README.md

---
base_model: CompVis/stable-diffusion-v1-2
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# Text-to-image finetuning - Shreyas21032003/sd_ffhq_shorter_captions_model_demo

This pipeline was finetuned from **CompVis/stable-diffusion-v1-2** on the **irodkin/ffhq_with_llava_shorter_captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None: 



## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("Shreyas21032003/sd_ffhq_shorter_captions_model_demo", torch_dtype=torch.float16)
# Check if validation_prompts is None and assign a default value
if args.validation_prompts is None:
    args.validation_prompts = ["A person sitting in a park under a tree"]
"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 1
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16



## Intended uses & limitations

#### How to use

```python
# TODO: add an example code snippet for running this diffusion pipeline
```

#### Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

## Training details

[TODO: describe the data used to train the model]