily load the dataset for this task using 🤗 Datasets and how to fine-tune a model on it using Keras.<jupyter_code>model_checkpoint = "t5-small"<jupyter_output><empty_output><jupyter_text>This notebook is built to run with any model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a sequence-to-sequence version in the Transformers library. Here we pick the [`t5-small`](https://huggingface.co/t5-small) checkpoint. Loading the dataset We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library to download the data and get the metric we need to use for evaluation (to compare our model to the benchmark). This can be easily done with the functions `load_dataset` and `load_metric`.<jupyter_code>from datasets import load_dataset, load_metric
raw_datasets = load_dataset("xsum")
metric = load_metric("rouge")<jupyter_output>Using custom data configuration default
Reusing dataset xsum (/home/matt/.cache/huggingface/datasets/xsum/default/1.2.0/32c23220eadddb1149b16ed2e9430a05293768cfffbdfd151058697d4c11f934)<jupyter_text>The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.htmldatasetdict), which contains one key for the training, validation and test set:<jupyter_code>raw_datasets<jupyter_output><empty_output><jupyter_text>To access an actual element, you need to select a split first, then give an index:<jupyter_code>raw_datasets["train"][0]<jupyter_output><empty_output><jupyter_text>To get a sense of what the data looks like, the following function will show some examples picked randomly in the dataset.<jupyter_code>import datasets
import random
import pandas as pd
from IPython.display import display, HTML
def show_random_elements(dataset, num_examples=5):
assert num_examples <= len(
dataset
), "Can't pick more elements than there are in the dataset."
picks = []
for _ in range(num_examples):
pick = random.randint(0, len(dataset) - 1)
while pick in picks:
pick = random.randint(0, len(dataset) - 1)
picks.append(pick)
df = pd.DataFrame(dataset[picks])
for column, typ in dataset.features.items():
if isinstance(typ, datasets.ClassLabel):
df[column] = df[column].transform(lambda i: typ.names[i])
display(HTML(df.to_html()))
show_random_elements(raw_datasets["train"])<jupyter_output><empty_output><jupyter_text>The metric is an instance of [`datasets.Metric`](https://huggingface.co/docs/datasets/package_reference/main_classes.htmldatasets.Metric):<jupyter_code>metric<jupyter_output><empty_output><jupyter_text>You can call its `compute` method with your predictions and labels, which need to be list of decoded strings:<jupyter_code>fake_preds = ["hello there", "general kenobi"]
fake_labels = ["hello there", "general kenobi"]
metric.compute(predictions=fake_preds, references=fake_labels)<jupyter_output><empty_output><jupyter_text>Preprocessing the data Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers `Tokenizer` which will (as the name indicates) tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary) and put it in a format the model expects, as well as generate the other inputs that the model requires.To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure:- we get a tokenizer that corresponds to the model architecture we want to use,- we download the vocabulary used when pretraining this specific checkpoint.That vocabulary will be cached, so it's not downloaded again the next time we run the cell.<jupyter_code>from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)<jupyter_output>/home/matt/PycharmProjects/transformers/src/transformers/models/t5/tokenization_t5_fast.py:156: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.
For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.
- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.
- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.
- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.
warnings.warn(<jupyter_text>By default, the call above will use one of the fast tokenizers (backed by Rust) from the 🤗 Tokenizers library. You can directly call this tokenizer on one sentence or a pair of sentences:<jupyter_code>tokenizer("Hello, this is a sentence!")<jupyter_output><empty_output><jupyter_text>Depending on the model you selected, you will see different keys in the dictionary returned by the cell above. They don't matter much for what we're doing here (just know they are required by the model we will instantiate later), you can learn more about them in [this tutorial](https://huggingface.co/transformers/preprocessing.html) if you're interested.Instead of one sentence, we can pass along a list of sentences:<jupyter_code>tokenizer(["Hello, this is a sentence!", "This is another sentence."])<jupyter_output><empty_output><jupyter_text>To prepare the targets for our model, we need to tokenize them inside the `as_target_tokenizer` context manager. This will make sure the tokenizer uses the special tokens corresponding to the targets:<jupyter_code>with tokenizer.as_target_tokenizer():
print(tokenizer(["Hello, this is a sentence!", "This is another sentence."]))<jupyter_output>{'input_ids': [[8774, 6, 48, 19, 3, 9, 7142, 55, 1], [100, 19, 430, 7142, 5, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]}<jupyter_text>If you are using one of the five T5 checkpoints we have to prefix the inputs with "summarize:" (the model can also translate and it needs the prefix to know which task it has to perform).<jupyter_code>if model_checkpoint in ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b"]:
prefix = "summarize: "
else:
prefix = ""<jupyter_output><empty_output><jupyter_text>We can then write the function that will preprocess our samples. We just feed them to the `tokenizer` with the argument `truncation=True`. This will ensure that an input longer that what the model selected can handle will be truncated to the maximum length accepted by the model. The padding will be dealt with later on (in a data collator) so we pad examples to the longest length in the batch and not the whole dataset.<jupyter_code>max_input_length = 1024
max_target_length = 128
def preprocess_function(examples):
inputs = [prefix + doc for doc in examples["document"]]
model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(
examples["summary"], max_length=max_target_length, truncation=True
)
model_inputs["labels"] = labels["input_ids"]
return model_inputs<jupyter_output><empty_output><jupyter_text>This function works with one or several examples. In the case of several examples, the tokenizer will return a list of lists for each key:<jupyter_code>preprocess_function(raw_datasets["train"][:2])<jupyter_output><empty_output><jupyter_text>To apply this function on all the pairs of sentences in our dataset, we just use the `map` method of our `dataset` object we created earlier. This will apply the function on all the elements of all the splits in `dataset`, so our training, validation and testing data will be preprocessed in one single command.<jupyter_code>tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)<jupyter_output>Loading cached processed dataset at /home/matt/.cache/huggingface/datasets/xsum/default/1.2.0/32c23220eadddb1149b16ed2e9430a05293768cfffbdfd151058697d4c11f934/cache-6884faaa11a4f3fa.arrow
Loading cached processed dataset at /home/matt/.cache/huggingface/datasets/xsum/default/1.2.0/32c23220eadddb1149b16ed2e9430a05293768cfffbdfd151058697d4c11f934/cache-891fbe6db8d3607d.arrow
Loading cached processed dataset at /home/matt/.cache/huggingface/datasets/xsum/default/1.2.0/32c23220eadddb1149b16ed2e9430a05293768cfffbdfd151058697d4c11f934/cache-e814de7328c711ab.arrow<jupyter_text>Even better, the results are automatically cached by the 🤗 Datasets library to avoid spending time on this step the next time you run your notebook. The 🤗 Datasets library is normally smart enough to detect when the function you pass to map has changed (and thus requires to not use the cache data). For instance, it will properly detect if you change the task in the first cell and rerun the notebook. 🤗 Datasets warns you when it uses cached files, you can pass `load_from_cache_file=False` in the call to `map` to not use the cached files and force the preprocessing to be applied again.Note that we passed `batched=True` to encode the texts by batches together. This is to leverage the full benefit of the fast tokenizer we loaded earlier, which will use multi-threading to treat the texts in a batch concurrently. Fine-tuning the model Now that our data is ready, we can download the pretrained model and fine-tune it. Since our task is sequence-to-sequence (both the input and output are text sequences), we use the `AutoModelForSeq2SeqLM` class. Like with the tokenizer, the `from_pretrained` method will download and cache the model for us.<jupyter_code>from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)<jupyter_output>2022-07-22 18:59:10.209197: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-22 18:59:10.216531: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-22 18:59:10.217844: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-22 18:59:10.219660: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags[...]<jupyter_text>Note that we don't get a warning like in our classification example. This means we used all the weights of the pretrained model and there is no randomly initialized head in this case. Next we set some parameters like the learning rate and the `batch_size`and customize the weight decay. The last two arguments are to setup everything so we can push the model to the [Hub](https://huggingface.co/models) at the end of training. Remove the two of them if you didn't follow the installation steps at the top of the notebook, otherwise you can change the value of push_to_hub_model_id to something you would prefer.<jupyter_code>batch_size = 8
learning_rate = 2e-5
weight_decay = 0.01
num_train_epochs = 1
model_name = model_checkpoint.split("/")[-1]
push_to_hub_model_id = f"{model_name}-finetuned-xsum"<jupyter_output><empty_output><jupyter_text>Then, we need a special kind of data collator, which will not only pad the inputs to the maximum length in the batch, but also the labels. Note that our data collators are designed to work for multiple frameworks, so ensure you set the `return_tensors='np'` argument to get NumPy arrays out - you don't want to accidentally get a load of `torch.Tensor` objects in the middle of your nice TF code! You could also use `return_tensors='tf'` to get TensorFlow tensors, but our TF dataset pipeline actually uses a NumPy loader internally, which is wrapped at the end with a `tf.data.Dataset`. As a result, `np` is usually more reliable and performant when you're using it!We also want to compute `ROUGE` metrics, which will require us to generate text from our model. To speed things up, we can compile our generation loop with XLA. This results in a *huge* speedup - up to 100X! The downside of XLA generation, though, is that it doesn't like variable input shapes, because it needs to run a new compilation for each new input shape! To compensate for that, let's use `pad_to_multiple_of` for the dataset we use for text generation. This will reduce the number of unique input shapes a lot, meaning we can get the benefits of XLA generation with only a few compilations.<jupyter_code>data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors="np")
generation_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors="np", pad_to_multiple_of=128)
tokenized_datasets["train"]<jupyter_output><empty_output><jupyter_text>Next, we convert our datasets to `tf.data.Dataset`, which Keras understands natively. There are two ways to do this - we can use the slightly more low-level [`Dataset.to_tf_dataset()`](https://huggingface.co/docs/datasets/package_reference/main_classesdatasets.Dataset.to_tf_dataset) method, or we can use [`Model.prepare_tf_dataset()`](https://huggingface.co/docs/transformers/main_classes/modeltransformers.TFPreTrainedModel.prepare_tf_dataset). The main difference between these two is that the `Model` method can inspect the model to determine which column names it can use as input, which means you don't need to specify them yourself. Make sure to specify the collator we just created as our `collate_fn`!<jupyter_code>train_dataset = model.prepare_tf_dataset(
tokenized_datasets["train"],
batch_size=batch_size,
shuffle=True,
collate_fn=data_collator,
)
validation_dataset = model.prepare_tf_dataset(
tokenized_datasets["validation"],
batch_size=batch_size,
shuffle=False,
collate_fn=data_collator,
)
generation_dataset = model.prepare_tf_dataset(
tokenized_datasets["validation"],
batch_size=8,
shuffle=False,
collate_fn=generation_data_collator
)<jupyter_output><empty_output><jupyter_text>Now we initialize our loss and optimizer and compile the model. Note that most Transformers models compute loss internally - we can train on this as our loss value simply by not specifying a loss when we `compile()`.<jupyter_code>from transformers import AdamWeightDecay
import tensorflow as tf
optimizer = AdamWeightDecay(learning_rate=learning_rate, weight_decay_rate=weight_decay)
model.compile(optimizer=optimizer)<jupyter_output>No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.<jupyter_text>Now we can train our model. We can also add a few optional callbacks here, which you can remove if they aren't useful to you. In no particular order, these are:- PushToHubCallback will sync up our model with the Hub - this allows us to resume training from other machines, share the model after training is finished, and even test the model's inference quality midway through training!- TensorBoard is a built-in Keras callback that logs TensorBoard metrics.- KerasMetricCallback is a callback for computing advanced metrics. There are a number of common metrics in NLP like ROUGE which are hard to fit into your compiled training loop because they depend on decoding predictions and labels back to strings with the tokenizer, and calling arbitrary Python functions to compute the metric. The KerasMetricCallback will wrap a metric function, outputting metrics as training progresses.If this is the first time you've seen `KerasMetricCallback`, it's worth explaining what exactly is going on here. The callback takes two main arguments - a `metric_fn` and an `eval_dataset`. It then iterates over the `eval_dataset` and collects the model's outputs for each sample, before passing the `list` of predictions and the associated `list` of labels to the user-defined `metric_fn`. If the `predict_with_generate` argument is `True`, then it will call `model.generate()` for each input sample instead of `model.predict()` - this is useful for metrics that expect generated text from the model, like `ROUGE`.This callback allows complex metrics to be computed each epoch that would not function as a standard Keras Metric. Metric values are printed each epoch, and can be used by other callbacks like `TensorBoard` or `EarlyStopping`.<jupyter_code>import numpy as np
import nltk
def metric_fn(eval_predictions):
predictions, labels = eval_predictions
decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True)
for label in labels:
label[label < 0] = tokenizer.pad_token_id # Replace masked label tokens
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Rouge expects a newline after each sentence
decoded_predictions = [
"\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_predictions
]
decoded_labels = [
"\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels
]
result = metric.compute(
predictions=decoded_predictions, references=decoded_labels, use_stemmer=True
)
# Extract a few results
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
# Add mean generated length
prediction_lens = [
np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions
]
result["gen_len"] = np.mean(prediction_lens)
return result<jupyter_output><empty_output><jupyter_text>And now we can try training our model. By default, we only do a single epoch of training here, as the inputs are very long, which means training is quite slow. However, you may wish to experiment with larger pre-trained models and longer training runs if you want to maximize the quality of your summaries.<jupyter_code>from transformers.keras_callbacks import PushToHubCallback, KerasMetricCallback
from tensorflow.keras.callbacks import TensorBoard
tensorboard_callback = TensorBoard(log_dir="./summarization_model_save/logs")
push_to_hub_callback = PushToHubCallback(
output_dir="./summarization_model_save",
tokenizer=tokenizer,
hub_model_id=push_to_hub_model_id,
)
metric_callback = KerasMetricCallback(
metric_fn, eval_dataset=generation_dataset, predict_with_generate=True, use_xla_generation=True
)
callbacks = [metric_callback, tensorboard_callback, push_to_hub_callback]
model.fit(
train_dataset, validation_data=validation_dataset, epochs=1, callbacks=callbacks
)<jupyter_output>/home/matt/PycharmProjects/notebooks/examples/summarization_model_save is already a clone of https://huggingface.co/Rocketknight1/t5-small-finetuned-xsum. Make sure you pull the latest changes with `repo.git_pull()`.<jupyter_text>If you used the callback above, you can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `"your-username/the-name-you-picked"` so for instance:```pythonfrom transformers import TFAutoModelForSeq2SeqLMmodel = TFAutoModelForSeq2SeqLM.from_pretrained("your-username/my-awesome-model")``` Inference Now we've trained our model, let's see how we could load it and use it to summarize text in future! First, let's load it from the hub. This means we can resume the code from here without needing to rerun everything above every time.<jupyter_code>from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
# You can of course substitute your own username and model here if you've trained and uploaded it!
model_name = 'Rocketknight1/t5-small-finetuned-xsum'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)<jupyter_output><empty_output><jupyter_text>Now let's try tokenizing a document from the training set. Don't forget to add 'summarize:' at the start if you're using a `T5` model.<jupyter_code>document = 'The full cost of damage in Newton Stewart, one of the areas worst affected, is still being assessed.\nRepair work is ongoing in Hawick and many roads in Peeblesshire remain badly affected by standing water.\nTrains on the west coast mainline face disruption due to damage at the Lamington Viaduct.\nMany businesses and householders were affected by flooding in Newton Stewart after the River Cree overflowed into the town.\nFirst Minister Nicola Sturgeon visited the area to inspect the damage.\nThe waters breached a retaining wall, flooding many commercial properties on Victoria Street - the main shopping thoroughfare.\nJeanette Tate, who owns the Cinnamon Cafe which was badly affected, said she could not fault the multi-agency response once the flood hit.\nHowever, she said more preventative work could have been carried out to ensure the retaining wall did not fail.\n"It is difficult but I do think there is so much publicity for Dumfries and the Nith - and I totally appreciate that - but it is almost like we\'re neglected or forgotten," she said.\n"That may not be true but it is perhaps my perspective over the last few days.\n"Why were you not ready to help us a bit more when the warning and the alarm alerts had gone out?"\nMeanwhile, a flood alert remains in place across the Borders because of the constant rain.\nPeebles was badly hit by problems, sparking calls to introduce more defences in the area.\nScottish Borders Council has put a list on its website of the roads worst affected and drivers have been urged not to ignore closure signs.\nThe Labour Party\'s deputy Scottish leader Alex Rowley was in Hawick on Monday to see the situation first hand.\nHe said it was important to get the flood protection plan right but backed calls to speed up the process.\n"I was quite taken aback by the amount of damage that has been done," he said.\n"Obviously it is heart-breaking for people who have been forced out of their homes and the impact on businesses."\nHe said it was important that "immediate steps" were taken to protect the areas most vulnerable and a clear timetable put in place for flood prevention plans.\nHave you been affected by flooding in Dumfries and Galloway or the Borders? Tell us about your experience of the situation and how it was handled. Email us on [email protected] or [email protected].'
if 't5' in model_name:
document = "summarize: " + document
tokenized = tokenizer([document], return_tensors='np')
out = model.generate(**tokenized, max_length=128)
with tokenizer.as_target_tokenizer():
print(tokenizer.decode(out[0]))<jupyter_output><pad> A flood warning has been issued to the people affected by flooding in Dumfries and the Nith, the Scottish Borders Council has said.</s><jupyter_text>Not bad for a single epoch of training! Of course, the flood warning isn't much use to them after they've been flooded, but the summary correctly identified flooding in Dumfries and the Nith as the key event. Using XLA in inference If you just want to generate a few summaries, the code above is all you need. However, generation can be **much** faster if you use XLA, and if you want to generate data in bulk, you should probably use it! If you're using XLA, though, remember that you'll need to do a new XLA compilation for every input size you pass to the model. This means that you should keep your batch size constant, and consider padding inputs to the same length, or using `pad_to_multiple_of` in your tokenizer to reduce the number of different input shapes you pass. Let's show an example of that:<jupyter_code>import tensorflow as tf
@tf.function(jit_compile=True)
def generate(inputs):
return model.generate(**inputs, max_length=128)
tokenized_data = tokenizer([document], return_tensors="np", pad_to_multiple_of=128)
out = generate(tokenized_data)
with tokenizer.as_target_tokenizer():
print(tokenizer.decode(out[0]))<jupyter_output><pad> A flood warning has been issued to the people affected by flooding in Dumfries and the Nith, the Scottish Borders Council has said.</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><jupyter_text>When using XLA generation, you'll notice that the first call to generate with a new input shape takes a long time because XLA has to compile your function, but subsequent calls are extremely quick. Also, XLA always generates to the maximum length, which can lead to a lot of padding tokens in your output! These are easy to remove, however:<jupyter_code>with tokenizer.as_target_tokenizer():
print(tokenizer.decode(out[0], skip_special_tokens=True))<jupyter_output>A flood warning has been issued to the people affected by flooding in Dumfries and the Nith, the Scottish Borders Council has said.<jupyter_text>Pipeline API The pipeline API offers a convenient shortcut for all of this, but doesn't (yet!) support XLA generation:<jupyter_code>from transformers import pipeline
summarizer = pipeline('text2text-generation', model_name, framework="tf")<jupyter_output>All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.
All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at Rocketknight1/t5-small-finetuned-xsum.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.<jupyter_text>Remember that if we're using a T5 model then we appended "summarize: " to the start of our input above. Don't forget to do that when you're getting summaries for new texts!<jupyter_code>summarizer(document, max_length=128)<jupyter_output>Token indices sequence length is longer than the specified maximum sequence length for this model (541 > 512). Running this sequence through the model will result in indexing errors
2022-07-25 15:05:51.802359: I tensorflow/compiler/xla/service/service.cc:170] XLA service 0x563f5be5ff70 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2022-07-25 15:05:51.802390: I tensorflow/compiler/xla/service/service.cc:178] StreamExecutor device (0): Host, Default Version | notebooks/examples/summarization-tf.ipynb/0 | {
"file_path": "notebooks/examples/summarization-tf.ipynb",
"repo_id": "notebooks",
"token_count": 8798
} | 152 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.<jupyter_code>#! pip install datasets transformers[sentencepiece] sacrebleu<jupyter_output><empty_output><jupyter_text>If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then execute the following cell and input your username and password:<jupyter_code>from huggingface_hub import notebook_login
notebook_login()<jupyter_output><empty_output><jupyter_text>Then you need to install Git-LFS. Uncomment the following instructions:<jupyter_code># !apt install git-lfs<jupyter_output><empty_output><jupyter_text>Make sure your version of Transformers is at least 4.11.0 since the functionality was introduced in that version:<jupyter_code>import transformers
print(transformers.__version__)<jupyter_output><empty_output><jupyter_text>You can find a script version of this notebook to fine-tune your model in a distributed fashion using multiple GPUs or TPUs [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq). We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely.<jupyter_code>from transformers.utils import send_example_telemetry
send_example_telemetry("translation_notebook", framework="pytorch")<jupyter_output><empty_output><jupyter_text>Fine-tuning a model on a translation task In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) model for a translation task. We will use the [WMT dataset](http://www.statmt.org/wmt16/), a machine translation dataset composed from a collection of various sources, including news commentaries and parliament proceedings.We will see how to easily load the dataset for this task using 🤗 Datasets and how to fine-tune a model on it using the `Trainer` API.<jupyter_code>model_checkpoint = "Helsinki-NLP/opus-mt-en-ro"<jupyter_output><empty_output><jupyter_text>This notebook is built to run with any model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a sequence-to-sequence version in the Transformers library. Here we picked the [`Helsinki-NLP/opus-mt-en-ro`](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) checkpoint. Loading the dataset We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library to download the data and get the metric we need to use for evaluation (to compare our model to the benchmark). This can be easily done with the functions `load_dataset` and `load_metric`. We use the English/Romanian part of the WMT dataset here.<jupyter_code>from datasets import load_dataset, load_metric
raw_datasets = load_dataset("wmt16", "ro-en")
metric = load_metric("sacrebleu")<jupyter_output>Reusing dataset wmt16 (/home/sgugger/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f)<jupyter_text>The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.htmldatasetdict), which contains one key for the training, validation and test set:<jupyter_code>raw_datasets<jupyter_output><empty_output><jupyter_text>To access an actual element, you need to select a split first, then give an index:<jupyter_code>raw_datasets["train"][0]<jupyter_output><empty_output><jupyter_text>To get a sense of what the data looks like, the following function will show some examples picked randomly in the dataset.<jupyter_code>import datasets
import random
import pandas as pd
from IPython.display import display, HTML
def show_random_elements(dataset, num_examples=5):
assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset."
picks = []
for _ in range(num_examples):
pick = random.randint(0, len(dataset)-1)
while pick in picks:
pick = random.randint(0, len(dataset)-1)
picks.append(pick)
df = pd.DataFrame(dataset[picks])
for column, typ in dataset.features.items():
if isinstance(typ, datasets.ClassLabel):
df[column] = df[column].transform(lambda i: typ.names[i])
display(HTML(df.to_html()))
show_random_elements(raw_datasets["train"])<jupyter_output><empty_output><jupyter_text>The metric is an instance of [`datasets.Metric`](https://huggingface.co/docs/datasets/package_reference/main_classes.htmldatasets.Metric):<jupyter_code>metric<jupyter_output><empty_output><jupyter_text>You can call its `compute` method with your predictions and labels, which need to be list of decoded strings (list of list for the labels):<jupyter_code>fake_preds = ["hello there", "general kenobi"]
fake_labels = [["hello there"], ["general kenobi"]]
metric.compute(predictions=fake_preds, references=fake_labels)<jupyter_output><empty_output><jupyter_text>Preprocessing the data Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers `Tokenizer` which will (as the name indicates) tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary) and put it in a format the model expects, as well as generate the other inputs that model requires.To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure:- we get a tokenizer that corresponds to the model architecture we want to use,- we download the vocabulary used when pretraining this specific checkpoint.That vocabulary will be cached, so it's not downloaded again the next time we run the cell.<jupyter_code>from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)<jupyter_output><empty_output><jupyter_text>For the mBART tokenizer (like we have here), we need to set the source and target languages (so the texts are preprocessed properly). You can check the language codes [here](https://huggingface.co/facebook/mbart-large-cc25) if you are using this notebook on a different pairs of languages.<jupyter_code>if "mbart" in model_checkpoint:
tokenizer.src_lang = "en-XX"
tokenizer.tgt_lang = "ro-RO"<jupyter_output><empty_output><jupyter_text>By default, the call above will use one of the fast tokenizers (backed by Rust) from the 🤗 Tokenizers library. You can directly call this tokenizer on one sentence or a pair of sentences:<jupyter_code>tokenizer("Hello, this one sentence!")<jupyter_output><empty_output><jupyter_text>Depending on the model you selected, you will see different keys in the dictionary returned by the cell above. They don't matter much for what we're doing here (just know they are required by the model we will instantiate later), you can learn more about them in [this tutorial](https://huggingface.co/transformers/preprocessing.html) if you're interested.Instead of one sentence, we can pass along a list of sentences:<jupyter_code>tokenizer(["Hello, this one sentence!", "This is another sentence."])<jupyter_output><empty_output><jupyter_text>To prepare the targets for our model, we need to tokenize them inside the `as_target_tokenizer` context manager. This will make sure the tokenizer uses the special tokens corresponding to the targets:<jupyter_code>with tokenizer.as_target_tokenizer():
print(tokenizer(["Hello, this one sentence!", "This is another sentence."]))<jupyter_output>{'input_ids': [[10334, 1204, 3, 15, 8915, 27, 452, 59, 29579, 581, 23, 0], [235, 1705, 11, 32, 8, 1205, 5305, 59, 29579, 581, 2, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}<jupyter_text>If you are using one of the five T5 checkpoints that require a special prefix to put before the inputs, you should adapt the following cell.<jupyter_code>if model_checkpoint in ["t5-small", "t5-base", "t5-larg", "t5-3b", "t5-11b"]:
prefix = "translate English to Romanian: "
else:
prefix = ""<jupyter_output><empty_output><jupyter_text>We can then write the function that will preprocess our samples. We just feed them to the `tokenizer` with the argument `truncation=True`. This will ensure that an input longer that what the model selected can handle will be truncated to the maximum length accepted by the model. The padding will be dealt with later on (in a data collator) so we pad examples to the longest length in the batch and not the whole dataset.<jupyter_code>max_input_length = 128
max_target_length = 128
source_lang = "en"
target_lang = "ro"
def preprocess_function(examples):
inputs = [prefix + ex[source_lang] for ex in examples["translation"]]
targets = [ex[target_lang] for ex in examples["translation"]]
model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs<jupyter_output><empty_output><jupyter_text>This function works with one or several examples. In the case of several examples, the tokenizer will return a list of lists for each key:<jupyter_code>preprocess_function(raw_datasets['train'][:2])<jupyter_output><empty_output><jupyter_text>To apply this function on all the pairs of sentences in our dataset, we just use the `map` method of our `dataset` object we created earlier. This will apply the function on all the elements of all the splits in `dataset`, so our training, validation and testing data will be preprocessed in one single command.<jupyter_code>tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)<jupyter_output>Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-126b7925258542cf.arrow
Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-995e36d1e1745505.arrow
Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f/cache-313c89c543a3c175.arrow<jupyter_text>Even better, the results are automatically cached by the 🤗 Datasets library to avoid spending time on this step the next time you run your notebook. The 🤗 Datasets library is normally smart enough to detect when the function you pass to map has changed (and thus requires to not use the cache data). For instance, it will properly detect if you change the task in the first cell and rerun the notebook. 🤗 Datasets warns you when it uses cached files, you can pass `load_from_cache_file=False` in the call to `map` to not use the cached files and force the preprocessing to be applied again.Note that we passed `batched=True` to encode the texts by batches together. This is to leverage the full benefit of the fast tokenizer we loaded earlier, which will use multi-threading to treat the texts in a batch concurrently. Fine-tuning the model Now that our data is ready, we can download the pretrained model and fine-tune it. Since our task is of the sequence-to-sequence kind, we use the `AutoModelForSeq2SeqLM` class. Like with the tokenizer, the `from_pretrained` method will download and cache the model for us.<jupyter_code>from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)<jupyter_output><empty_output><jupyter_text>Note that we don't get a warning like in our classification example. This means we used all the weights of the pretrained model and there is no randomly initialized head in this case. To instantiate a `Seq2SeqTrainer`, we will need to define three more things. The most important is the [`Seq2SeqTrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.htmltransformers.Seq2SeqTrainingArguments), which is a class that contains all the attributes to customize the training. It requires one folder name, which will be used to save the checkpoints of the model, and all other arguments are optional:<jupyter_code>batch_size = 16
model_name = model_checkpoint.split("/")[-1]
args = Seq2SeqTrainingArguments(
f"{model_name}-finetuned-{source_lang}-to-{target_lang}",
evaluation_strategy = "epoch",
learning_rate=2e-5,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=1,
predict_with_generate=True,
fp16=True,
push_to_hub=True,
)<jupyter_output><empty_output><jupyter_text>Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, use the `batch_size` defined at the top of the cell and customize the weight decay. Since the `Seq2SeqTrainer` will save the model regularly and our dataset is quite large, we tell it to make three saves maximum. Lastly, we use the `predict_with_generate` option (to properly generate summaries) and activate mixed precision training (to go a bit faster).The last argument to setup everything so we can push the model to the [Hub](https://huggingface.co/models) regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally in a name that is different than the name of the repository it will be pushed, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `"sgugger/marian-finetuned-en-to-ro"` or `"huggingface/marian-finetuned-en-to-ro"`).Then, we need a special kind of data collator, which will not only pad the inputs to the maximum length in the batch, but also the labels:<jupyter_code>data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)<jupyter_output><empty_output><jupyter_text>The last thing to define for our `Seq2SeqTrainer` is how to compute the metrics from the predictions. We need to define a function for this, which will just use the `metric` we loaded earlier, and we have to do a bit of pre-processing to decode the predictions into texts:<jupyter_code>import numpy as np
def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [[label.strip()] for label in labels]
return preds, labels
def compute_metrics(eval_preds):
preds, labels = eval_preds
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
# Replace -100 in the labels as we can't decode them.
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Some simple post-processing
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
result = {"bleu": result["score"]}
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
result["gen_len"] = np.mean(prediction_lens)
result = {k: round(v, 4) for k, v in result.items()}
return result<jupyter_output><empty_output><jupyter_text>Then we just need to pass all of this along with our datasets to the `Seq2SeqTrainer`:<jupyter_code>trainer = Seq2SeqTrainer(
model,
args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics
)<jupyter_output><empty_output><jupyter_text>We can now finetune our model by just calling the `train` method:<jupyter_code>trainer.train()<jupyter_output><empty_output><jupyter_text>You can now upload the result of the training to the Hub, just execute this instruction:<jupyter_code>trainer.push_to_hub()<jupyter_output><empty_output> | notebooks/examples/translation.ipynb/0 | {
"file_path": "notebooks/examples/translation.ipynb",
"repo_id": "notebooks",
"token_count": 5285
} | 153 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - training with custom metrics Binary Classification with `Trainer` and `imdb` dataset In this demo, we extend the basic classification demo by adding **metrics definition** to capture and visualize training metrics.The documentation of the SageMaker metrics capture feature can be seen at https://docs.aws.amazon.com/sagemaker/latest/dg/training-metrics.htmlWe additionally use **SageMaker Checkpointing** to send intermediary checkpoint data to S3 uncompressed in parallel to the training happening https://docs.aws.amazon.com/sagemaker/latest/dg/model-checkpoints.htmlSageMaker Checkpointing is supported by HF Trainer after Transformers 4.4.0+ Import libraries and set environment_*Note:* we only install the required libraries from Hugging Face and AWS. You also need PyTorch or Tensorflow, if you haven´t it installed_<jupyter_code>!pip install "sagemaker>=2.140.0" "transformers==4.26.1" "datasets[s3]==2.10.1" --upgrade<jupyter_output><empty_output><jupyter_text>Development environment<jupyter_code>import sagemaker.huggingface<jupyter_output><empty_output><jupyter_text>Permissions_If you are going to use Sagemaker in a local environment. You need access to an IAM Role with the required permissions for Sagemaker. You can find [here](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html) more about it._<jupyter_code>import sagemaker
import boto3
sess = sagemaker.Session()
# sagemaker session bucket -> used for uploading data, models and logs
# sagemaker will automatically create this bucket if it not exists
sagemaker_session_bucket=None
if sagemaker_session_bucket is None and sess is not None:
# set to default bucket if a bucket name is not given
sagemaker_session_bucket = sess.default_bucket()
try:
role = sagemaker.get_execution_role()
except ValueError:
iam = boto3.client('iam')
role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)
print(f"sagemaker role arn: {role}")
print(f"sagemaker bucket: {sess.default_bucket()}")
print(f"sagemaker session region: {sess.boto_region_name}")<jupyter_output><empty_output><jupyter_text>PreprocessingWe are using the `datasets` library to download and preprocess the `imdb` dataset. After preprocessing, the dataset will be uploaded to our `sagemaker_session_bucket` to be used within our training job. The [imdb](http://ai.stanford.edu/~amaas/data/sentiment/) dataset consists of 25000 training and 25000 testing highly polar movie reviews. Tokenization<jupyter_code>from datasets import load_dataset
from transformers import AutoTokenizer
# tokenizer used in preprocessing
tokenizer_name = 'distilbert-base-uncased'
# dataset used
dataset_name = 'imdb'
# s3 key prefix for the data
s3_prefix = 'samples/datasets/imdb'
# load dataset
dataset = load_dataset(dataset_name)
# download tokenizer
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
# tokenizer helper function
def tokenize(batch):
return tokenizer(batch['text'], padding='max_length', truncation=True)
# load dataset
train_dataset, test_dataset = load_dataset('imdb', split=['train', 'test'])
test_dataset = test_dataset.shuffle().select(range(10000)) # smaller the size for test dataset to 10k
# tokenize dataset
train_dataset = train_dataset.map(tokenize, batched=True)
test_dataset = test_dataset.map(tokenize, batched=True)
# set format for pytorch
train_dataset = train_dataset.rename_column("label", "labels")
train_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels'])
test_dataset = test_dataset.rename_column("label", "labels")
test_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels'])<jupyter_output><empty_output><jupyter_text>Uploading data to Amazon S3After we processed the `datasets` we are going to use the new `FileSystem` [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload our dataset to S3.<jupyter_code># save train_dataset to s3
training_input_path = f's3://{sess.default_bucket()}/{s3_prefix}/train'
train_dataset.save_to_disk(training_input_path)
# save test_dataset to s3
test_input_path = f's3://{sess.default_bucket()}/{s3_prefix}/test'
test_dataset.save_to_disk(test_input_path)<jupyter_output><empty_output><jupyter_text>Launching a Training Job with custom metrics<jupyter_code>from sagemaker.huggingface import HuggingFace
# hyperparameters, which will be passed into the training job
hyperparameters={
'epochs': 3,
'train_batch_size': 32,
'checkpoints': '/opt/ml/checkpoints/',
'model_name':'distilbert-base-uncased'}<jupyter_output><empty_output><jupyter_text>We create a metric_definition dictionary that contains regex-based definitions that will be used to parse the job logs and extract metrics<jupyter_code>metric_definitions=[
{'Name': 'loss', 'Regex': "'loss': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'learning_rate', 'Regex': "'learning_rate': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'eval_loss', 'Regex': "'eval_loss': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'eval_accuracy', 'Regex': "'eval_accuracy': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'eval_f1', 'Regex': "'eval_f1': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'eval_precision', 'Regex': "'eval_precision': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'eval_recall', 'Regex': "'eval_recall': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'eval_runtime', 'Regex': "'eval_runtime': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'eval_samples_per_second', 'Regex': "'eval_samples_per_second': ([0-9]+(.|e\-)[0-9]+),?"},
{'Name': 'epoch', 'Regex': "'epoch': ([0-9]+(.|e\-)[0-9]+),?"}]
huggingface_estimator = HuggingFace(
entry_point='train.py',
source_dir='./scripts',
instance_type='ml.p3.2xlarge',
instance_count=1,
transformers_version='4.26',
pytorch_version='1.13',
py_version='py39',
role=role,
hyperparameters=hyperparameters,
metric_definitions=metric_definitions)
# starting the train job with our uploaded datasets as input
huggingface_estimator.fit({'train': training_input_path, 'test': test_input_path})<jupyter_output><empty_output><jupyter_text>Accessing Training MetricsThe training job doesn't emit metrics immediately. For example, it first needs to provision a training instance, download the training image, download the data. Additionally in this demo the first evaluation logs come after 500 steps (default in the Hugging Face trainer https://huggingface.co/transformers/main_classes/trainer.htmltransformers.TrainingArguments).Hence, **run the below section 15 to 20 minutes after launching the training, otherwise it may not have available metrics yet and return an error**Note that you can also copy this code and run it from a different place (as long as connected to the cloud and authorized to use the API), by specifiying the exact training job name in the `TrainingJobAnalytics` API call.)<jupyter_code>from sagemaker import TrainingJobAnalytics
# Captured metrics can be accessed as a Pandas dataframe
df = TrainingJobAnalytics(training_job_name=huggingface_estimator.latest_training_job.name).dataframe()
df.head(10)<jupyter_output>INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole<jupyter_text>We can also plot some of the metrics collected*Note: the plot below were generated at the end of the training job, with metrics available for all training duration*<jupyter_code>!pip install seaborn
from matplotlib import pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = [15,5]
evals = df[df.metric_name.isin(['eval_accuracy','eval_precision'])]
losses = df[df.metric_name.isin(['loss', 'eval_loss'])]
sns.lineplot(
x='timestamp',
y='value',
data=evals,
hue='metric_name',
palette=['blue', 'purple'])
ax2 = plt.twinx()
sns.lineplot(
x='timestamp',
y='value',
data=losses,
hue='metric_name',
palette=['orange', 'red'],
ax=ax2)<jupyter_output><empty_output><jupyter_text>Deploying the endpointTo deploy our endpoint, we call `deploy()` on our HuggingFace estimator object, passing in our desired number of instances and instance type.<jupyter_code>predictor = huggingface_estimator.deploy(1,"ml.g4dn.xlarge")<jupyter_output><empty_output><jupyter_text>Then, we use the returned predictor object to call the endpoint.<jupyter_code>sentiment_input= {"inputs":"I love using the new Inference DLC."}
predictor.predict(sentiment_input)<jupyter_output><empty_output><jupyter_text>Finally, we delete the endpoint again.<jupyter_code>predictor.delete_model()
predictor.delete_endpoint()<jupyter_output><empty_output> | notebooks/sagemaker/06_sagemaker_metrics/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/06_sagemaker_metrics/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 3000
} | 154 |
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