Delete roop-unleashed-main

roop-unleashed-main/.flake8

@@ -1,3 +0,0 @@
-[flake8]
-select = E3, E4, F
-per-file-ignores = roop/core.py:E402

roop-unleashed-main/.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,37 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Details**
-What OS are you using?
-- [ ] Linux
-- [ ] Linux in WSL
-- [ ] Windows 
-- [ ] Mac
-
-Are you using a GPU?
-- [ ] No. CPU FTW
-- [ ] NVIDIA
-- [ ] AMD
-- [ ] Intel
-- [ ] Mac
-
-**Which version of roop unleashed are you using?**
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.

roop-unleashed-main/.github/workflows/stale.yml

@@ -1,29 +0,0 @@
-# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
-#
-# You can adjust the behavior by modifying this file.
-# For more information, see:
-# https://github.com/actions/stale
-name: Mark stale issues and pull requests
-
-on:
-  schedule:
-  - cron: '32 0 * * *'
-
-jobs:
-  stale:
-
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-
-    steps:
-    - uses: actions/stale@v5
-      with:
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        stale-issue-message: 'This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
-        stale-pr-message: 'This PR is stale because it has been open 45 days with no activity. Remove stale label or comment or this will be closed in 10 days.'
-        close-issue-message: 'This issue was closed because it has been stalled for 5 days with no activity.'
-        days-before-stale: 30
-        days-before-close: 5
-        days-before-pr-close: -1

roop-unleashed-main/.gitignore

@@ -1,15 +0,0 @@
-.vs
-.idea
-models
-temp
-__pycache__
-*.pth
-/start.bat
-/env
-.vscode
-output
-temp
-config.yaml
-run.bat
-venv
-start.sh

roop-unleashed-main/Dockerfile

@@ -1,18 +0,0 @@
-FROM python:3.11
-
-# making app folder
-WORKDIR /app
-
-# copying files
-COPY . .
-
-# installing requirements
-RUN apt-get update 
-RUN apt-get install ffmpeg -y
-RUN pip install --upgrade pip 
-RUN pip install -r ./requirements.txt 
-
-# launching gradio app 
-ENV GRADIO_SERVER_NAME="0.0.0.0"
-EXPOSE 7860
-ENTRYPOINT python ./run.py

roop-unleashed-main/LICENSE

@@ -1,661 +0,0 @@
-                    GNU AFFERO GENERAL PUBLIC LICENSE
-                       Version 3, 19 November 2007
-
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- Everyone is permitted to copy and distribute verbatim copies
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-  13. Remote Network Interaction; Use with the GNU General Public License.
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-  Notwithstanding any other provision of this License, if you modify the
-Program, your modified version must prominently offer all users
-interacting with it remotely through a computer network (if your version
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-  14. Revised Versions of this License.
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-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
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-
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-    it under the terms of the GNU Affero General Public License as published
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-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
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-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU AGPL, see
-<https://www.gnu.org/licenses/>.

roop-unleashed-main/README.md

@@ -1,253 +0,0 @@
-# roop-unleashed
-
-[Changelog](#changelog) • [Usage](#usage) • [Wiki](https://github.com/C0untFloyd/roop-unleashed/wiki)
-
-
-Uncensored Deepfakes for images and videos without training and an easy-to-use GUI.
-
-
-![Screen](https://github.com/C0untFloyd/roop-unleashed/assets/131583554/6ee6860d-efbe-4337-8c62-a67598863637)
-
-### Features
-
-- Platform-independant Browser GUI
-- Selection of multiple input/output faces in one go
-- Many different swapping modes, first detected, face selections, by gender
-- Batch processing of images/videos
-- Masking of face occluders using text prompts or automatically
-- Optional Face Upscaler/Restoration using different enhancers
-- Preview swapping from different video frames
-- Live Fake Cam using your webcam
-- Extras Tab for cutting videos etc.
-- Settings - storing configuration for next session
-- Theme Support
-
-and lots more...
-
-
-## Disclaimer
-
-This project is for technical and academic use only.
-Users of this software are expected to use this software responsibly while abiding the local law. If a face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers of this software will not be responsible for actions of end-users.
-**Please do not apply it to illegal and unethical scenarios.**
-
-In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability
-
-### Installation
-
-Please refer to the [wiki](https://github.com/C0untFloyd/roop-unleashed/wiki).
-
-#### macOS Installation
-Simply run the following command. It will check and install all dependencies if necessary.
-
-`/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/PJF16/roop-unleashed/master/installer/macOSinstaller.sh)"`
-
-
-
-### Usage
-
-- Windows: run the `windows_run.bat` from the Installer.
-- Linux: `python run.py`
-- macOS: `sh runMacOS.sh`
-- Dockerfile:
-  ```shell
-  docker build -t roop-unleashed . && docker run -t \
-    -p 7860:7860 \
-    -v ./config.yaml:/app/config.yaml \
-    -v ./models:/app/models \
-    -v ./temp:/app/temp \
-    -v ./output:/app/output \
-    roop-unleashed
-  ```
-
-<a target="_blank" href="https://colab.research.google.com/github/C0untFloyd/roop-unleashed/blob/main/roop-unleashed.ipynb">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
-  
-
-Additional commandline arguments are currently unsupported and settings should be done via the UI.
-
-> Note: When you run this program for the first time, it will download some models roughly ~2Gb in size.
-
-
-
-
-### Changelog
-
-**31.12.2024** v4.4.0 Hotfix
-
-Bugfix: Updated Colab to use present Cuda Drivers
-Bugfix: Live-Cam not working because of new face swapper
-Set default swapping model back to Insightface
-
-Happy New Year!
-
-
-**30.12.2024** v4.4.0
-
-- Added random face selection mode
-- Added alternative face swapping model with 128px & 256 px output ([ReSwapper](https://github.com/somanchiu/ReSwapper/tree/main))
-- Video repair added to Extras Tab
-- Updated most packages to newer versions. CUDA >= 12.4 now required!
-- Several minor bugfixes and QoL Changes
-
-
-**28.9.2024** v4.3.1
-
-- Bugfix: Several possible memory leaks
-- Added different output modes, e.g. to virtual cam stream
-- New swapping mode "All input faces"
-- Average total fps displayed and setting for autorun
-
-
-**16.9.2024** v4.2.8
-
-- Bugfix: Starting roop-unleashed without NVIDIA gpu but cuda option enabled
-- Bugfix: Target Faces couldn't be moved left/right
-- Bugfix: Enhancement and upscaling working again in virtual cam
-- Corrupt videos caught when adding to target files, displaying warning msg
-- Source Files Component cleared after face detection to release temp files
-- Added masking and mouth restore options to virtual cam
-
-
-**9.9.2024** v4.2.3
-
-- Hotfix for gradio pydantic issue with fastapi
-- Upgraded to Gradio 4.43 hoping it will fix remaining issues
-- Added new action when no face detected -> use last swapped
-- Specified image format for image controls - opening new tabs on preview images possible again!
-- Hardcoded image output format for livecam to jpeg - might be faster than previous webp
-- Chain events to be only executed if previous was a success
-
-
-**5.9.2024** v4.2.0
-
-- Added ability to move input & target faces order
-- New CLI Arguments override settings
-- Small UI changes to faceswapping tab
-- Added mask option and code for restoration of original mouth area
-- Updated gradio to v4.42.0
-- Added CLI Arguments --server_share and --cuda_device_id
-- Added webp image support
-
-
-**15.07.2024** v4.1.1
-
-- Bugfix: Post-processing after swapping
-
-
-**14.07.2024** v4.1.0
-
-- Added subsample upscaling to increase swap resolution
-- Upgraded gradio
-
-
-**12.05.2024** v4.0.0
-
-- Bugfix: Unnecessary init every frame in live-cam
-- Bugfix: Installer downloading insightface package each run
-- Added xseg masking to live-cam
-- Added realesrganx2 to frame processors
-- Upgraded some requirements
-- Added subtypes and different model support to frame processors
-- Allow frame processors to change resolutions of videos
-- Different OpenCV Cap for MacOS Virtual Cam
-- Added complete frame processing to extras tab
-- Colorize, upscale and misc filters added
-
-
-**22.04.2024** v3.9.0
-
-- Bugfix: Face detection bounding box corrupt values at weird angles
-- Rewrote mask previewing to work with every model
-- Switching mask engines toggles text interactivity
-- Clearing target files, resets face selection dropdown
-- Massive rewrite of swapping architecture, needed for xseg implementation
-- Added DFL Xseg Support for partial face occlusion
-- Face masking only runs when there is a face detected
-- Removed unnecessary toggle checkbox for text masking
-
-
-**22.03.2024** v3.6.5
-
-- Bugfix: Installer pulling latest update on first installation
-- Bugfix: Regression issue, blurring/erosion missing from face swap
-- Exposed erosion and blur amounts to UI
-- Using same values for manual masking too
-
-
-**20.03.2024** v3.6.3
-
-- Bugfix: Workaround for Gradio Slider Change Bug
-- Bugfix: CSS Styling to fix Gradio Image Height Bug
-- Made face swapping mask offsets resolution independant
-- Show offset mask as overlay
-- Changed layout for masking
-
-
-**18.03.2024** v3.6.0
-
-- Updated to Gradio 4.21.0 - requiring many changes under the hood
-- New manual masking (draw the mask yourself)
-- Extras Tab, streamlined cutting/joining videos
-- Re-added face selection by gender (on-demand loading, default turned off)
-- Removed unnecessary activate live-cam option
-- Added time info to preview frame and changed frame slider event to allow faster changes
-
-
-**10.03.2024** v3.5.5
-
-- Bugfix: Installer Path Env
-- Bugfix: file attributes
-- Video processing checks for presence of ffmpeg and displays warning if not found
-- Removed gender + age detection to speed up processing. Option removed from UI
-- Replaced restoreformer with restoreformer++
-- Live Cam recoded to run separate from virtual cam and without blocking controls
-- Swapping with only 1 target face allows selecting from several input faces
-
-
-
-**08.01.2024** v3.5.0
-
-- Bugfix: wrong access options when creating folders
-- New auto rotation of horizontal faces, fixing bad landmark positions (expanded on ![PR 364](https://github.com/C0untFloyd/roop-unleashed/pull/364))
-- Simple VR Option for stereo Images/Movies, best used in selected face mode
-- Added RestoreFormer Enhancer - https://github.com/wzhouxiff/RestoreFormer
-- Bumped up package versions for onnx/Torch etc.   
-
-
-**16.10.2023** v3.3.4
-
-**11.8.2023** v2.7.0
-
-Initial Gradio Version - old TkInter Version now deprecated
-
-- Re-added unified padding to face enhancers
-- Fixed DMDNet for all resolutions
-- Selecting target face now automatically switches swapping mode to selected
-- GPU providers are correctly set using the GUI (needs restart currently)
-- Local output folder can be opened from page
-- Unfinished extras functions disabled for now
-- Installer checks out specific commit, allowing to go back to first install
-- Updated readme for new gradio version
-- Updated Colab
-
-
-# Acknowledgements
-
-Lots of ideas, code or pre-trained models borrowed from the following projects:
-
-https://github.com/deepinsight/insightface<br />
-https://github.com/s0md3v/roop<br />
-https://github.com/AUTOMATIC1111/stable-diffusion-webui<br /> 
-https://github.com/Hillobar/Rope<br />
-https://github.com/TencentARC/GFPGAN<br />   
-https://github.com/kadirnar/codeformer-pip<br />
-https://github.com/csxmli2016/DMDNet<br />
-https://github.com/glucauze/sd-webui-faceswaplab<br />
-https://github.com/ykk648/face_power<br />
-
-<br />
-<br />
-Thanks to all developers!
-

roop-unleashed-main/clip/__init__.py

@@ -1 +0,0 @@
-from .clip import *

roop-unleashed-main/clip/bpe_simple_vocab_16e6.txt.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
-size 1356917

roop-unleashed-main/clip/clip.py

@@ -1,241 +0,0 @@
-import hashlib
-import os
-import urllib
-import warnings
-from typing import Any, Union, List
-
-import torch
-from PIL import Image
-from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
-from tqdm import tqdm
-
-from .model import build_model
-from .simple_tokenizer import SimpleTokenizer as _Tokenizer
-
-try:
-    from torchvision.transforms import InterpolationMode
-    BICUBIC = InterpolationMode.BICUBIC
-except ImportError:
-    BICUBIC = Image.BICUBIC
-
-
-
-__all__ = ["available_models", "load", "tokenize"]
-_tokenizer = _Tokenizer()
-
-_MODELS = {
-    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
-    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
-    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
-    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
-    "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
-    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
-    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
-    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
-    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
-}
-
-
-def _download(url: str, root: str):
-    os.makedirs(root, exist_ok=True)
-    filename = os.path.basename(url)
-
-    expected_sha256 = url.split("/")[-2]
-    download_target = os.path.join(root, filename)
-
-    if os.path.exists(download_target) and not os.path.isfile(download_target):
-        raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-    if os.path.isfile(download_target):
-        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
-            return download_target
-        else:
-            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
-
-    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
-        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
-            while True:
-                buffer = source.read(8192)
-                if not buffer:
-                    break
-
-                output.write(buffer)
-                loop.update(len(buffer))
-
-    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
-        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
-
-    return download_target
-
-
-def _convert_image_to_rgb(image):
-    return image.convert("RGB")
-
-
-def _transform(n_px):
-    return Compose([
-        Resize(n_px, interpolation=BICUBIC),
-        CenterCrop(n_px),
-        _convert_image_to_rgb,
-        ToTensor(),
-        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
-    ])
-
-
-def available_models() -> List[str]:
-    """Returns the names of available CLIP models"""
-    return list(_MODELS.keys())
-
-
-def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
-    """Load a CLIP model
-
-    Parameters
-    ----------
-    name : str
-        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
-
-    device : Union[str, torch.device]
-        The device to put the loaded model
-
-    jit : bool
-        Whether to load the optimized JIT model or more hackable non-JIT model (default).
-
-    download_root: str
-        path to download the model files; by default, it uses "~/.cache/clip"
-
-    Returns
-    -------
-    model : torch.nn.Module
-        The CLIP model
-
-    preprocess : Callable[[PIL.Image], torch.Tensor]
-        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
-    """
-    if name in _MODELS:
-        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
-    elif os.path.isfile(name):
-        model_path = name
-    else:
-        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
-
-    with open(model_path, 'rb') as opened_file:
-        try:
-            # loading JIT archive
-            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
-            state_dict = None
-        except RuntimeError:
-            # loading saved state dict
-            if jit:
-                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
-                jit = False
-            state_dict = torch.load(opened_file, map_location="cpu")
-
-    if not jit:
-        model = build_model(state_dict or model.state_dict()).to(device)
-        if str(device) == "cpu":
-            model.float()
-        return model, _transform(model.visual.input_resolution)
-
-    # patch the device names
-    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
-    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
-
-    def _node_get(node: torch._C.Node, key: str):
-        """Gets attributes of a node which is polymorphic over return type.
-        
-        From https://github.com/pytorch/pytorch/pull/82628
-        """
-        sel = node.kindOf(key)
-        return getattr(node, sel)(key)
-
-    def patch_device(module):
-        try:
-            graphs = [module.graph] if hasattr(module, "graph") else []
-        except RuntimeError:
-            graphs = []
-
-        if hasattr(module, "forward1"):
-            graphs.append(module.forward1.graph)
-
-        for graph in graphs:
-            for node in graph.findAllNodes("prim::Constant"):
-                if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
-                    node.copyAttributes(device_node)
-
-    model.apply(patch_device)
-    patch_device(model.encode_image)
-    patch_device(model.encode_text)
-
-    # patch dtype to float32 on CPU
-    if str(device) == "cpu":
-        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
-        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
-        float_node = float_input.node()
-
-        def patch_float(module):
-            try:
-                graphs = [module.graph] if hasattr(module, "graph") else []
-            except RuntimeError:
-                graphs = []
-
-            if hasattr(module, "forward1"):
-                graphs.append(module.forward1.graph)
-
-            for graph in graphs:
-                for node in graph.findAllNodes("aten::to"):
-                    inputs = list(node.inputs())
-                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
-                        if _node_get(inputs[i].node(), "value") == 5:
-                            inputs[i].node().copyAttributes(float_node)
-
-        model.apply(patch_float)
-        patch_float(model.encode_image)
-        patch_float(model.encode_text)
-
-        model.float()
-
-    return model, _transform(model.input_resolution.item())
-
-
-def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
-    """
-    Returns the tokenized representation of given input string(s)
-
-    Parameters
-    ----------
-    texts : Union[str, List[str]]
-        An input string or a list of input strings to tokenize
-
-    context_length : int
-        The context length to use; all CLIP models use 77 as the context length
-
-    truncate: bool
-        Whether to truncate the text in case its encoding is longer than the context length
-
-    Returns
-    -------
-    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
-    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
-    """
-    if isinstance(texts, str):
-        texts = [texts]
-
-    sot_token = _tokenizer.encoder["<|startoftext|>"]
-    eot_token = _tokenizer.encoder["<|endoftext|>"]
-    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
-    #if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
-    #    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-    #else:
-    result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
-
-    for i, tokens in enumerate(all_tokens):
-        if len(tokens) > context_length:
-            if truncate:
-                tokens = tokens[:context_length]
-                tokens[-1] = eot_token
-            else:
-                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
-        result[i, :len(tokens)] = torch.tensor(tokens)
-
-    return result

roop-unleashed-main/clip/clipseg.py

@@ -1,538 +0,0 @@
-import math
-from os.path import basename, dirname, join, isfile
-import torch
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules.activation import ReLU
-
-
-def get_prompt_list(prompt):
-    if prompt == 'plain':
-        return ['{}']    
-    elif prompt == 'fixed':
-        return ['a photo of a {}.']
-    elif prompt == 'shuffle':
-        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
-    elif prompt == 'shuffle+':
-        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
-                            'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
-                            'a bad photo of a {}.', 'a photo of the {}.']
-    else:
-        raise ValueError('Invalid value for prompt')        
-
-
-def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
-    """ 
-    Simplified version of multihead attention (taken from torch source code but without tons of if clauses). 
-    The mlp and layer norm come from CLIP.
-    x: input.
-    b: multihead attention module. 
-    """
-
-    x_ = b.ln_1(x)
-    q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
-    tgt_len, bsz, embed_dim = q.size()
-
-    head_dim = embed_dim // b.attn.num_heads
-    scaling = float(head_dim) ** -0.5
-
-    q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-    k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-    v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-
-    q = q * scaling
-
-    attn_output_weights = torch.bmm(q, k.transpose(1, 2)) #  n_heads * batch_size, tokens^2, tokens^2
-    if attn_mask is not None:
-
-
-        attn_mask_type, attn_mask = attn_mask
-        n_heads = attn_output_weights.size(0) // attn_mask.size(0)
-        attn_mask = attn_mask.repeat(n_heads, 1)
-        
-        if attn_mask_type == 'cls_token':
-            # the mask only affects similarities compared to the readout-token.
-            attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
-            # attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
-
-        if attn_mask_type == 'all':
-            # print(attn_output_weights.shape, attn_mask[:, None].shape)
-            attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
-        
-    
-    attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
-
-    attn_output = torch.bmm(attn_output_weights, v)
-    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-    attn_output = b.attn.out_proj(attn_output)
-
-    x = x + attn_output
-    x = x + b.mlp(b.ln_2(x))
-
-    if with_aff:
-        return x, attn_output_weights
-    else:
-        return x
-
-
-class CLIPDenseBase(nn.Module):
-
-    def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
-        super().__init__()
-
-        import clip
-
-        # prec = torch.FloatTensor
-        self.clip_model, _ = clip.load(version, device='cpu', jit=False)
-        self.model = self.clip_model.visual
-
-        # if not None, scale conv weights such that we obtain n_tokens.
-        self.n_tokens = n_tokens
-
-        for p in self.clip_model.parameters():
-            p.requires_grad_(False)
-
-        # conditional
-        if reduce_cond is not None:
-            self.reduce_cond = nn.Linear(512, reduce_cond)
-            for p in self.reduce_cond.parameters():
-                p.requires_grad_(False)
-        else:
-            self.reduce_cond = None        
-
-        self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        
-        self.reduce = nn.Linear(768, reduce_dim)
-
-        self.prompt_list = get_prompt_list(prompt)     
-
-        # precomputed prompts
-        import pickle
-        if isfile('precomputed_prompt_vectors.pickle'):
-            precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
-            self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}        
-        else:
-            self.precomputed_prompts = dict()
-    
-    def rescaled_pos_emb(self, new_size):
-        assert len(new_size) == 2
-
-        a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
-        b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
-        return torch.cat([self.model.positional_embedding[:1], b])
-
-    def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-        
-
-        with torch.no_grad():
-
-            inp_size = x_inp.shape[2:]
-
-            if self.n_tokens is not None:
-                stride2 = x_inp.shape[2] // self.n_tokens
-                conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
-                x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
-            else:
-                x = self.model.conv1(x_inp)  # shape = [*, width, grid, grid]
-
-            x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-            x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-
-            x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-
-            standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
-
-            if x.shape[1] != standard_n_tokens:
-                new_shape = int(math.sqrt(x.shape[1]-1))
-                x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
-            else:
-                x = x + self.model.positional_embedding.to(x.dtype)
-
-            x = self.model.ln_pre(x)
-
-            x = x.permute(1, 0, 2)  # NLD -> LND
-
-            activations, affinities = [], []
-            for i, res_block in enumerate(self.model.transformer.resblocks):
-                
-                if mask is not None:
-                    mask_layer, mask_type, mask_tensor = mask
-                    if mask_layer == i or mask_layer == 'all':
-                        # import ipdb; ipdb.set_trace()
-                        size = int(math.sqrt(x.shape[0] - 1))
-                        
-                        attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
-                        
-                    else:
-                        attn_mask = None
-                else:
-                    attn_mask = None
-
-                x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
-
-                if i in extract_layers:
-                    affinities += [aff_per_head]
-
-                    #if self.n_tokens is not None:
-                    #    activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
-                    #else:
-                    activations += [x]
-
-                if len(extract_layers) > 0 and i == max(extract_layers) and skip:
-                    print('early skip')
-                    break
-                
-            x = x.permute(1, 0, 2)  # LND -> NLD
-            x = self.model.ln_post(x[:, 0, :])
-
-            if self.model.proj is not None:
-                x = x @ self.model.proj
-
-            return x, activations, affinities
-
-    def sample_prompts(self, words, prompt_list=None):
-
-        prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
-        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
-        prompts = [prompt_list[i] for i in prompt_indices]
-        return [promt.format(w) for promt, w in zip(prompts, words)]
-
-    def get_cond_vec(self, conditional, batch_size):
-        # compute conditional from a single string
-        if conditional is not None and type(conditional) == str:
-            cond = self.compute_conditional(conditional)
-            cond = cond.repeat(batch_size, 1)
-
-        # compute conditional from string list/tuple
-        elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
-            assert len(conditional) == batch_size
-            cond = self.compute_conditional(conditional)
-
-        # use conditional directly
-        elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
-            cond = conditional
-
-        # compute conditional from image
-        elif conditional is not None and type(conditional) == torch.Tensor:
-            with torch.no_grad():
-                cond, _, _ = self.visual_forward(conditional)
-        else:
-            raise ValueError('invalid conditional')
-        return cond   
-
-    def compute_conditional(self, conditional):
-        import clip
-
-        dev = next(self.parameters()).device
-
-        if type(conditional) in {list, tuple}:
-            text_tokens = clip.tokenize(conditional).to(dev)
-            cond = self.clip_model.encode_text(text_tokens)
-        else:
-            if conditional in self.precomputed_prompts:
-                cond = self.precomputed_prompts[conditional].float().to(dev)
-            else:
-                text_tokens = clip.tokenize([conditional]).to(dev)
-                cond = self.clip_model.encode_text(text_tokens)[0]
-        
-        if self.shift_vector is not None:
-            return cond + self.shift_vector
-        else:
-            return cond
-
-
-def clip_load_untrained(version):
-    assert version == 'ViT-B/16'
-    from clip.model import CLIP
-    from clip.clip import _MODELS, _download
-    model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
-    state_dict = model.state_dict()
-
-    vision_width = state_dict["visual.conv1.weight"].shape[0]
-    vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-    vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-    grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-    image_resolution = vision_patch_size * grid_size
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
-    return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size, 
-        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)    
-
-
-class CLIPDensePredT(CLIPDenseBase):
-
-    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', 
-                 extra_blocks=0, reduce_cond=None, fix_shift=False,
-                 learn_trans_conv_only=False,  limit_to_clip_only=False, upsample=False, 
-                 add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False):
-        
-        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
-        # device = 'cpu'
-
-        self.extract_layers = extract_layers
-        self.cond_layer = cond_layer
-        self.limit_to_clip_only = limit_to_clip_only
-        self.process_cond = None
-        self.rev_activations = rev_activations
-        
-        depth = len(extract_layers)
-
-        if add_calibration:
-            self.calibration_conds = 1
-
-        self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
-        self.add_activation1 = True
-
-        self.version = version
-        
-        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-
-        if fix_shift:
-            # self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
-            self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
-            # self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
-        else:
-            self.shift_vector = None
-
-        if trans_conv is None:
-            trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
-        else:
-            # explicitly define transposed conv kernel size
-            trans_conv_ks = (trans_conv, trans_conv)
-
-        if not complex_trans_conv:
-            self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-        else:
-            assert trans_conv_ks[0] == trans_conv_ks[1]
-
-            tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)
-
-            self.trans_conv = nn.Sequential(
-                nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),
-                nn.ReLU(),
-                nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]),
-                nn.ReLU(),
-                nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]),               
-            )
-
-#        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-        
-        assert len(self.extract_layers) == depth
-
-        self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
-        self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
-        self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-        
-        # refinement and trans conv
-
-        if learn_trans_conv_only:
-            for p in self.parameters():
-                p.requires_grad_(False)
-            
-            for p in self.trans_conv.parameters():
-                p.requires_grad_(True)
-
-        self.prompt_list = get_prompt_list(prompt)
-
-
-    def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
-        assert type(return_features) == bool
-
-        inp_image = inp_image.to(self.model.positional_embedding.device)
-
-        if mask is not None:
-            raise ValueError('mask not supported')
-
-        # x_inp = normalize(inp_image)
-        x_inp = inp_image
-
-        bs, dev = inp_image.shape[0], x_inp.device
-
-        cond = self.get_cond_vec(conditional, bs)
-
-        visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
-        activation1 = activations[0]
-        activations = activations[1:]
-
-        _activations = activations[::-1] if not self.rev_activations else activations
-
-        a = None
-        for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
-            
-            if a is not None:
-                a = reduce(activation) + a
-            else:
-                a = reduce(activation)
-
-            if i == self.cond_layer:
-                if self.reduce_cond is not None:
-                    cond = self.reduce_cond(cond)
-                
-                a = self.film_mul(cond) * a + self.film_add(cond)
-
-            a = block(a)
-
-        for block in self.extra_blocks:
-            a = a + block(a)
-
-        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
-        size = int(math.sqrt(a.shape[2]))
-
-        a = a.view(bs, a.shape[1], size, size)
-
-        a = self.trans_conv(a)
-
-        if self.n_tokens is not None:
-            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True) 
-
-        if self.upsample_proj is not None:
-            a = self.upsample_proj(a)
-            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
-        if return_features:
-            return a, visual_q, cond, [activation1] + activations
-        else:
-            return a,
-
-
-
-class CLIPDensePredTMasked(CLIPDensePredT):
-
-    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, 
-                 prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False, 
-                 refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):
-
-        super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim, 
-                         n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond, 
-                         fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
-                         limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
-                         n_tokens=n_tokens)
-
-    def visual_forward_masked(self, img_s, seg_s):
-        return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))
-
-    def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):
-
-        if seg_s is None:
-            cond = cond_or_img_s
-        else:
-            img_s = cond_or_img_s
-
-            with torch.no_grad():
-                cond, _, _ = self.visual_forward_masked(img_s, seg_s)
-
-        return super().forward(img_q, cond, return_features=return_features)
-
-
-
-class CLIPDenseBaseline(CLIPDenseBase):
-
-    def __init__(self, version='ViT-B/32', cond_layer=0, 
-                extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed', 
-                 reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
-        
-        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
-        device = 'cpu'
-
-        # self.cond_layer = cond_layer
-        self.extract_layer = extract_layer
-        self.limit_to_clip_only = limit_to_clip_only
-        self.shift_vector = None
-
-        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-        
-        assert reduce2_dim is not None
-
-        self.reduce2 = nn.Sequential(
-            nn.Linear(reduce_dim, reduce2_dim),
-            nn.ReLU(),
-            nn.Linear(reduce2_dim, reduce_dim)
-        )
-        
-        trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
-        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
-
-    def forward(self, inp_image, conditional=None, return_features=False):
-
-        inp_image = inp_image.to(self.model.positional_embedding.device)
-
-        # x_inp = normalize(inp_image)
-        x_inp = inp_image
-
-        bs, dev = inp_image.shape[0], x_inp.device
-
-        cond = self.get_cond_vec(conditional, bs)
-
-        visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])
-
-        a = activations[0]
-        a = self.reduce(a)
-        a = self.film_mul(cond) * a + self.film_add(cond)
-
-        if self.reduce2 is not None:
-            a = self.reduce2(a)
-
-        # the original model would execute a transformer block here
-
-        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
-        size = int(math.sqrt(a.shape[2]))
-
-        a = a.view(bs, a.shape[1], size, size)
-        a = self.trans_conv(a)
-
-        if return_features:
-            return a, visual_q, cond, activations
-        else:
-            return a,
-
-
-class CLIPSegMultiLabel(nn.Module):
-
-    def __init__(self, model) -> None:
-        super().__init__()
-
-        from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
-
-        self.pascal_classes = VOC
-
-        from clip.clipseg import CLIPDensePredT
-        from general_utils import load_model
-        # self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
-        self.clipseg = load_model(model, strict=False)
-        
-        self.clipseg.eval()
-
-    def forward(self, x):
-
-        bs = x.shape[0]
-        out = torch.ones(21, bs, 352, 352).to(x.device) * -10
-
-        for class_id, class_name in enumerate(self.pascal_classes):
-        
-            fac = 3 if class_name == 'background' else 1
-
-            with torch.no_grad():
-                pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac
-
-            out[class_id] += pred
-
-
-        out = out.permute(1, 0, 2, 3)
-
-        return out
-
-        # construct output tensor
-                    

roop-unleashed-main/clip/model.py

@@ -1,436 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu1 = nn.ReLU(inplace=True)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.relu2 = nn.ReLU(inplace=True)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu3 = nn.ReLU(inplace=True)
-
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(OrderedDict([
-                ("-1", nn.AvgPool2d(stride)),
-                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
-                ("1", nn.BatchNorm2d(planes * self.expansion))
-            ]))
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu1(self.bn1(self.conv1(x)))
-        out = self.relu2(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu3(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x[:1], key=x, value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False
-        )
-        return x.squeeze(0)
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.input_resolution = input_resolution
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.avgpool = nn.AvgPool2d(2)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        def stem(x):
-            x = self.relu1(self.bn1(self.conv1(x)))
-            x = self.relu2(self.bn2(self.conv2(x)))
-            x = self.relu3(self.bn3(self.conv3(x)))
-            x = self.avgpool(x)
-            return x
-
-        x = x.type(self.conv1.weight.dtype)
-        x = stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
-    def forward(self, x: torch.Tensor):
-        return self.resblocks(x)
-
-
-class VisionTransformer(nn.Module):
-    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
-        self.ln_pre = LayerNorm(width)
-
-        self.transformer = Transformer(width, layers, heads)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-class CLIP(nn.Module):
-    def __init__(self,
-                 embed_dim: int,
-                 # vision
-                 image_resolution: int,
-                 vision_layers: Union[Tuple[int, int, int, int], int],
-                 vision_width: int,
-                 vision_patch_size: int,
-                 # text
-                 context_length: int,
-                 vocab_size: int,
-                 transformer_width: int,
-                 transformer_heads: int,
-                 transformer_layers: int
-                 ):
-        super().__init__()
-
-        self.context_length = context_length
-
-        if isinstance(vision_layers, (tuple, list)):
-            vision_heads = vision_width * 32 // 64
-            self.visual = ModifiedResNet(
-                layers=vision_layers,
-                output_dim=embed_dim,
-                heads=vision_heads,
-                input_resolution=image_resolution,
-                width=vision_width
-            )
-        else:
-            vision_heads = vision_width // 64
-            self.visual = VisionTransformer(
-                input_resolution=image_resolution,
-                patch_size=vision_patch_size,
-                width=vision_width,
-                layers=vision_layers,
-                heads=vision_heads,
-                output_dim=embed_dim
-            )
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        if isinstance(self.visual, ModifiedResNet):
-            if self.visual.attnpool is not None:
-                std = self.visual.attnpool.c_proj.in_features ** -0.5
-                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
-            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
-                for name, param in resnet_block.named_parameters():
-                    if name.endswith("bn3.weight"):
-                        nn.init.zeros_(param)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def dtype(self):
-        return self.visual.conv1.weight.dtype
-
-    def encode_image(self, image):
-        return self.visual(image.type(self.dtype))
-
-    def encode_text(self, text):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
-        return x
-
-    def forward(self, image, text):
-        image_features = self.encode_image(image)
-        text_features = self.encode_text(text)
-
-        # normalized features
-        image_features = image_features / image_features.norm(dim=1, keepdim=True)
-        text_features = text_features / text_features.norm(dim=1, keepdim=True)
-
-        # cosine similarity as logits
-        logit_scale = self.logit_scale.exp()
-        logits_per_image = logit_scale * image_features @ text_features.t()
-        logits_per_text = logits_per_image.t()
-
-        # shape = [global_batch_size, global_batch_size]
-        return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
-    vit = "visual.proj" in state_dict
-
-    if vit:
-        vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-        image_resolution = vision_patch_size * grid_size
-    else:
-        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
-        vision_layers = tuple(counts)
-        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
-        vision_patch_size = None
-        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
-        image_resolution = output_width * 32
-
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
-
-    model = CLIP(
-        embed_dim,
-        image_resolution, vision_layers, vision_width, vision_patch_size,
-        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
-    )
-
-    for key in ["input_resolution", "context_length", "vocab_size"]:
-        if key in state_dict:
-            del state_dict[key]
-
-    convert_weights(model)
-    model.load_state_dict(state_dict)
-    return model.eval()

roop-unleashed-main/clip/simple_tokenizer.py

@@ -1,132 +0,0 @@
-import gzip
-import html
-import os
-from functools import lru_cache
-
-import ftfy
-import regex as re
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
-        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text

roop-unleashed-main/clip/vitseg.py

@@ -1,286 +0,0 @@
-import math
-from posixpath import basename, dirname, join
-# import clip
-from clip.model import convert_weights
-import torch
-import json
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules import activation
-from torch.nn.modules.activation import ReLU
-from torchvision import transforms
-
-normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-from torchvision.models import ResNet
-
-
-def process_prompts(conditional, prompt_list, conditional_map):
-    # DEPRECATED
-            
-    # randomly sample a synonym
-    words = [conditional_map[int(i)] for i in conditional]
-    words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
-    words = [w.replace('_', ' ') for w in words]
-
-    if prompt_list is not None:
-        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
-        prompts = [prompt_list[i] for i in prompt_indices]
-    else:
-        prompts = ['a photo of {}'] * (len(words))
-
-    return [promt.format(w) for promt, w in zip(prompts, words)]
-
-
-class VITDenseBase(nn.Module):
-    
-    def rescaled_pos_emb(self, new_size):
-        assert len(new_size) == 2
-
-        a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
-        b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
-        return torch.cat([self.model.positional_embedding[:1], b])
-
-    def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-        
-        with torch.no_grad():
-
-            x_inp = nnf.interpolate(x_inp, (384, 384))
-
-            x = self.model.patch_embed(x_inp)
-            cls_token = self.model.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-            if self.model.dist_token is None:
-                x = torch.cat((cls_token, x), dim=1)
-            else:
-                x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
-            x = self.model.pos_drop(x + self.model.pos_embed)
-
-            activations = []
-            for i, block in enumerate(self.model.blocks):
-                x = block(x)
-
-                if i in extract_layers:
-                    # permute to be compatible with CLIP
-                    activations += [x.permute(1,0,2)]                
-
-            x = self.model.norm(x)
-            x = self.model.head(self.model.pre_logits(x[:, 0]))
-
-            # again for CLIP compatibility
-            # x = x.permute(1, 0, 2)
-
-        return x, activations, None
-
-    def sample_prompts(self, words, prompt_list=None):
-
-        prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
-        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
-        prompts = [prompt_list[i] for i in prompt_indices]
-        return [promt.format(w) for promt, w in zip(prompts, words)]
-
-    def get_cond_vec(self, conditional, batch_size):
-        # compute conditional from a single string
-        if conditional is not None and type(conditional) == str:
-            cond = self.compute_conditional(conditional)
-            cond = cond.repeat(batch_size, 1)
-
-        # compute conditional from string list/tuple
-        elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
-            assert len(conditional) == batch_size
-            cond = self.compute_conditional(conditional)
-
-        # use conditional directly
-        elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
-            cond = conditional
-
-        # compute conditional from image
-        elif conditional is not None and type(conditional) == torch.Tensor:
-            with torch.no_grad():
-                cond, _, _ = self.visual_forward(conditional)
-        else:
-            raise ValueError('invalid conditional')
-        return cond   
-
-    def compute_conditional(self, conditional):
-        import clip
-
-        dev = next(self.parameters()).device
-
-        if type(conditional) in {list, tuple}:
-            text_tokens = clip.tokenize(conditional).to(dev)
-            cond = self.clip_model.encode_text(text_tokens)
-        else:
-            if conditional in self.precomputed_prompts:
-                cond = self.precomputed_prompts[conditional].float().to(dev)
-            else:
-                text_tokens = clip.tokenize([conditional]).to(dev)
-                cond = self.clip_model.encode_text(text_tokens)[0]
-        
-        return cond
-
-
-class VITDensePredT(VITDenseBase):
-
-    def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', 
-                 depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
-                 learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False, 
-                 add_calibration=False, process_cond=None, not_pretrained=False):
-        super().__init__()
-        # device = 'cpu'
-
-        self.extract_layers = extract_layers
-        self.cond_layer = cond_layer
-        self.limit_to_clip_only = limit_to_clip_only
-        self.process_cond = None
-        
-        if add_calibration:
-            self.calibration_conds = 1
-
-        self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
-        self.add_activation1 = True
-
-        import timm 
-        self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
-        self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
-
-        for p in self.model.parameters():
-            p.requires_grad_(False)
-
-        import clip
-        self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
-        # del self.clip_model.visual
-        
-        
-        self.token_shape = (14, 14)
-
-        # conditional
-        if reduce_cond is not None:
-            self.reduce_cond = nn.Linear(512, reduce_cond)
-            for p in self.reduce_cond.parameters():
-                p.requires_grad_(False)
-        else:
-            self.reduce_cond = None
-
-        # self.film = AVAILABLE_BLOCKS['film'](512, 128)
-        self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        
-        # DEPRECATED
-        # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
-        
-        assert len(self.extract_layers) == depth
-
-        self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
-        self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
-        self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
-        trans_conv_ks = (16, 16)
-        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
-        # refinement and trans conv
-
-        if learn_trans_conv_only:
-            for p in self.parameters():
-                p.requires_grad_(False)
-            
-            for p in self.trans_conv.parameters():
-                p.requires_grad_(True)
-
-        if prompt == 'fixed':
-            self.prompt_list = ['a photo of a {}.']
-        elif prompt == 'shuffle':
-            self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
-        elif prompt == 'shuffle+':
-            self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
-                                'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
-                                'a bad photo of a {}.', 'a photo of the {}.']
-        elif prompt == 'shuffle_clip':
-            from models.clip_prompts import imagenet_templates
-            self.prompt_list = imagenet_templates
-
-        if process_cond is not None:
-            if process_cond == 'clamp' or process_cond[0] == 'clamp':
-
-                val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
-
-                def clamp_vec(x):
-                    return torch.clamp(x, -val, val)
-
-                self.process_cond = clamp_vec
-
-            elif process_cond.endswith('.pth'):
-                
-                shift = torch.load(process_cond)
-                def add_shift(x):
-                    return x + shift.to(x.device)
-
-                self.process_cond = add_shift
-
-        import pickle
-        precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
-        self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
-
-
-    def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
-        assert type(return_features) == bool
-
-        # inp_image = inp_image.to(self.model.positional_embedding.device)
-
-        if mask is not None:
-            raise ValueError('mask not supported')
-
-        # x_inp = normalize(inp_image)
-        x_inp = inp_image
-
-        bs, dev = inp_image.shape[0], x_inp.device
-
-        inp_image_size = inp_image.shape[2:]
-
-        cond = self.get_cond_vec(conditional, bs)
-
-        visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
-        activation1 = activations[0]
-        activations = activations[1:]
-
-        a = None
-        for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
-            
-            if a is not None:
-                a = reduce(activation) + a
-            else:
-                a = reduce(activation)
-
-            if i == self.cond_layer:
-                if self.reduce_cond is not None:
-                    cond = self.reduce_cond(cond)
-                
-                a = self.film_mul(cond) * a + self.film_add(cond)
-
-            a = block(a)
-
-        for block in self.extra_blocks:
-            a = a + block(a)
-
-        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
-        size = int(math.sqrt(a.shape[2]))
-
-        a = a.view(bs, a.shape[1], size, size)
-
-        if self.trans_conv is not None:
-            a = self.trans_conv(a)
-
-        if self.upsample_proj is not None:
-            a = self.upsample_proj(a)
-            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
-        a = nnf.interpolate(a, inp_image_size)
-
-        if return_features:
-            return a, visual_q, cond, [activation1] + activations
-        else:
-            return a,

roop-unleashed-main/config_colab.yaml

@@ -1,14 +0,0 @@
-clear_output: true
-force_cpu: false
-max_threads: 3
-memory_limit: 0
-output_image_format: png
-output_template: '{file}_{time}'
-output_video_codec: libx264
-output_video_format: mp4
-provider: cuda
-selected_theme: Default
-server_name: ''
-server_port: 0
-server_share: true
-video_quality: 14

roop-unleashed-main/installer/installer.py

@@ -1,87 +0,0 @@
-import argparse
-import glob
-import os
-import shutil
-import site
-import subprocess
-import sys
-
-
-script_dir = os.getcwd()
-
-
-def run_cmd(cmd, capture_output=False, env=None):
-    # Run shell commands
-    return subprocess.run(cmd, shell=True, capture_output=capture_output, env=env)
-
-
-def check_env():
-    # If we have access to conda, we are probably in an environment
-    conda_not_exist = run_cmd("conda", capture_output=True).returncode
-    if conda_not_exist:
-        print("Conda is not installed. Exiting...")
-        sys.exit()
-    
-    # Ensure this is a new environment and not the base environment
-    if os.environ["CONDA_DEFAULT_ENV"] == "base":
-        print("Create an environment for this project and activate it. Exiting...")
-        sys.exit()
-
-
-def install_dependencies():
-    global MY_PATH
-
-    # Install Git and clone repo
-    run_cmd("conda install -y -k git")
-    run_cmd("git clone https://github.com/C0untFloyd/roop-unleashed.git")
-    os.chdir(MY_PATH)
-    run_cmd("git checkout 5bfafdc97a0c47b46ec83e6530a57399aaad75d7")
-    # Installs dependencies from requirements.txt
-    run_cmd("python -m pip install -r requirements.txt")
-
-
-
-def update_dependencies():
-    global MY_PATH
-    
-    os.chdir(MY_PATH)
-	# do a hard reset for to update even if there are local changes
-    run_cmd("git fetch --all")
-    run_cmd("git reset --hard origin/main")
-    run_cmd("git pull")
-    # Installs/Updates dependencies from all requirements.txt
-    run_cmd("python -m pip install -r requirements.txt")
-
-
-def start_app():
-    global MY_PATH
-    
-    os.chdir(MY_PATH)
-    # forward commandline arguments
-    sys.argv.pop(0)
-    args = ' '.join(sys.argv)
-    print("Launching App")
-    run_cmd(f'python run.py {args}')
-
-
-if __name__ == "__main__":
-    global MY_PATH
-    
-    MY_PATH = "roop-unleashed"
-
-    
-    # Verifies we are in a conda environment
-    check_env()
-
-    # If webui has already been installed, skip and run
-    if not os.path.exists(MY_PATH):
-        install_dependencies()
-    else:
-        # moved update from batch to here, because of batch limitations
-        updatechoice = input("Check for Updates? [y/n]").lower()
-        if updatechoice == "y":
-           update_dependencies()
-
-    # Run the model with webui
-    os.chdir(script_dir)
-    start_app()

roop-unleashed-main/installer/macOSinstaller.sh

@@ -1,73 +0,0 @@
-#!/bin/bash
-
-# This script checks and installs all dependencies which are needed to run roop-unleashed. After that, it clones the repo.
-# Execute this easily with /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/PJF16/roop-unleashed/master/installer/macOSinstaller.sh)
-
-# Function to check if a command exists
-command_exists() {
-    command -v "$1" >/dev/null 2>&1
-}
-
-echo "Starting check and installation process of dependencies for roop-unleashed"
-
-# Check if Homebrew is installed
-if ! command_exists brew; then
-    echo "Homebrew is not installed. Starting installation..."
-    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-else
-    echo "Homebrew is already installed."
-fi
-
-# Update Homebrew
-echo "Updating Homebrew..."
-brew update
-
-# Check if Python 3.11 is installed
-if brew list --versions [email protected] >/dev/null; then
-    echo "Python 3.11 is already installed."
-else
-    echo "Python 3.11 is not installed. Installing it now..."
-    brew install [email protected]
-fi
-
-# Check if [email protected] is installed
-if brew list --versions [email protected] >/dev/null; then
-    echo "[email protected] is already installed."
-else
-    echo "[email protected] is not installed. Installing it now..."
-    brew install [email protected]
-fi
-
-# Check if ffmpeg is installed
-if command_exists ffmpeg; then
-    echo "ffmpeg is already installed."
-else
-    echo "ffmpeg is not installed. Installing it now..."
-    brew install ffmpeg
-fi
-
-# Check if git is installed
-if command_exists git; then
-    echo "git is already installed."
-else
-    echo "git is not installed. Installing it now..."
-    brew install git
-fi
-
-# Clone the repository
-REPO_URL="https://github.com/C0untFloyd/roop-unleashed.git"
-REPO_NAME="roop-unleashed"
-
-echo "Cloning the repository $REPO_URL..."
-git clone $REPO_URL
-
-# Check if the repository was cloned successfully
-if [ -d "$REPO_NAME" ]; then
-    echo "Repository cloned successfully. Changing into directory $REPO_NAME..."
-    cd "$REPO_NAME"
-else
-    echo "Failed to clone the repository."
-fi
-
-echo "Check and installation process completed."
-

roop-unleashed-main/installer/windows_run.bat

@@ -1,95 +0,0 @@
-@echo off
-
-REM No CLI arguments supported anymore
-set COMMANDLINE_ARGS=
-
-cd /D "%~dp0"
-
-echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-@rem config
-set INSTALL_DIR=%cd%\installer_files
-set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe
-set FFMPEG_DOWNLOAD_URL=https://github.com/GyanD/codexffmpeg/releases/download/7.1/ffmpeg-7.1-essentials_build.zip
-set INSTALL_FFMPEG_DIR=%cd%\installer_files\ffmpeg
-set INSIGHTFACE_PACKAGE_URL=https://github.com/C0untFloyd/roop-unleashed/releases/download/3.6.6/insightface-0.7.3-cp310-cp310-win_amd64.whl
-set INSIGHTFACE_PACKAGE_PATH=%INSTALL_DIR%\insightface-0.7.3-cp310-cp310-win_amd64.whl
-
-set conda_exists=F
-set ffmpeg_exists=F
-
-@rem figure out whether git and conda needs to be installed
-call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
-if "%ERRORLEVEL%" EQU "0" set conda_exists=T
-
-@rem Check if FFmpeg is already in PATH
-where ffmpeg >nul 2>&1
-if "%ERRORLEVEL%" EQU "0" (
-    echo FFmpeg is already installed.
-    set ffmpeg_exists=T
-)
-
-@rem (if necessary) install git and conda into a contained environment
-
-@rem download conda
-if "%conda_exists%" == "F" (
-    echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
-    mkdir "%INSTALL_DIR%"
-    call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
-    echo Installing Miniconda to %CONDA_ROOT_PREFIX%
-    start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
-
-    @rem test the conda binary
-    echo Miniconda version:
-    call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
-)
-
-@rem create the installer env
-if not exist "%INSTALL_ENV_DIR%" (
-    echo Creating Conda Environment
-    call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo ERROR: Conda environment creation failed. && goto end )
-    @rem check if conda environment was actually created
-    if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end )
-    @rem activate installer env
-    call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo ERROR: Miniconda hook not found. && goto end )
-    @rem Download insightface package
-    echo Downloading insightface package from %INSIGHTFACE_PACKAGE_URL% to %INSIGHTFACE_PACKAGE_PATH%
-    call curl -Lk "%INSIGHTFACE_PACKAGE_URL%" > "%INSIGHTFACE_PACKAGE_PATH%" || ( echo. && echo ERROR: Insightface package failed to download. && goto end )
-    @rem install insightface package using pip
-    echo Installing insightface package
-    call pip install "%INSIGHTFACE_PACKAGE_PATH%" || ( echo. && echo ERROR: Insightface package installation failed. && goto end )
-)
-
-@rem Download and install FFmpeg if not already installed
-if "%ffmpeg_exists%" == "F" (
-    if not exist "%INSTALL_FFMPEG_DIR%" (
-        echo Downloading ffmpeg from %FFMPEG_DOWNLOAD_URL% to %INSTALL_DIR%
-        call curl -Lk "%FFMPEG_DOWNLOAD_URL%" > "%INSTALL_DIR%\ffmpeg.zip" || ( echo. && echo ffmpeg failed to download. && goto end )
-        call powershell -command "Expand-Archive -Force '%INSTALL_DIR%\ffmpeg.zip' '%INSTALL_DIR%\'"
-        cd "%INSTALL_DIR%"
-        move ffmpeg-* ffmpeg
-        setx PATH "%INSTALL_FFMPEG_DIR%\bin\;%PATH%"
-        echo To use videos, you need to restart roop after this installation.
-        cd ..
-    )
-) else (
-    echo Skipping FFmpeg installation as it is already available.
-)
-
-@rem setup installer env
-@rem check if conda environment was actually created
-if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end )
-@rem activate installer env
-call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo ERROR: Miniconda hook not found. && goto end )
-echo Launching roop unleashed
-call python installer.py %COMMANDLINE_ARGS%
-
-echo.
-echo Done!
-
-:end
-pause

roop-unleashed-main/mypy.ini

@@ -1,7 +0,0 @@
-[mypy]
-check_untyped_defs = True
-disallow_any_generics = True
-disallow_untyped_calls = True
-disallow_untyped_defs = True
-ignore_missing_imports = True
-strict_optional = False

roop-unleashed-main/requirements.txt

@@ -1,19 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu124
-numpy==1.26.4
-gradio==5.9.1
-opencv-python-headless==4.10.0.84
-onnx==1.16.1
-insightface==0.7.3
-albucore==0.0.16
-psutil==5.9.6
-torch==2.5.1+cu124; sys_platform != 'darwin'
-torch==2.5.1; sys_platform == 'darwin'
-torchvision==0.20.1+cu124; sys_platform != 'darwin'
-torchvision==0.20.1; sys_platform == 'darwin'
-onnxruntime==1.20.1; sys_platform == 'darwin' and platform_machine != 'arm64'
-onnxruntime-silicon==1.20.1; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.20.1; sys_platform != 'darwin'
-tqdm==4.66.4
-ftfy
-regex
-pyvirtualcam

roop-unleashed-main/roop-unleashed.ipynb

@@ -1,166 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "G9BdiCppV6AS"
-      },
-      "source": [
-        "# Colab for roop-unleashed - Gradio version\n",
-        "https://github.com/C0untFloyd/roop-unleashed\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CanIXgLJgaOj"
-      },
-      "source": [
-        "Install CUDA 12.6 on Google Cloud Compute\n",
-        "(currently unnecessary because the latest 12.x should be already installed)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "96GE4UgYg3Ej"
-      },
-      "outputs": [],
-      "source": [
-        "# don't run this cell if you know that there is at least Cuda 12.4 installed\n",
-        "!apt-get -y update\n",
-        "!apt-get -y install cuda-toolkit-12-6\n",
-        "import os\n",
-        "os.environ[\"LD_LIBRARY_PATH\"] += \":\" + \"/usr/local/cuda-12/lib64\"\n",
-        "os.environ[\"LD_LIBRARY_PATH\"] += \":\" + \"/usr/local/cuda-12.6/lib64\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0ZYRNb0AWLLW"
-      },
-      "source": [
-        "Installing & preparing requirements"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "t1yPuhdySqCq"
-      },
-      "outputs": [],
-      "source": [
-        "!git clone https://github.com/C0untFloyd/roop-unleashed.git\n",
-        "%cd roop-unleashed\n",
-        "!mv config_colab.yaml config.yaml\n",
-        "!pip install -r requirements.txt"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "u_4JQiSlV9Fi"
-      },
-      "source": [
-        "Running roop-unleashed with default config"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Is6U2huqSzLE"
-      },
-      "outputs": [],
-      "source": [
-        "!python run.py"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UdQ1VHdI8lCf"
-      },
-      "source": [
-        "### Download generated images folder\n",
-        "(only needed if you want to zip the generated output)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 17
-        },
-        "id": "oYjWveAmw10X",
-        "outputId": "5b4c3650-f951-434a-c650-5525a8a70c1e"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
-            "text/plain": [
-              "<IPython.core.display.Javascript object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/javascript": "download(\"download_789eab11-93d2-4880-adf3-6aceee0cc5f9\", \"fake_output.zip.zip\", 80125)",
-            "text/plain": [
-              "<IPython.core.display.Javascript object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import shutil\n",
-        "import os\n",
-        "from google.colab import files\n",
-        "\n",
-        "def zip_directory(directory_path, zip_path):\n",
-        "    shutil.make_archive(zip_path, 'zip', directory_path)\n",
-        "\n",
-        "# Set the directory path you want to download\n",
-        "directory_path = '/content/roop-unleashed/output'\n",
-        "\n",
-        "# Set the zip file name\n",
-        "zip_filename = 'fake_output.zip'\n",
-        "\n",
-        "# Zip the directory\n",
-        "zip_directory(directory_path, zip_filename)\n",
-        "\n",
-        "# Download the zip file\n",
-        "files.download(zip_filename+'.zip')\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "collapsed_sections": [
-        "UdQ1VHdI8lCf"
-      ],
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

roop-unleashed-main/roop/FaceSet.py

@@ -1,20 +0,0 @@
-import numpy as np
-
-class FaceSet:
-    faces = []
-    ref_images = []
-    embedding_average = 'None'
-    embeddings_backup = None
-
-    def __init__(self):
-        self.faces = []
-        self.ref_images = []
-        self.embeddings_backup = None
-
-    def AverageEmbeddings(self):
-        if len(self.faces) > 1 and self.embeddings_backup is None:
-            self.embeddings_backup = self.faces[0]['embedding']
-            embeddings = [face.embedding for face in self.faces]
-
-            self.faces[0]['embedding'] = np.mean(embeddings, axis=0)
-            # try median too?

roop-unleashed-main/roop/ProcessEntry.py

@@ -1,7 +0,0 @@
-class ProcessEntry:
-    def __init__(self, filename: str, start: int, end: int, fps: float):
-        self.filename = filename
-        self.finalname = None
-        self.startframe = start
-        self.endframe = end
-        self.fps = fps

roop-unleashed-main/roop/ProcessMgr.py

@@ -1,911 +0,0 @@
-import os
-import cv2 
-import numpy as np
-import psutil
-
-from roop.ProcessOptions import ProcessOptions
-
-from roop.face_util import get_first_face, get_all_faces, rotate_anticlockwise, rotate_clockwise, clamp_cut_values
-from roop.utilities import compute_cosine_distance, get_device, str_to_class, shuffle_array
-import roop.vr_util as vr
-
-from typing import Any, List, Callable
-from roop.typing import Frame, Face
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from threading import Thread, Lock
-from queue import Queue
-from tqdm import tqdm
-from roop.ffmpeg_writer import FFMPEG_VideoWriter
-from roop.StreamWriter import StreamWriter
-import roop.globals
-
-
-
-# Poor man's enum to be able to compare to int
-class eNoFaceAction():
-    USE_ORIGINAL_FRAME = 0
-    RETRY_ROTATED = 1
-    SKIP_FRAME = 2
-    SKIP_FRAME_IF_DISSIMILAR = 3,
-    USE_LAST_SWAPPED = 4
-
-
-
-def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
-    queue: Queue[str] = Queue()
-    for frame_path in temp_frame_paths:
-        queue.put(frame_path)
-    return queue
-
-
-def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
-    queues = []
-    for _ in range(queue_per_future):
-        if not queue.empty():
-            queues.append(queue.get())
-    return queues
-
-
-
-class ProcessMgr():
-    input_face_datas = []
-    target_face_datas = []
-
-    imagemask = None
-
-    processors = []
-    options : ProcessOptions = None
-    
-    num_threads = 1
-    current_index = 0
-    processing_threads = 1
-    buffer_wait_time = 0.1
-
-    lock = Lock()
-
-    frames_queue = None
-    processed_queue = None
-
-    videowriter= None
-    streamwriter = None
-
-    progress_gradio = None
-    total_frames = 0
-
-    num_frames_no_face = 0
-    last_swapped_frame = None
-
-    output_to_file = None
-    output_to_cam = None
-
-
-    plugins =  { 
-    'faceswap'          : 'FaceSwapInsightFace',
-    'mask_clip2seg'     : 'Mask_Clip2Seg',
-    'mask_xseg'         : 'Mask_XSeg',
-    'codeformer'        : 'Enhance_CodeFormer',
-    'gfpgan'            : 'Enhance_GFPGAN',
-    'dmdnet'            : 'Enhance_DMDNet',
-    'gpen'              : 'Enhance_GPEN',
-    'restoreformer++'   : 'Enhance_RestoreFormerPPlus',
-    'colorizer'         : 'Frame_Colorizer',
-    'filter_generic'    : 'Frame_Filter',
-    'removebg'          : 'Frame_Masking',
-    'upscale'           : 'Frame_Upscale'
-    }
-
-    def __init__(self, progress):
-        if progress is not None:
-            self.progress_gradio = progress
-
-    def reuseOldProcessor(self, name:str):
-        for p in self.processors:
-            if p.processorname == name:
-                return p
-            
-        return None
-
-
-    def initialize(self, input_faces, target_faces, options):
-        self.input_face_datas = input_faces
-        self.target_face_datas = target_faces
-        self.num_frames_no_face = 0
-        self.last_swapped_frame = None
-        self.options = options
-        devicename = get_device()
-
-        roop.globals.g_desired_face_analysis=["landmark_3d_68", "landmark_2d_106","detection","recognition"]
-        if options.swap_mode == "all_female" or options.swap_mode == "all_male":
-            roop.globals.g_desired_face_analysis.append("genderage")
-        elif options.swap_mode == "all_random":
-            # don't modify original list
-            self.input_face_datas = input_faces.copy()
-            shuffle_array(self.input_face_datas)
-
-
-        for p in self.processors:
-            newp = next((x for x in options.processors.keys() if x == p.processorname), None)
-            if newp is None:
-                p.Release()
-                del p
-
-        newprocessors = []
-        for key, extoption in options.processors.items():
-            p = self.reuseOldProcessor(key)
-            if p is None:
-                classname = self.plugins[key]
-                module = 'roop.processors.' + classname
-                p = str_to_class(module, classname)
-            if p is not None:
-                extoption.update({"devicename": devicename})
-                if p.type == "swap":
-                    if self.options.swap_modelname == "InSwapper 128":
-                        extoption.update({"modelname": "inswapper_128.onnx"})
-                    elif self.options.swap_modelname == "ReSwapper 128":
-                        extoption.update({"modelname": "reswapper_128.onnx"})
-                    elif self.options.swap_modelname == "ReSwapper 256":
-                        extoption.update({"modelname": "reswapper_256.onnx"})
-
-                p.Initialize(extoption)
-                newprocessors.append(p)
-            else:
-                print(f"Not using {module}")
-        self.processors = newprocessors
-
-
-
-        if isinstance(self.options.imagemask, dict) and self.options.imagemask.get("layers") and len(self.options.imagemask["layers"]) > 0:
-            self.options.imagemask  = self.options.imagemask.get("layers")[0]
-            # Get rid of alpha
-            self.options.imagemask = cv2.cvtColor(self.options.imagemask, cv2.COLOR_RGBA2GRAY)
-            if np.any(self.options.imagemask):
-                mo = self.input_face_datas[0].faces[0].mask_offsets
-                self.options.imagemask = self.blur_area(self.options.imagemask, mo[4], mo[5])
-                self.options.imagemask = self.options.imagemask.astype(np.float32) / 255
-                self.options.imagemask = cv2.cvtColor(self.options.imagemask, cv2.COLOR_GRAY2RGB)
-            else:
-                self.options.imagemask = None
-
-        self.options.frame_processing = False
-        for p in self.processors:
-            if p.type.startswith("frame_"):
-                self.options.frame_processing = True
-
-            
- 
-
-
-
-    def run_batch(self, source_files, target_files, threads:int = 1):
-        progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
-        self.total_frames = len(source_files)
-        self.num_threads = threads
-        with tqdm(total=self.total_frames, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
-            with ThreadPoolExecutor(max_workers=threads) as executor:
-                futures = []
-                queue = create_queue(source_files)
-                queue_per_future = max(len(source_files) // threads, 1)
-                while not queue.empty():
-                    future = executor.submit(self.process_frames, source_files, target_files, pick_queue(queue, queue_per_future), lambda: self.update_progress(progress))
-                    futures.append(future)
-                for future in as_completed(futures):
-                    future.result()
-
-
-    def process_frames(self, source_files: List[str], target_files: List[str], current_files, update: Callable[[], None]) -> None:
-        for f in current_files:
-            if not roop.globals.processing:
-                return
-            
-            # Decode the byte array into an OpenCV image
-            temp_frame = cv2.imdecode(np.fromfile(f, dtype=np.uint8), cv2.IMREAD_COLOR)
-            if temp_frame is not None:
-                if self.options.frame_processing:
-                    for p in self.processors:
-                        frame = p.Run(temp_frame)
-                    resimg = frame
-                else:
-                    resimg = self.process_frame(temp_frame)
-                if resimg is not None:
-                    i = source_files.index(f)
-                    # Also let numpy write the file to support utf-8/16 filenames
-                    cv2.imencode(f'.{roop.globals.CFG.output_image_format}',resimg)[1].tofile(target_files[i])
-            if update:
-                update()
-
-
-
-    def read_frames_thread(self, cap, frame_start, frame_end, num_threads):
-        num_frame = 0
-        total_num = frame_end - frame_start
-        if frame_start > 0:
-            cap.set(cv2.CAP_PROP_POS_FRAMES,frame_start)
-
-        while True and roop.globals.processing:
-            ret, frame = cap.read()
-            if not ret:
-                break
-                
-            self.frames_queue[num_frame % num_threads].put(frame, block=True)
-            num_frame += 1
-            if num_frame == total_num:
-                break
-
-        for i in range(num_threads):
-            self.frames_queue[i].put(None)
-
-
-
-    def process_videoframes(self, threadindex, progress) -> None:
-        while True:
-            frame = self.frames_queue[threadindex].get()
-            if frame is None:
-                self.processing_threads -= 1
-                self.processed_queue[threadindex].put((False, None))
-                return
-            else:
-                if self.options.frame_processing:
-                    for p in self.processors:
-                        frame = p.Run(frame)
-                    resimg = frame
-                else:                            
-                    resimg = self.process_frame(frame)
-                self.processed_queue[threadindex].put((True, resimg))
-                del frame
-                progress()
-
-
-    def write_frames_thread(self):
-        nextindex = 0
-        num_producers = self.num_threads
-        
-        while True:
-            process, frame = self.processed_queue[nextindex % self.num_threads].get()
-            nextindex += 1
-            if frame is not None:
-                if self.output_to_file:
-                    self.videowriter.write_frame(frame)
-                if self.output_to_cam:
-                    self.streamwriter.WriteToStream(frame)
-                del frame
-            elif process == False:
-                num_producers -= 1
-                if num_producers < 1:
-                    return
-            
-
-
-    def run_batch_inmem(self, output_method, source_video, target_video, frame_start, frame_end, fps, threads:int = 1):
-        if len(self.processors) < 1:
-            print("No processor defined!")
-            return
-
-        cap = cv2.VideoCapture(source_video)
-        # frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        frame_count = (frame_end - frame_start) + 1
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-        processed_resolution = None
-        for p in self.processors:
-            if hasattr(p, 'getProcessedResolution'):
-                processed_resolution = p.getProcessedResolution(width, height)
-                print(f"Processed resolution: {processed_resolution}")
-        if processed_resolution is not None:
-            width = processed_resolution[0]
-            height = processed_resolution[1]
-
-
-        self.total_frames = frame_count
-        self.num_threads = threads
-
-        self.processing_threads = self.num_threads
-        self.frames_queue = []
-        self.processed_queue = []
-        for _ in range(threads):
-            self.frames_queue.append(Queue(1))
-            self.processed_queue.append(Queue(1))
-
-        self.output_to_file = output_method != "Virtual Camera"
-        self.output_to_cam = output_method == "Virtual Camera" or output_method == "Both"
-
-        if self.output_to_file:
-            self.videowriter = FFMPEG_VideoWriter(target_video, (width, height), fps, codec=roop.globals.video_encoder, crf=roop.globals.video_quality, audiofile=None)
-        if self.output_to_cam:
-            self.streamwriter = StreamWriter((width, height), int(fps))
-
-        readthread = Thread(target=self.read_frames_thread, args=(cap, frame_start, frame_end, threads))
-        readthread.start()
-
-        writethread = Thread(target=self.write_frames_thread)
-        writethread.start()
-
-        progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
-        with tqdm(total=self.total_frames, desc='Processing', unit='frames', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
-            with ThreadPoolExecutor(thread_name_prefix='swap_proc', max_workers=self.num_threads) as executor:
-                futures = []
-                
-                for threadindex in range(threads):
-                    future = executor.submit(self.process_videoframes, threadindex, lambda: self.update_progress(progress))
-                    futures.append(future)
-                
-                for future in as_completed(futures):
-                    future.result()
-        # wait for the task to complete
-        readthread.join()
-        writethread.join()
-        cap.release()
-        if self.output_to_file:
-            self.videowriter.close()
-        if self.output_to_cam:
-            self.streamwriter.Close()
-
-        self.frames_queue.clear()
-        self.processed_queue.clear()
-
-
-
-
-    def update_progress(self, progress: Any = None) -> None:
-        process = psutil.Process(os.getpid())
-        memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
-        progress.set_postfix({
-            'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
-            'execution_threads': self.num_threads
-        })
-        progress.update(1)
-        if self.progress_gradio is not None:
-            self.progress_gradio((progress.n, self.total_frames), desc='Processing', total=self.total_frames, unit='frames')
-
-
-
-    def process_frame(self, frame:Frame):
-        if len(self.input_face_datas) < 1 and not self.options.show_face_masking:
-            return frame
-        temp_frame = frame.copy()
-        num_swapped, temp_frame = self.swap_faces(frame, temp_frame)
-        if num_swapped > 0:
-            if roop.globals.no_face_action == eNoFaceAction.SKIP_FRAME_IF_DISSIMILAR:
-                if len(self.input_face_datas) > num_swapped:
-                    return None
-            self.num_frames_no_face = 0
-            self.last_swapped_frame = temp_frame.copy()
-            return temp_frame
-        if roop.globals.no_face_action == eNoFaceAction.USE_LAST_SWAPPED:
-            if self.last_swapped_frame is not None and self.num_frames_no_face < self.options.max_num_reuse_frame:
-                self.num_frames_no_face += 1
-                return self.last_swapped_frame.copy()
-            return frame
-
-        elif roop.globals.no_face_action == eNoFaceAction.USE_ORIGINAL_FRAME:
-            return frame
-        if roop.globals.no_face_action == eNoFaceAction.SKIP_FRAME:
-            #This only works with in-mem processing, as it simply skips the frame.
-            #For 'extract frames' it simply leaves the unprocessed frame unprocessed and it gets used in the final output by ffmpeg.
-            #If we could delete that frame here, that'd work but that might cause ffmpeg to fail unless the frames are renamed, and I don't think we have the info on what frame it actually is?????
-            #alternatively, it could mark all the necessary frames for deletion, delete them at the end, then rename the remaining frames that might work?
-            return None
-        else:
-            return self.retry_rotated(frame)
-
-    def retry_rotated(self, frame):
-        copyframe = frame.copy()
-        copyframe = rotate_clockwise(copyframe)
-        temp_frame = copyframe.copy()
-        num_swapped, temp_frame = self.swap_faces(copyframe, temp_frame)
-        if num_swapped > 0:
-            return rotate_anticlockwise(temp_frame)
-        
-        copyframe = frame.copy()
-        copyframe = rotate_anticlockwise(copyframe)
-        temp_frame = copyframe.copy()
-        num_swapped, temp_frame = self.swap_faces(copyframe, temp_frame)
-        if num_swapped > 0:
-            return rotate_clockwise(temp_frame)
-        del copyframe
-        return frame
-        
-
-
-    def swap_faces(self, frame, temp_frame):
-        num_faces_found = 0
-
-        if self.options.swap_mode == "first":
-            face = get_first_face(frame)
-
-            if face is None:
-                return num_faces_found, frame
-            
-            num_faces_found += 1
-            temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
-            del face
-
-        else:
-            faces = get_all_faces(frame)
-            if faces is None:
-                return num_faces_found, frame
-            
-            if self.options.swap_mode == "all":
-                for face in faces:
-                    num_faces_found += 1
-                    temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
-
-            elif self.options.swap_mode == "all_input" or self.options.swap_mode == "all_random":
-                for i,face in enumerate(faces):
-                    num_faces_found += 1
-                    if i < len(self.input_face_datas):
-                        temp_frame = self.process_face(i, face, temp_frame)
-                    else:
-                        break
-            
-            elif self.options.swap_mode == "selected":
-                num_targetfaces = len(self.target_face_datas) 
-                use_index = num_targetfaces == 1
-                for i,tf in enumerate(self.target_face_datas):
-                    for face in faces:
-                        if compute_cosine_distance(tf.embedding, face.embedding) <= self.options.face_distance_threshold:
-                            if i < len(self.input_face_datas):
-                                if use_index:
-                                    temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
-                                else:
-                                    temp_frame = self.process_face(i, face, temp_frame)
-                                num_faces_found += 1
-                            if not roop.globals.vr_mode and num_faces_found == num_targetfaces:
-                                break
-            elif self.options.swap_mode == "all_female" or self.options.swap_mode == "all_male":
-                gender = 'F' if self.options.swap_mode == "all_female" else 'M'
-                for face in faces:
-                    if face.sex == gender:
-                        num_faces_found += 1
-                        temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
-            
-            # might be slower but way more clean to release everything here
-            for face in faces:
-                del face
-            faces.clear()
-
-
-
-        if roop.globals.vr_mode and num_faces_found % 2 > 0:
-            # stereo image, there has to be an even number of faces
-            num_faces_found = 0
-            return num_faces_found, frame
-        if num_faces_found == 0:
-            return num_faces_found, frame
-
-        #maskprocessor = next((x for x in self.processors if x.type == 'mask'), None)
-
-        if self.options.imagemask is not None and self.options.imagemask.shape == frame.shape:
-            temp_frame = self.simple_blend_with_mask(temp_frame, frame, self.options.imagemask)
-        return num_faces_found, temp_frame
-
-
-    def rotation_action(self, original_face:Face, frame:Frame):
-        (height, width) = frame.shape[:2]
-
-        bounding_box_width = original_face.bbox[2] - original_face.bbox[0]
-        bounding_box_height = original_face.bbox[3] - original_face.bbox[1]
-        horizontal_face = bounding_box_width > bounding_box_height
-
-        center_x = width // 2.0
-        start_x = original_face.bbox[0]
-        end_x = original_face.bbox[2]
-        bbox_center_x = start_x + (bounding_box_width // 2.0)
-
-        # need to leverage the array of landmarks as decribed here:
-        # https://github.com/deepinsight/insightface/tree/master/alignment/coordinate_reg
-        # basically, we should be able to check for the relative position of eyes and nose
-        # then use that to determine which way the face is actually facing when in a horizontal position
-        # and use that to determine the correct rotation_action
-
-        forehead_x = original_face.landmark_2d_106[72][0]
-        chin_x = original_face.landmark_2d_106[0][0]
-
-        if horizontal_face:
-            if chin_x < forehead_x:
-                # this is someone lying down with their face like this (:
-                return "rotate_anticlockwise"
-            elif forehead_x < chin_x:
-                # this is someone lying down with their face like this :)
-                return "rotate_clockwise"
-            if bbox_center_x >= center_x:
-                # this is someone lying down with their face in the right hand side of the frame
-                return "rotate_anticlockwise"
-            if bbox_center_x < center_x:
-                # this is someone lying down with their face in the left hand side of the frame
-                return "rotate_clockwise"
-
-        return None
-
-
-    def auto_rotate_frame(self, original_face, frame:Frame):
-        target_face = original_face
-        original_frame = frame
-
-        rotation_action = self.rotation_action(original_face, frame)
-
-        if rotation_action == "rotate_anticlockwise":
-            #face is horizontal, rotating frame anti-clockwise and getting face bounding box from rotated frame
-            frame = rotate_anticlockwise(frame)
-        elif rotation_action == "rotate_clockwise":
-            #face is horizontal, rotating frame clockwise and getting face bounding box from rotated frame
-            frame = rotate_clockwise(frame)
-
-        return target_face, frame, rotation_action
-    
-
-    def auto_unrotate_frame(self, frame:Frame, rotation_action):
-        if rotation_action == "rotate_anticlockwise":
-            return rotate_clockwise(frame)
-        elif rotation_action == "rotate_clockwise":
-            return rotate_anticlockwise(frame)
-        
-        return frame
-
-
-
-    def process_face(self,face_index, target_face:Face, frame:Frame):
-        from roop.face_util import align_crop
-
-        enhanced_frame = None
-        if(len(self.input_face_datas) > 0):
-            inputface = self.input_face_datas[face_index].faces[0]
-        else:
-            inputface = None
-
-        rotation_action = None
-        if roop.globals.autorotate_faces:
-            # check for sideways rotation of face
-            rotation_action = self.rotation_action(target_face, frame)
-            if rotation_action is not None:
-                (startX, startY, endX, endY) = target_face["bbox"].astype("int")
-                width = endX - startX
-                height = endY - startY
-                offs = int(max(width,height) * 0.25)
-                rotcutframe,startX, startY, endX, endY = self.cutout(frame, startX - offs, startY - offs, endX + offs, endY + offs)
-                if rotation_action == "rotate_anticlockwise":
-                    rotcutframe = rotate_anticlockwise(rotcutframe)
-                elif rotation_action == "rotate_clockwise":
-                    rotcutframe = rotate_clockwise(rotcutframe)
-                # rotate image and re-detect face to correct wonky landmarks
-                rotface = get_first_face(rotcutframe)
-                if rotface is None:
-                    rotation_action = None
-                else:
-                    saved_frame = frame.copy()
-                    frame = rotcutframe
-                    target_face = rotface
-
-
-
-        # if roop.globals.vr_mode:
-            # bbox = target_face.bbox
-            # [orig_width, orig_height, _] = frame.shape
-
-            # # Convert bounding box to ints
-            # x1, y1, x2, y2 = map(int, bbox)
-
-            # # Determine the center of the bounding box
-            # x_center = (x1 + x2) / 2
-            # y_center = (y1 + y2) / 2
-
-            # # Normalize coordinates to range [-1, 1]
-            # x_center_normalized = x_center / (orig_width / 2) - 1
-            # y_center_normalized = y_center / (orig_width / 2) - 1
-
-            # # Convert normalized coordinates to spherical (theta, phi)
-            # theta = x_center_normalized * 180  # Theta ranges from -180 to 180 degrees
-            # phi = -y_center_normalized * 90  # Phi ranges from -90 to 90 degrees
-
-            # img = vr.GetPerspective(frame, 90, theta, phi, 1280, 1280)  # Generate perspective image
-
-
-        """ Code ported/adapted from Facefusion which borrowed the idea from Rope:
-            Kind of subsampling the cutout and aligned face image and faceswapping slices of it up to
-            the desired output resolution. This works around the current resolution limitations without using enhancers.
-        """
-        model_output_size = self.options.swap_output_size
-        subsample_size = max(self.options.subsample_size, model_output_size)
-        subsample_total = subsample_size // model_output_size
-        aligned_img, M = align_crop(frame, target_face.kps, subsample_size)
-
-        fake_frame = aligned_img
-        target_face.matrix = M
-
-        for p in self.processors:
-            if p.type == 'swap':
-                swap_result_frames = []
-                subsample_frames = self.implode_pixel_boost(aligned_img, model_output_size, subsample_total)
-                for sliced_frame in subsample_frames:
-                    for _ in range(0,self.options.num_swap_steps):
-                        sliced_frame = self.prepare_crop_frame(sliced_frame)
-                        sliced_frame = p.Run(inputface, target_face, sliced_frame)
-                        sliced_frame = self.normalize_swap_frame(sliced_frame)
-                    swap_result_frames.append(sliced_frame)
-                fake_frame = self.explode_pixel_boost(swap_result_frames, model_output_size, subsample_total, subsample_size)
-                fake_frame = fake_frame.astype(np.uint8)
-                scale_factor = 0.0
-            elif p.type == 'mask':
-                fake_frame = self.process_mask(p, aligned_img, fake_frame)
-            else:
-                enhanced_frame, scale_factor = p.Run(self.input_face_datas[face_index], target_face, fake_frame)
-
-        upscale = 512
-        orig_width = fake_frame.shape[1]
-        if orig_width != upscale:
-            fake_frame = cv2.resize(fake_frame, (upscale, upscale), cv2.INTER_CUBIC)
-        mask_offsets = (0,0,0,0,1,20) if inputface is None else inputface.mask_offsets
-
-        
-        if enhanced_frame is None:
-            scale_factor = int(upscale / orig_width)
-            result = self.paste_upscale(fake_frame, fake_frame, target_face.matrix, frame, scale_factor, mask_offsets)
-        else:
-            result = self.paste_upscale(fake_frame, enhanced_frame, target_face.matrix, frame, scale_factor, mask_offsets)
-
-        # Restore mouth before unrotating
-        if self.options.restore_original_mouth:
-            mouth_cutout, mouth_bb = self.create_mouth_mask(target_face, frame)
-            result = self.apply_mouth_area(result, mouth_cutout, mouth_bb)
-
-        if rotation_action is not None:
-            fake_frame = self.auto_unrotate_frame(result, rotation_action)
-            result = self.paste_simple(fake_frame, saved_frame, startX, startY)
-        
-        return result
-
-        
-
-
-    def cutout(self, frame:Frame, start_x, start_y, end_x, end_y):
-        if start_x < 0:
-            start_x = 0
-        if start_y < 0:
-            start_y = 0
-        if end_x > frame.shape[1]:
-            end_x = frame.shape[1]
-        if end_y > frame.shape[0]:
-            end_y = frame.shape[0]
-        return frame[start_y:end_y, start_x:end_x], start_x, start_y, end_x, end_y
-
-    def paste_simple(self, src:Frame, dest:Frame, start_x, start_y):
-        end_x = start_x + src.shape[1]
-        end_y = start_y + src.shape[0]
-
-        start_x, end_x, start_y, end_y = clamp_cut_values(start_x, end_x, start_y, end_y, dest)
-        dest[start_y:end_y, start_x:end_x] = src
-        return dest
-        
-    def simple_blend_with_mask(self, image1, image2, mask):
-        # Blend the images
-        blended_image = image1.astype(np.float32) * (1.0 - mask) + image2.astype(np.float32) * mask
-        return blended_image.astype(np.uint8)
-
-
-    def paste_upscale(self, fake_face, upsk_face, M, target_img, scale_factor, mask_offsets):
-        M_scale = M * scale_factor
-        IM = cv2.invertAffineTransform(M_scale)
-
-        face_matte = np.full((target_img.shape[0],target_img.shape[1]), 255, dtype=np.uint8)
-        # Generate white square sized as a upsk_face
-        img_matte = np.zeros((upsk_face.shape[0],upsk_face.shape[1]), dtype=np.uint8)
-
-        w = img_matte.shape[1]
-        h = img_matte.shape[0]
-
-        top = int(mask_offsets[0] * h)
-        bottom = int(h - (mask_offsets[1] * h))
-        left = int(mask_offsets[2] * w)
-        right = int(w - (mask_offsets[3] * w))
-        img_matte[top:bottom,left:right] = 255
-
-        # Transform white square back to target_img
-        img_matte = cv2.warpAffine(img_matte, IM, (target_img.shape[1], target_img.shape[0]), flags=cv2.INTER_NEAREST, borderValue=0.0) 
-        ##Blacken the edges of face_matte by 1 pixels (so the mask in not expanded on the image edges)
-        img_matte[:1,:] = img_matte[-1:,:] = img_matte[:,:1] = img_matte[:,-1:] = 0
-
-        img_matte = self.blur_area(img_matte, mask_offsets[4], mask_offsets[5])
-        #Normalize images to float values and reshape
-        img_matte = img_matte.astype(np.float32)/255
-        face_matte = face_matte.astype(np.float32)/255
-        img_matte = np.minimum(face_matte, img_matte)
-        if self.options.show_face_area_overlay:
-            # Additional steps for green overlay
-            green_overlay = np.zeros_like(target_img)
-            green_color = [0, 255, 0]  # RGB for green
-            for i in range(3):  # Apply green color where img_matte is not zero
-                green_overlay[:, :, i] = np.where(img_matte > 0, green_color[i], 0)        ##Transform upcaled face back to target_img
-        img_matte = np.reshape(img_matte, [img_matte.shape[0],img_matte.shape[1],1]) 
-        paste_face = cv2.warpAffine(upsk_face, IM, (target_img.shape[1], target_img.shape[0]), borderMode=cv2.BORDER_REPLICATE)
-        if upsk_face is not fake_face:
-            fake_face = cv2.warpAffine(fake_face, IM, (target_img.shape[1], target_img.shape[0]), borderMode=cv2.BORDER_REPLICATE)
-            paste_face = cv2.addWeighted(paste_face, self.options.blend_ratio, fake_face, 1.0 - self.options.blend_ratio, 0)
-
-        # Re-assemble image
-        paste_face = img_matte * paste_face
-        paste_face = paste_face + (1-img_matte) * target_img.astype(np.float32)
-        if self.options.show_face_area_overlay:
-            # Overlay the green overlay on the final image
-            paste_face = cv2.addWeighted(paste_face.astype(np.uint8), 1 - 0.5, green_overlay, 0.5, 0)
-        return paste_face.astype(np.uint8)
-
-
-    def blur_area(self, img_matte, num_erosion_iterations, blur_amount):
-        # Detect the affine transformed white area
-        mask_h_inds, mask_w_inds = np.where(img_matte==255) 
-        # Calculate the size (and diagonal size) of transformed white area width and height boundaries
-        mask_h = np.max(mask_h_inds) - np.min(mask_h_inds) 
-        mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
-        mask_size = int(np.sqrt(mask_h*mask_w))
-        # Calculate the kernel size for eroding img_matte by kernel (insightface empirical guess for best size was max(mask_size//10,10))
-        # k = max(mask_size//12, 8)
-        k = max(mask_size//(blur_amount // 2) , blur_amount // 2)
-        kernel = np.ones((k,k),np.uint8)
-        img_matte = cv2.erode(img_matte,kernel,iterations = num_erosion_iterations)
-        #Calculate the kernel size for blurring img_matte by blur_size (insightface empirical guess for best size was max(mask_size//20, 5))
-        # k = max(mask_size//24, 4) 
-        k = max(mask_size//blur_amount, blur_amount//5) 
-        kernel_size = (k, k)
-        blur_size = tuple(2*i+1 for i in kernel_size)
-        return cv2.GaussianBlur(img_matte, blur_size, 0)
-
-
-    def prepare_crop_frame(self, swap_frame):
-        model_type = 'inswapper'
-        model_mean = [0.0, 0.0, 0.0]
-        model_standard_deviation = [1.0, 1.0, 1.0]
-
-        if model_type == 'ghost':
-            swap_frame = swap_frame[:, :, ::-1] / 127.5 - 1
-        else:
-            swap_frame = swap_frame[:, :, ::-1] / 255.0
-        swap_frame = (swap_frame - model_mean) / model_standard_deviation
-        swap_frame = swap_frame.transpose(2, 0, 1)
-        swap_frame = np.expand_dims(swap_frame, axis = 0).astype(np.float32)
-        return swap_frame
-
-
-    def normalize_swap_frame(self, swap_frame):
-        model_type = 'inswapper'
-        swap_frame = swap_frame.transpose(1, 2, 0)
-
-        if model_type == 'ghost':
-            swap_frame = (swap_frame * 127.5 + 127.5).round()
-        else:
-            swap_frame = (swap_frame * 255.0).round()
-        swap_frame = swap_frame[:, :, ::-1]
-        return swap_frame
-
-    def implode_pixel_boost(self, aligned_face_frame, model_size, pixel_boost_total : int):
-        subsample_frame = aligned_face_frame.reshape(model_size, pixel_boost_total, model_size, pixel_boost_total, 3)
-        subsample_frame = subsample_frame.transpose(1, 3, 0, 2, 4).reshape(pixel_boost_total ** 2, model_size, model_size, 3)
-        return subsample_frame
-
-
-    def explode_pixel_boost(self, subsample_frame, model_size, pixel_boost_total, pixel_boost_size):
-        final_frame = np.stack(subsample_frame, axis = 0).reshape(pixel_boost_total, pixel_boost_total, model_size, model_size, 3)
-        final_frame = final_frame.transpose(2, 0, 3, 1, 4).reshape(pixel_boost_size, pixel_boost_size, 3)
-        return final_frame
-
-    def process_mask(self, processor, frame:Frame, target:Frame):
-        img_mask = processor.Run(frame, self.options.masking_text)
-        img_mask = cv2.resize(img_mask, (target.shape[1], target.shape[0]))
-        img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
-
-        if self.options.show_face_masking:
-            result = (1 - img_mask) * frame.astype(np.float32)
-            return np.uint8(result)
-
-
-        target = target.astype(np.float32)
-        result = (1-img_mask) * target
-        result += img_mask * frame.astype(np.float32)
-        return np.uint8(result)
-
-
-    # Code for mouth restoration adapted from https://github.com/iVideoGameBoss/iRoopDeepFaceCam
-    
-    def create_mouth_mask(self, face: Face, frame: Frame):
-        mouth_cutout = None
-        
-        landmarks = face.landmark_2d_106
-        if landmarks is not None:
-            # Get mouth landmarks (indices 52 to 71 typically represent the outer mouth)
-            mouth_points = landmarks[52:71].astype(np.int32)
-            
-            # Add padding to mouth area
-            min_x, min_y = np.min(mouth_points, axis=0)
-            max_x, max_y = np.max(mouth_points, axis=0)
-            min_x = max(0, min_x - (15*6))
-            min_y = max(0, min_y - 22)
-            max_x = min(frame.shape[1], max_x + (15*6))
-            max_y = min(frame.shape[0], max_y + (90*6))
-            
-            # Extract the mouth area from the frame using the calculated bounding box
-            mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
-
-        return mouth_cutout, (min_x, min_y, max_x, max_y)
-
-
-
-    def create_feathered_mask(self, shape, feather_amount=30):
-        mask = np.zeros(shape[:2], dtype=np.float32)
-        center = (shape[1] // 2, shape[0] // 2)
-        cv2.ellipse(mask, center, (shape[1] // 2 - feather_amount, shape[0] // 2 - feather_amount), 
-                    0, 0, 360, 1, -1)
-        mask = cv2.GaussianBlur(mask, (feather_amount*2+1, feather_amount*2+1), 0)
-        return mask / np.max(mask)
-
-    def apply_mouth_area(self, frame: np.ndarray, mouth_cutout: np.ndarray, mouth_box: tuple) -> np.ndarray:
-        min_x, min_y, max_x, max_y = mouth_box
-        box_width = max_x - min_x
-        box_height = max_y - min_y
-        
-
-        # Resize the mouth cutout to match the mouth box size
-        if mouth_cutout is None or box_width is None or box_height is None:
-            return frame
-        try:
-            resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
-            
-            # Extract the region of interest (ROI) from the target frame
-            roi = frame[min_y:max_y, min_x:max_x]
-            
-            # Ensure the ROI and resized_mouth_cutout have the same shape
-            if roi.shape != resized_mouth_cutout.shape:
-                resized_mouth_cutout = cv2.resize(resized_mouth_cutout, (roi.shape[1], roi.shape[0]))
-            
-            # Apply color transfer from ROI to mouth cutout
-            color_corrected_mouth = self.apply_color_transfer(resized_mouth_cutout, roi)
-            
-            # Create a feathered mask with increased feather amount
-            feather_amount = min(30, box_width // 15, box_height // 15)
-            mask = self.create_feathered_mask(resized_mouth_cutout.shape, feather_amount)
-            
-            # Blend the color-corrected mouth cutout with the ROI using the feathered mask
-            mask = mask[:,:,np.newaxis]  # Add channel dimension to mask
-            blended = (color_corrected_mouth * mask + roi * (1 - mask)).astype(np.uint8)
-            
-            # Place the blended result back into the frame
-            frame[min_y:max_y, min_x:max_x] = blended
-        except Exception as e:
-            print(f'Error {e}')
-            pass
-
-        return frame
-
-    def apply_color_transfer(self, source, target):
-        """
-        Apply color transfer from target to source image
-        """
-        source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
-        target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
-
-        source_mean, source_std = cv2.meanStdDev(source)
-        target_mean, target_std = cv2.meanStdDev(target)
-
-        # Reshape mean and std to be broadcastable
-        source_mean = source_mean.reshape(1, 1, 3)
-        source_std = source_std.reshape(1, 1, 3)
-        target_mean = target_mean.reshape(1, 1, 3)
-        target_std = target_std.reshape(1, 1, 3)
-
-        # Perform the color transfer
-        source = (source - source_mean) * (target_std / source_std) + target_mean
-        return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
-
-
-
-    def unload_models():
-        pass
-
-
-    def release_resources(self):
-        for p in self.processors:
-            p.Release()
-        self.processors.clear()
-        if self.videowriter is not None:
-            self.videowriter.close()
-        if self.streamwriter is not None:
-            self.streamwriter.Close()
-

roop-unleashed-main/roop/ProcessOptions.py

@@ -1,18 +0,0 @@
-class ProcessOptions:
-
-    def __init__(self, swap_model, processordefines:dict, face_distance,  blend_ratio, swap_mode, selected_index, masking_text, imagemask, num_steps, subsample_size, show_face_area, restore_original_mouth, show_mask=False):
-        self.swap_modelname = swap_model
-        self.swap_output_size = int(swap_model.split()[-1])
-        self.processors = processordefines
-        self.face_distance_threshold = face_distance
-        self.blend_ratio = blend_ratio
-        self.swap_mode = swap_mode
-        self.selected_index = selected_index
-        self.masking_text = masking_text
-        self.imagemask = imagemask
-        self.num_swap_steps = num_steps
-        self.show_face_area_overlay = show_face_area
-        self.show_face_masking = show_mask
-        self.subsample_size = subsample_size
-        self.restore_original_mouth = restore_original_mouth
-        self.max_num_reuse_frame = 15

roop-unleashed-main/roop/StreamWriter.py

@@ -1,60 +0,0 @@
-import threading
-import time
-import pyvirtualcam
-
-
-class StreamWriter():
-    FPS = 30
-    VCam = None
-    Active = False
-    THREAD_LOCK_STREAM = threading.Lock()
-    time_last_process = None
-    timespan_min = 0.0
-    
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.Close()
-
-    def __init__(self, size, fps):
-        self.time_last_process = time.perf_counter()
-        self.FPS = fps
-        self.timespan_min = 1.0 / fps
-        print('Detecting virtual cam devices')
-        self.VCam = pyvirtualcam.Camera(width=size[0], height=size[1], fps=fps, fmt=pyvirtualcam.PixelFormat.BGR, print_fps=False)
-        if self.VCam is None:
-             print("No virtual camera found!")
-             return
-        print(f'Using virtual camera: {self.VCam.device}')
-        print(f'Using {self.VCam.native_fmt}')
-        self.Active = True
-         
-
-    def LimitFrames(self):
-        while True:
-            current_time = time.perf_counter()
-            time_passed = current_time - self.time_last_process
-            if time_passed >= self.timespan_min:
-                break
-
-    # First version used a queue and threading. Surprisingly this
-    # totally simple, blocking version is 10 times faster!
-    def WriteToStream(self, frame):
-        if self.VCam is None:
-             return
-        with self.THREAD_LOCK_STREAM:
-            self.LimitFrames()
-            self.VCam.send(frame)
-            self.time_last_process = time.perf_counter()
-             
-
-    def Close(self):
-        self.Active = False
-        if self.VCam is None:
-            self.VCam.close()
-            self.VCam = None
-
-
-
-

roop-unleashed-main/roop/capturer.py

@@ -1,46 +0,0 @@
-from typing import Optional
-import cv2
-import numpy as np
-
-from roop.typing import Frame
-
-current_video_path = None
-current_frame_total = 0
-current_capture = None
-
-def get_image_frame(filename: str):
-    try:
-        return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)
-    except:
-        print(f"Exception reading {filename}")
-    return None
-
-    
-def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Frame]:
-    global current_video_path, current_capture, current_frame_total
-
-    if video_path != current_video_path:
-        release_video()
-        current_capture = cv2.VideoCapture(video_path)
-        current_video_path = video_path
-        current_frame_total = current_capture.get(cv2.CAP_PROP_FRAME_COUNT)
-
-    current_capture.set(cv2.CAP_PROP_POS_FRAMES, min(current_frame_total, frame_number - 1))
-    has_frame, frame = current_capture.read()
-    if has_frame:
-        return frame
-    return None
-
-def release_video():
-    global current_capture    
-
-    if current_capture is not None:
-        current_capture.release()
-        current_capture = None
-        
-
-def get_video_frame_total(video_path: str) -> int:
-    capture = cv2.VideoCapture(video_path)
-    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
-    capture.release()
-    return video_frame_total

roop-unleashed-main/roop/core.py

@@ -1,406 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-import shutil
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-
-import warnings
-from typing import List
-import platform
-import signal
-import torch
-import onnxruntime
-import pathlib
-import argparse
-
-from time import time
-
-import roop.globals
-import roop.metadata
-import roop.utilities as util
-import roop.util_ffmpeg as ffmpeg
-import ui.main as main
-from settings import Settings
-from roop.face_util import extract_face_images
-from roop.ProcessEntry import ProcessEntry
-from roop.ProcessMgr import ProcessMgr
-from roop.ProcessOptions import ProcessOptions
-from roop.capturer import get_video_frame_total, release_video
-
-
-clip_text = None
-
-call_display_ui = None
-
-process_mgr = None
-
-
-if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-    del torch
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
-    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-    roop.globals.headless = False
-
-    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
-    program.add_argument('--server_share', help='Public server', dest='server_share', action='store_true', default=False)
-    program.add_argument('--cuda_device_id', help='Index of the cuda gpu to use', dest='cuda_device_id', type=int, default=0)
-    roop.globals.startup_args = program.parse_args()
-    # Always enable all processors when using GUI
-    roop.globals.frame_processors = ['face_swapper', 'face_enhancer']
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    list_providers = [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-    
-    try:
-        for i in range(len(list_providers)):
-            if list_providers[i] == 'CUDAExecutionProvider':
-                list_providers[i] = ('CUDAExecutionProvider', {'device_id': roop.globals.cuda_device_id})
-                torch.cuda.set_device(roop.globals.cuda_device_id)
-                break
-    except:
-        pass
-
-    return list_providers
-    
-
-
-def suggest_max_memory() -> int:
-    if platform.system().lower() == 'darwin':
-        return 4
-    return 16
-
-
-def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    return 8
-
-
-def limit_resources() -> None:
-    # limit memory usage
-    if roop.globals.max_memory:
-        memory = roop.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = roop.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
-            import ctypes
-            kernel32 = ctypes.windll.kernel32  # type: ignore[attr-defined]
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-
-def release_resources() -> None:
-    import gc
-    global process_mgr
-
-    if process_mgr is not None:
-        process_mgr.release_resources()
-        process_mgr = None
-
-    gc.collect()
-    # if 'CUDAExecutionProvider' in roop.globals.execution_providers and torch.cuda.is_available():
-    #     with torch.cuda.device('cuda'):
-    #         torch.cuda.empty_cache()
-    #         torch.cuda.ipc_collect()
-
-
-def pre_check() -> bool:
-    if sys.version_info < (3, 9):
-        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
-        return False
-    
-    download_directory_path = util.resolve_relative_path('../models')
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/reswapper_128.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/reswapper_256.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GFPGANv1.4.onnx'])
-    util.conditional_download(download_directory_path, ['https://github.com/csxmli2016/DMDNet/releases/download/v1/DMDNet.pth'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GPEN-BFR-512.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/restoreformer_plus_plus.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/xseg.onnx'])
-    download_directory_path = util.resolve_relative_path('../models/CLIP')
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/rd64-uni-refined.pth'])
-    download_directory_path = util.resolve_relative_path('../models/CodeFormer')
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/CodeFormerv0.1.onnx'])
-    download_directory_path = util.resolve_relative_path('../models/Frame')
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_artistic.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_stable.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/isnet-general-use.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x4.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x2.onnx'])
-    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/lsdir_x4.onnx'])
-
-    if not shutil.which('ffmpeg'):
-       update_status('ffmpeg is not installed.')
-    return True
-
-def set_display_ui(function):
-    global call_display_ui
-
-    call_display_ui = function
-
-
-def update_status(message: str) -> None:
-    global call_display_ui
-
-    print(message)
-    if call_display_ui is not None:
-        call_display_ui(message)
-
-
-
-
-def start() -> None:
-    if roop.globals.headless:
-        print('Headless mode currently unsupported - starting UI!')
-        # faces = extract_face_images(roop.globals.source_path,  (False, 0))
-        # roop.globals.INPUT_FACES.append(faces[roop.globals.source_face_index])
-        # faces = extract_face_images(roop.globals.target_path,  (False, util.has_image_extension(roop.globals.target_path)))
-        # roop.globals.TARGET_FACES.append(faces[roop.globals.target_face_index])
-        # if 'face_enhancer' in roop.globals.frame_processors:
-        #     roop.globals.selected_enhancer = 'GFPGAN'
-       
-    batch_process_regular(None, False, None)
-
-
-def get_processing_plugins(masking_engine):
-    processors = {  "faceswap": {}}
-    if masking_engine is not None:
-        processors.update({masking_engine: {}})
-    
-    if roop.globals.selected_enhancer == 'GFPGAN':
-        processors.update({"gfpgan": {}})
-    elif roop.globals.selected_enhancer == 'Codeformer':
-        processors.update({"codeformer": {}})
-    elif roop.globals.selected_enhancer == 'DMDNet':
-        processors.update({"dmdnet": {}})
-    elif roop.globals.selected_enhancer == 'GPEN':
-        processors.update({"gpen": {}})
-    elif roop.globals.selected_enhancer == 'Restoreformer++':
-        processors.update({"restoreformer++": {}})
-    return processors
-
-
-def live_swap(frame, options):
-    global process_mgr
-
-    if frame is None:
-        return frame
-
-    if process_mgr is None:
-        process_mgr = ProcessMgr(None)
-    
-#    if len(roop.globals.INPUT_FACESETS) <= selected_index:
-#        selected_index = 0
-    process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
-    newframe = process_mgr.process_frame(frame)
-    if newframe is None:
-        return frame
-    return newframe
-
-
-def batch_process_regular(swap_model, output_method, files:list[ProcessEntry], masking_engine:str, new_clip_text:str, use_new_method, imagemask, restore_original_mouth, num_swap_steps, progress, selected_index = 0) -> None:
-    global clip_text, process_mgr
-
-    release_resources()
-    limit_resources()
-    if process_mgr is None:
-        process_mgr = ProcessMgr(progress)
-    mask = imagemask["layers"][0] if imagemask is not None else None
-    if len(roop.globals.INPUT_FACESETS) <= selected_index:
-        selected_index = 0
-    options = ProcessOptions(swap_model, get_processing_plugins(masking_engine), roop.globals.distance_threshold, roop.globals.blend_ratio,
-                              roop.globals.face_swap_mode, selected_index, new_clip_text, mask, num_swap_steps,
-                              roop.globals.subsample_size, False, restore_original_mouth)
-    process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
-    batch_process(output_method, files, use_new_method)
-    return
-
-def batch_process_with_options(files:list[ProcessEntry], options, progress):
-    global clip_text, process_mgr
-
-    release_resources()
-    limit_resources()
-    if process_mgr is None:
-        process_mgr = ProcessMgr(progress)
-    process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
-    roop.globals.keep_frames = False
-    roop.globals.wait_after_extraction = False
-    roop.globals.skip_audio = False
-    batch_process("Files", files, True)
-
-
-
-def batch_process(output_method, files:list[ProcessEntry], use_new_method) -> None:
-    global clip_text, process_mgr
-
-    roop.globals.processing = True
-
-    # limit threads for some providers
-    max_threads = suggest_execution_threads()
-    if max_threads == 1:
-        roop.globals.execution_threads = 1
-
-    imagefiles:list[ProcessEntry] = []
-    videofiles:list[ProcessEntry] = []
-           
-    update_status('Sorting videos/images')
-
-
-    for index, f in enumerate(files):
-        fullname = f.filename
-        if util.has_image_extension(fullname):
-            destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'.{roop.globals.CFG.output_image_format}')
-            destination = util.replace_template(destination, index=index)
-            pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
-            f.finalname = destination
-            imagefiles.append(f)
-
-        elif util.is_video(fullname) or util.has_extension(fullname, ['gif']):
-            destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'__temp.{roop.globals.CFG.output_video_format}')
-            f.finalname = destination
-            videofiles.append(f)
-
-
-
-    if(len(imagefiles) > 0):
-        update_status('Processing image(s)')
-        origimages = []
-        fakeimages = []
-        for f in imagefiles:
-            origimages.append(f.filename)
-            fakeimages.append(f.finalname)
-
-        process_mgr.run_batch(origimages, fakeimages, roop.globals.execution_threads)
-        origimages.clear()
-        fakeimages.clear()
-
-    if(len(videofiles) > 0):
-        for index,v in enumerate(videofiles):
-            if not roop.globals.processing:
-                end_processing('Processing stopped!')
-                return
-            fps = v.fps if v.fps > 0 else util.detect_fps(v.filename)
-            if v.endframe == 0:
-                v.endframe = get_video_frame_total(v.filename)
-
-            is_streaming_only = output_method == "Virtual Camera"
-            if is_streaming_only == False:
-                update_status(f'Creating {os.path.basename(v.finalname)} with {fps} FPS...')
-
-            start_processing = time()
-            if is_streaming_only == False and roop.globals.keep_frames or not use_new_method:
-                util.create_temp(v.filename)
-                update_status('Extracting frames...')
-                ffmpeg.extract_frames(v.filename,v.startframe,v.endframe, fps)
-                if not roop.globals.processing:
-                    end_processing('Processing stopped!')
-                    return
-
-                temp_frame_paths = util.get_temp_frame_paths(v.filename)
-                process_mgr.run_batch(temp_frame_paths, temp_frame_paths, roop.globals.execution_threads)
-                if not roop.globals.processing:
-                    end_processing('Processing stopped!')
-                    return
-                if roop.globals.wait_after_extraction:
-                    extract_path = os.path.dirname(temp_frame_paths[0])
-                    util.open_folder(extract_path)
-                    input("Press any key to continue...")
-                    print("Resorting frames to create video")
-                    util.sort_rename_frames(extract_path)                                    
-                
-                ffmpeg.create_video(v.filename, v.finalname, fps)
-                if not roop.globals.keep_frames:
-                    util.delete_temp_frames(temp_frame_paths[0])
-            else:
-                if util.has_extension(v.filename, ['gif']):
-                    skip_audio = True
-                else:
-                    skip_audio = roop.globals.skip_audio
-                process_mgr.run_batch_inmem(output_method, v.filename, v.finalname, v.startframe, v.endframe, fps,roop.globals.execution_threads)
-                
-            if not roop.globals.processing:
-                end_processing('Processing stopped!')
-                return
-            
-            video_file_name = v.finalname
-            if os.path.isfile(video_file_name):
-                destination = ''
-                if util.has_extension(v.filename, ['gif']):
-                    gifname = util.get_destfilename_from_path(v.filename, roop.globals.output_path, '.gif')
-                    destination = util.replace_template(gifname, index=index)
-                    pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
-
-                    update_status('Creating final GIF')
-                    ffmpeg.create_gif_from_video(video_file_name, destination)
-                    if os.path.isfile(destination):
-                        os.remove(video_file_name)
-                else:
-                    skip_audio = roop.globals.skip_audio
-                    destination = util.replace_template(video_file_name, index=index)
-                    pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
-
-                    if not skip_audio:
-                        ffmpeg.restore_audio(video_file_name, v.filename, v.startframe, v.endframe, destination)
-                        if os.path.isfile(destination):
-                            os.remove(video_file_name)
-                    else:
-                        shutil.move(video_file_name, destination)
-
-            elif is_streaming_only == False:
-                update_status(f'Failed processing {os.path.basename(v.finalname)}!')
-            elapsed_time = time() - start_processing
-            average_fps = (v.endframe - v.startframe) / elapsed_time
-            update_status(f'\nProcessing {os.path.basename(destination)} took {elapsed_time:.2f} secs, {average_fps:.2f} frames/s')
-    end_processing('Finished')
-
-
-def end_processing(msg:str):
-    update_status(msg)
-    roop.globals.target_folder_path = None
-    release_resources()
-
-
-def destroy() -> None:
-    if roop.globals.target_path:
-        util.clean_temp(roop.globals.target_path)
-    release_resources()        
-    sys.exit()
-
-
-def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    roop.globals.CFG = Settings('config.yaml')
-    roop.globals.cuda_device_id = roop.globals.startup_args.cuda_device_id
-    roop.globals.execution_threads = roop.globals.CFG.max_threads
-    roop.globals.video_encoder = roop.globals.CFG.output_video_codec
-    roop.globals.video_quality = roop.globals.CFG.video_quality
-    roop.globals.max_memory = roop.globals.CFG.memory_limit if roop.globals.CFG.memory_limit > 0 else None
-    if roop.globals.startup_args.server_share:
-        roop.globals.CFG.server_share = True
-    main.run()

roop-unleashed-main/roop/face_util.py

@@ -1,338 +0,0 @@
-import threading
-from typing import Any
-import insightface
-
-import roop.globals
-from roop.typing import Frame, Face
-
-import cv2
-import numpy as np
-from skimage import transform as trans
-from roop.capturer import get_video_frame
-from roop.utilities import resolve_relative_path, conditional_thread_semaphore
-
-FACE_ANALYSER = None
-#THREAD_LOCK_ANALYSER = threading.Lock()
-#THREAD_LOCK_SWAPPER = threading.Lock()
-FACE_SWAPPER = None
-
-
-def get_face_analyser() -> Any:
-    global FACE_ANALYSER
-
-    with conditional_thread_semaphore():
-        if FACE_ANALYSER is None or roop.globals.g_current_face_analysis != roop.globals.g_desired_face_analysis:
-            model_path = resolve_relative_path('..')
-            # removed genderage
-            allowed_modules = roop.globals.g_desired_face_analysis
-            roop.globals.g_current_face_analysis = roop.globals.g_desired_face_analysis
-            if roop.globals.CFG.force_cpu:
-                print("Forcing CPU for Face Analysis")
-                FACE_ANALYSER = insightface.app.FaceAnalysis(
-                    name="buffalo_l",
-                    root=model_path, providers=["CPUExecutionProvider"],allowed_modules=allowed_modules
-                )
-            else:
-                FACE_ANALYSER = insightface.app.FaceAnalysis(
-                    name="buffalo_l", root=model_path, providers=roop.globals.execution_providers,allowed_modules=allowed_modules
-                )
-            FACE_ANALYSER.prepare(
-                ctx_id=0,
-                det_size=(640, 640) if roop.globals.default_det_size else (320, 320),
-            )
-    return FACE_ANALYSER
-
-
-def get_first_face(frame: Frame) -> Any:
-    try:
-        faces = get_face_analyser().get(frame)
-        return min(faces, key=lambda x: x.bbox[0])
-    #   return sorted(faces, reverse=True, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))[0]
-    except:
-        return None
-
-
-def get_all_faces(frame: Frame) -> Any:
-    try:
-        faces = get_face_analyser().get(frame)
-        return sorted(faces, key=lambda x: x.bbox[0])
-    except:
-        return None
-
-
-def extract_face_images(source_filename, video_info, extra_padding=-1.0):
-    face_data = []
-    source_image = None
-
-    if video_info[0]:
-        frame = get_video_frame(source_filename, video_info[1])
-        if frame is not None:
-            source_image = frame
-        else:
-            return face_data
-    else:
-        source_image = cv2.imdecode(np.fromfile(source_filename, dtype=np.uint8), cv2.IMREAD_COLOR)
-
-    faces = get_all_faces(source_image)
-    if faces is None:
-        return face_data
-
-    i = 0
-    for face in faces:
-        (startX, startY, endX, endY) = face["bbox"].astype("int")
-        startX, endX, startY, endY = clamp_cut_values(startX, endX, startY, endY, source_image)
-        if extra_padding > 0.0:
-            if source_image.shape[:2] == (512, 512):
-                i += 1
-                face_data.append([face, source_image])
-                continue
-
-            found = False
-            for i in range(1, 3):
-                (startX, startY, endX, endY) = face["bbox"].astype("int")
-                startX, endX, startY, endY = clamp_cut_values(startX, endX, startY, endY, source_image)
-                cutout_padding = extra_padding
-                # top needs extra room for detection
-                padding = int((endY - startY) * cutout_padding)
-                oldY = startY
-                startY -= padding
-
-                factor = 0.25 if i == 1 else 0.5
-                cutout_padding = factor
-                padding = int((endY - oldY) * cutout_padding)
-                endY += padding
-                padding = int((endX - startX) * cutout_padding)
-                startX -= padding
-                endX += padding
-                startX, endX, startY, endY = clamp_cut_values(
-                    startX, endX, startY, endY, source_image
-                )
-                face_temp = source_image[startY:endY, startX:endX]
-                face_temp = resize_image_keep_content(face_temp)
-                testfaces = get_all_faces(face_temp)
-                if testfaces is not None and len(testfaces) > 0:
-                    i += 1
-                    face_data.append([testfaces[0], face_temp])
-                    found = True
-                    break
-
-            if not found:
-                print("No face found after resizing, this shouldn't happen!")
-            continue
-
-        face_temp = source_image[startY:endY, startX:endX]
-        if face_temp.size < 1:
-            continue
-
-        i += 1
-        face_data.append([face, face_temp])
-    return face_data
-
-
-def clamp_cut_values(startX, endX, startY, endY, image):
-    if startX < 0:
-        startX = 0
-    if endX > image.shape[1]:
-        endX = image.shape[1]
-    if startY < 0:
-        startY = 0
-    if endY > image.shape[0]:
-        endY = image.shape[0]
-    return startX, endX, startY, endY
-
-
-
-def face_offset_top(face: Face, offset):
-    face["bbox"][1] += offset
-    face["bbox"][3] += offset
-    lm106 = face.landmark_2d_106
-    add = np.full_like(lm106, [0, offset])
-    face["landmark_2d_106"] = lm106 + add
-    return face
-
-
-def resize_image_keep_content(image, new_width=512, new_height=512):
-    dim = None
-    (h, w) = image.shape[:2]
-    if h > w:
-        r = new_height / float(h)
-        dim = (int(w * r), new_height)
-    else:
-        # Calculate the ratio of the width and construct the dimensions
-        r = new_width / float(w)
-        dim = (new_width, int(h * r))
-    image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
-    (h, w) = image.shape[:2]
-    if h == new_height and w == new_width:
-        return image
-    resize_img = np.zeros(shape=(new_height, new_width, 3), dtype=image.dtype)
-    offs = (new_width - w) if h == new_height else (new_height - h)
-    startoffs = int(offs // 2) if offs % 2 == 0 else int(offs // 2) + 1
-    offs = int(offs // 2)
-
-    if h == new_height:
-        resize_img[0:new_height, startoffs : new_width - offs] = image
-    else:
-        resize_img[startoffs : new_height - offs, 0:new_width] = image
-    return resize_img
-
-
-def rotate_image_90(image, rotate=True):
-    if rotate:
-        return np.rot90(image)
-    else:
-        return np.rot90(image, 1, (1, 0))
-
-
-def rotate_anticlockwise(frame):
-    return rotate_image_90(frame)
-
-
-def rotate_clockwise(frame):
-    return rotate_image_90(frame, False)
-
-
-def rotate_image_180(image):
-    return np.flip(image, 0)
-
-
-# alignment code from insightface https://github.com/deepinsight/insightface/blob/master/python-package/insightface/utils/face_align.py
-
-arcface_dst = np.array(
-    [
-        [38.2946, 51.6963],
-        [73.5318, 51.5014],
-        [56.0252, 71.7366],
-        [41.5493, 92.3655],
-        [70.7299, 92.2041],
-    ],
-    dtype=np.float32,
-)
-
-
-""" def estimate_norm(lmk, image_size=112):
-    assert lmk.shape == (5, 2)
-    if image_size % 112 == 0:
-        ratio = float(image_size) / 112.0
-        diff_x = 0
-    elif image_size % 128 == 0:
-        ratio = float(image_size) / 128.0
-        diff_x = 8.0 * ratio
-    elif image_size % 512 == 0:
-        ratio = float(image_size) / 512.0
-        diff_x = 32.0 * ratio
-
-    dst = arcface_dst * ratio
-    dst[:, 0] += diff_x
-    tform = trans.SimilarityTransform()
-    tform.estimate(lmk, dst)
-    M = tform.params[0:2, :]
-    return M
- """
-
-def estimate_norm(lmk, image_size=112):
-    if image_size%112==0:
-        ratio = float(image_size)/112.0
-        diff_x = 0
-    else:
-        ratio = float(image_size)/128.0
-        diff_x = 8.0*ratio
-    dst = arcface_dst * ratio
-    dst[:,0] += diff_x
-
-    if image_size == 160:
-        dst[:,0] += 0.1
-        dst[:,1] += 0.1
-    elif image_size == 256:
-        dst[:,0] += 0.5
-        dst[:,1] += 0.5
-    elif image_size == 320:
-        dst[:,0] += 0.75
-        dst[:,1] += 0.75
-    elif image_size == 512:
-        dst[:,0] += 1.5
-        dst[:,1] += 1.5
-
-    tform = trans.SimilarityTransform()
-    tform.estimate(lmk, dst)
-    M = tform.params[0:2, :]
-    return M
-
-
-
-# aligned, M = norm_crop2(f[1], face.kps, 512)
-def align_crop(img, landmark, image_size=112, mode="arcface"):
-    M = estimate_norm(landmark, image_size)
-    warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
-    return warped, M
-
-
-def square_crop(im, S):
-    if im.shape[0] > im.shape[1]:
-        height = S
-        width = int(float(im.shape[1]) / im.shape[0] * S)
-        scale = float(S) / im.shape[0]
-    else:
-        width = S
-        height = int(float(im.shape[0]) / im.shape[1] * S)
-        scale = float(S) / im.shape[1]
-    resized_im = cv2.resize(im, (width, height))
-    det_im = np.zeros((S, S, 3), dtype=np.uint8)
-    det_im[: resized_im.shape[0], : resized_im.shape[1], :] = resized_im
-    return det_im, scale
-
-
-def transform(data, center, output_size, scale, rotation):
-    scale_ratio = scale
-    rot = float(rotation) * np.pi / 180.0
-    # translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
-    t1 = trans.SimilarityTransform(scale=scale_ratio)
-    cx = center[0] * scale_ratio
-    cy = center[1] * scale_ratio
-    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
-    t3 = trans.SimilarityTransform(rotation=rot)
-    t4 = trans.SimilarityTransform(translation=(output_size / 2, output_size / 2))
-    t = t1 + t2 + t3 + t4
-    M = t.params[0:2]
-    cropped = cv2.warpAffine(data, M, (output_size, output_size), borderValue=0.0)
-    return cropped, M
-
-
-def trans_points2d(pts, M):
-    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
-    for i in range(pts.shape[0]):
-        pt = pts[i]
-        new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
-        new_pt = np.dot(M, new_pt)
-        # print('new_pt', new_pt.shape, new_pt)
-        new_pts[i] = new_pt[0:2]
-
-    return new_pts
-
-
-def trans_points3d(pts, M):
-    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
-    # print(scale)
-    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
-    for i in range(pts.shape[0]):
-        pt = pts[i]
-        new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
-        new_pt = np.dot(M, new_pt)
-        # print('new_pt', new_pt.shape, new_pt)
-        new_pts[i][0:2] = new_pt[0:2]
-        new_pts[i][2] = pts[i][2] * scale
-
-    return new_pts
-
-
-def trans_points(pts, M):
-    if pts.shape[1] == 2:
-        return trans_points2d(pts, M)
-    else:
-        return trans_points3d(pts, M)
-    
-def create_blank_image(width, height):
-    img = np.zeros((height, width, 4), dtype=np.uint8)
-    img[:] = [0,0,0,0]
-    return img
-

roop-unleashed-main/roop/ffmpeg_writer.py

@@ -1,218 +0,0 @@
-"""
-FFMPEG_Writer - write set of frames to video file
-
-original from
-https://github.com/Zulko/moviepy/blob/master/moviepy/video/io/ffmpeg_writer.py
-
-removed unnecessary dependencies
-
-The MIT License (MIT)
-
-Copyright (c) 2015 Zulko
-Copyright (c) 2023 Janvarev Vladislav
-"""
-
-import os
-import subprocess as sp
-
-PIPE = -1
-STDOUT = -2
-DEVNULL = -3
-
-FFMPEG_BINARY = "ffmpeg"
-
-class FFMPEG_VideoWriter:
-    """ A class for FFMPEG-based video writing.
-
-    A class to write videos using ffmpeg. ffmpeg will write in a large
-    choice of formats.
-
-    Parameters
-    -----------
-
-    filename
-      Any filename like 'video.mp4' etc. but if you want to avoid
-      complications it is recommended to use the generic extension
-      '.avi' for all your videos.
-
-    size
-      Size (width,height) of the output video in pixels.
-
-    fps
-      Frames per second in the output video file.
-
-    codec
-      FFMPEG codec. It seems that in terms of quality the hierarchy is
-      'rawvideo' = 'png' > 'mpeg4' > 'libx264'
-      'png' manages the same lossless quality as 'rawvideo' but yields
-      smaller files. Type ``ffmpeg -codecs`` in a terminal to get a list
-      of accepted codecs.
-
-      Note for default 'libx264': by default the pixel format yuv420p
-      is used. If the video dimensions are not both even (e.g. 720x405)
-      another pixel format is used, and this can cause problem in some
-      video readers.
-
-    audiofile
-      Optional: The name of an audio file that will be incorporated
-      to the video.
-
-    preset
-      Sets the time that FFMPEG will take to compress the video. The slower,
-      the better the compression rate. Possibilities are: ultrafast,superfast,
-      veryfast, faster, fast, medium (default), slow, slower, veryslow,
-      placebo.
-
-    bitrate
-      Only relevant for codecs which accept a bitrate. "5000k" offers
-      nice results in general.
-
-    """
-
-    def __init__(self, filename, size, fps, codec="libx265", crf=14, audiofile=None,
-                 preset="medium", bitrate=None,
-                 logfile=None, threads=None, ffmpeg_params=None):
-
-        if logfile is None:
-            logfile = sp.PIPE
-
-        self.filename = filename
-        self.codec = codec
-        self.ext = self.filename.split(".")[-1]
-        w = size[0] - 1 if size[0] % 2 != 0 else size[0]
-        h = size[1] - 1 if size[1] % 2 != 0 else size[1]
-
-
-        # order is important
-        cmd = [
-            FFMPEG_BINARY,
-            '-hide_banner',
-            '-hwaccel', 'auto',
-            '-y',
-            '-loglevel', 'error' if logfile == sp.PIPE else 'info',
-            '-f', 'rawvideo',
-            '-vcodec', 'rawvideo',
-            '-s', '%dx%d' % (size[0], size[1]),
-            #'-pix_fmt', 'rgba' if withmask else 'rgb24',
-            '-pix_fmt', 'bgr24',
-            '-r', str(fps),
-            '-an', '-i', '-' 
-        ]
-
-        if audiofile is not None:
-            cmd.extend([
-                '-i', audiofile,
-                '-acodec', 'copy'
-            ])
-
-        cmd.extend([
-            '-vcodec', codec,
-            '-crf', str(crf)
-            #'-preset', preset,
-        ])
-        if ffmpeg_params is not None:
-            cmd.extend(ffmpeg_params)
-        if bitrate is not None:
-            cmd.extend([
-                '-b', bitrate
-            ])
-
-        # scale to a resolution divisible by 2 if not even
-        cmd.extend(['-vf', f'scale={w}:{h}' if w != size[0] or h != size[1] else 'colorspace=bt709:iall=bt601-6-625:fast=1'])
-
-        if threads is not None:
-            cmd.extend(["-threads", str(threads)])
-
-        cmd.extend([
-            '-pix_fmt', 'yuv420p',
-
-        ])
-        cmd.extend([
-            filename
-        ])
-
-        test = str(cmd)
-        print(test)
-
-        popen_params = {"stdout": DEVNULL,
-                        "stderr": logfile,
-                        "stdin": sp.PIPE}
-
-        # This was added so that no extra unwanted window opens on windows
-        # when the child process is created
-        if os.name == "nt":
-            popen_params["creationflags"] = 0x08000000  # CREATE_NO_WINDOW
-        
-        self.proc = sp.Popen(cmd, **popen_params)
-
-
-    def write_frame(self, img_array):
-        """ Writes one frame in the file."""
-        try:
-            #if PY3:
-            self.proc.stdin.write(img_array.tobytes())
-            # else:
-            #    self.proc.stdin.write(img_array.tostring())
-        except IOError as err:
-            _, ffmpeg_error = self.proc.communicate()
-            error = (str(err) + ("\n\nroop unleashed error: FFMPEG encountered "
-                                 "the following error while writing file %s:"
-                                 "\n\n %s" % (self.filename, str(ffmpeg_error))))
-
-            if b"Unknown encoder" in ffmpeg_error:
-
-                error = error+("\n\nThe video export "
-                  "failed because FFMPEG didn't find the specified "
-                  "codec for video encoding (%s). Please install "
-                  "this codec or change the codec when calling "
-                  "write_videofile. For instance:\n"
-                  "  >>> clip.write_videofile('myvid.webm', codec='libvpx')")%(self.codec)
-
-            elif b"incorrect codec parameters ?" in ffmpeg_error:
-
-                 error = error+("\n\nThe video export "
-                  "failed, possibly because the codec specified for "
-                  "the video (%s) is not compatible with the given "
-                  "extension (%s). Please specify a valid 'codec' "
-                  "argument in write_videofile. This would be 'libx264' "
-                  "or 'mpeg4' for mp4, 'libtheora' for ogv, 'libvpx for webm. "
-                  "Another possible reason is that the audio codec was not "
-                  "compatible with the video codec. For instance the video "
-                  "extensions 'ogv' and 'webm' only allow 'libvorbis' (default) as a"
-                  "video codec."
-                  )%(self.codec, self.ext)
-
-            elif  b"encoder setup failed" in ffmpeg_error:
-
-                error = error+("\n\nThe video export "
-                  "failed, possibly because the bitrate you specified "
-                  "was too high or too low for the video codec.")
-
-            elif b"Invalid encoder type" in ffmpeg_error:
-
-                error = error + ("\n\nThe video export failed because the codec "
-                  "or file extension you provided is not a video")
-
-
-            raise IOError(error)
-
-    def close(self):
-        if self.proc:
-            self.proc.stdin.close()
-            if self.proc.stderr is not None:
-                self.proc.stderr.close()
-            self.proc.wait()
-
-        self.proc = None
-
-    # Support the Context Manager protocol, to ensure that resources are cleaned up.
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.close()
-
-
-
-    

roop-unleashed-main/roop/globals.py

@@ -1,56 +0,0 @@
-from settings import Settings
-from typing import List
-
-source_path = None
-target_path = None
-output_path = None
-target_folder_path = None
-startup_args = None
-
-cuda_device_id = 0
-frame_processors: List[str] = []
-keep_fps = None
-keep_frames = None
-autorotate_faces = None
-vr_mode = None
-skip_audio = None
-wait_after_extraction = None
-many_faces = None
-use_batch = None
-source_face_index = 0
-target_face_index = 0
-face_position = None
-video_encoder = None
-video_quality = None
-max_memory = None
-execution_providers: List[str] = []
-execution_threads = None
-headless = None
-log_level = 'error'
-selected_enhancer = None
-subsample_size = 128
-face_swap_mode = None
-blend_ratio = 0.5
-distance_threshold = 0.65
-default_det_size = True
-
-no_face_action = 0
-
-processing = False
-
-g_current_face_analysis = None
-g_desired_face_analysis = None
-
-FACE_ENHANCER = None
-
-INPUT_FACESETS = []
-TARGET_FACES = []
-
-
-IMAGE_CHAIN_PROCESSOR = None
-VIDEO_CHAIN_PROCESSOR = None
-BATCH_IMAGE_CHAIN_PROCESSOR = None
-
-CFG: Settings = None
-
-

roop-unleashed-main/roop/metadata.py

@@ -1,2 +0,0 @@
-name = 'roop unleashed'
-version = '4.4.0'

roop-unleashed-main/roop/processors/Enhance_CodeFormer.py

@@ -1,71 +0,0 @@
-from typing import Any, List, Callable
-import cv2 
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-class Enhance_CodeFormer():
-    model_codeformer = None
-
-    plugin_options:dict = None
-
-    processorname = 'codeformer'
-    type = 'enhance'
-    
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_codeformer is None:
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-            model_path = resolve_relative_path('../models/CodeFormer/CodeFormerv0.1.onnx')
-            self.model_codeformer = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            self.model_inputs = self.model_codeformer.get_inputs()
-            model_outputs = self.model_codeformer.get_outputs()
-            self.io_binding = self.model_codeformer.io_binding()           
-            self.io_binding.bind_cpu_input(self.model_inputs[1].name, np.array([0.5]))
-            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
-
-    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
-        input_size = temp_frame.shape[1]
-        # preprocess
-        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
-        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
-        temp_frame = temp_frame.astype('float32') / 255.0
-        temp_frame = (temp_frame - 0.5) / 0.5
-        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-        
-        self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame.astype(np.float32))
-        self.model_codeformer.run_with_iobinding(self.io_binding)
-        ort_outs = self.io_binding.copy_outputs_to_cpu()
-        result = ort_outs[0][0]
-        del ort_outs
-        
-        # post-process
-        result = result.transpose((1, 2, 0))
-
-        un_min = -1.0
-        un_max = 1.0
-        result = np.clip(result, un_min, un_max)
-        result = (result - un_min) / (un_max - un_min)
-
-        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
-        result = (result * 255.0).round()
-        scale_factor = int(result.shape[1] / input_size)       
-        return result.astype(np.uint8), scale_factor
-
-
-    def Release(self):
-        del self.model_codeformer
-        self.model_codeformer = None
-        del self.io_binding
-        self.io_binding = None
-

roop-unleashed-main/roop/processors/Enhance_DMDNet.py

@@ -1,898 +0,0 @@
-from typing import Any, List, Callable
-import cv2 
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn.utils.spectral_norm as SpectralNorm
-import threading
-from torchvision.ops import roi_align
-
-from math import sqrt
-
-from torchvision.transforms.functional import normalize
-
-from roop.typing import Face, Frame, FaceSet
-
-
-THREAD_LOCK_DMDNET = threading.Lock()
-
-
-class Enhance_DMDNet():
-    plugin_options:dict = None
-    model_dmdnet = None
-    torchdevice = None
-
-    processorname = 'dmdnet'
-    type = 'enhance'
-
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_dmdnet is None:
-            self.model_dmdnet = self.create(self.plugin_options["devicename"])
-            
-
-    # temp_frame already cropped+aligned, bbox not
-    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
-        input_size = temp_frame.shape[1]
-
-        result = self.enhance_face(source_faceset, temp_frame, target_face)
-        scale_factor = int(result.shape[1] / input_size)       
-        return result.astype(np.uint8), scale_factor
-
-
-    def Release(self):
-        self.model_gfpgan = None
-
-
-    # https://stackoverflow.com/a/67174339
-    def landmarks106_to_68(self, pt106):
-        map106to68=[1,10,12,14,16,3,5,7,0,23,21,19,32,30,28,26,17,
-                        43,48,49,51,50,
-                        102,103,104,105,101,
-                        72,73,74,86,78,79,80,85,84,
-                        35,41,42,39,37,36,
-                        89,95,96,93,91,90,
-                        52,64,63,71,67,68,61,58,59,53,56,55,65,66,62,70,69,57,60,54
-                        ]
-      
-        pt68 = []
-        for i in range(68):
-            index = map106to68[i]
-            pt68.append(pt106[index])
-        return pt68
-
-        
-
-
-    def check_bbox(self, imgs, boxes):
-        boxes = boxes.view(-1, 4, 4)
-        colors = [(0, 255, 0), (0, 255, 0), (255, 255, 0), (255, 0, 0)]
-        i = 0
-        for img, box in zip(imgs, boxes):
-            img = (img + 1)/2 * 255
-            img2 = img.permute(1, 2, 0).float().cpu().flip(2).numpy().copy()
-            for idx, point in enumerate(box):
-                cv2.rectangle(img2, (int(point[0]), int(point[1])), (int(point[2]), int(point[3])), color=colors[idx], thickness=2)
-            cv2.imwrite('dmdnet_{:02d}.png'.format(i), img2)
-            i += 1
-
-
-    def trans_points2d(self, pts, M):
-        new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
-        for i in range(pts.shape[0]):
-            pt = pts[i]
-            new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
-            new_pt = np.dot(M, new_pt)
-            new_pts[i] = new_pt[0:2]
-
-        return new_pts
-
-
-    def enhance_face(self, ref_faceset: FaceSet, temp_frame, face: Face):
-        # preprocess
-        start_x, start_y, end_x, end_y = map(int, face['bbox'])
-        lm106 = face.landmark_2d_106
-        lq_landmarks = np.asarray(self.landmarks106_to_68(lm106))
-
-        if temp_frame.shape[0] != 512 or temp_frame.shape[1] != 512:
-            # scale to 512x512
-            scale_factor = 512 / temp_frame.shape[1]
-
-            M = face.matrix * scale_factor
-
-            lq_landmarks = self.trans_points2d(lq_landmarks, M)
-            temp_frame = cv2.resize(temp_frame, (512,512), interpolation = cv2.INTER_AREA)
-
-        if temp_frame.ndim == 2:
-            temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB)  # GGG
-        # else:
-        #     temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)  # RGB
-
-        lq = read_img_tensor(temp_frame)
-
-        LQLocs = get_component_location(lq_landmarks)
-        # self.check_bbox(lq, LQLocs.unsqueeze(0))
-
-        # specific, change 1000 to 1 to activate
-        if len(ref_faceset.faces) > 1:
-            SpecificImgs = []
-            SpecificLocs = []
-            for i,face in enumerate(ref_faceset.faces):
-                lm106 = face.landmark_2d_106
-                lq_landmarks = np.asarray(self.landmarks106_to_68(lm106))
-                ref_image = ref_faceset.ref_images[i]
-                if ref_image.shape[0] != 512 or ref_image.shape[1] != 512:
-                    # scale to 512x512
-                    scale_factor = 512 / ref_image.shape[1]
-
-                    M = face.matrix * scale_factor
-
-                    lq_landmarks = self.trans_points2d(lq_landmarks, M)
-                    ref_image = cv2.resize(ref_image, (512,512), interpolation = cv2.INTER_AREA)
-
-                if ref_image.ndim == 2:
-                    temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB)  # GGG
-                # else:
-                #     temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)  # RGB
-
-                ref_tensor = read_img_tensor(ref_image)
-                ref_locs = get_component_location(lq_landmarks)
-                # self.check_bbox(ref_tensor, ref_locs.unsqueeze(0))
-
-                SpecificImgs.append(ref_tensor)
-                SpecificLocs.append(ref_locs.unsqueeze(0))
-
-            SpecificImgs = torch.cat(SpecificImgs, dim=0)
-            SpecificLocs = torch.cat(SpecificLocs, dim=0)
-            # check_bbox(SpecificImgs, SpecificLocs)
-            SpMem256, SpMem128, SpMem64 = self.model_dmdnet.generate_specific_dictionary(sp_imgs = SpecificImgs.to(self.torchdevice), sp_locs = SpecificLocs)
-            SpMem256Para = {}
-            SpMem128Para = {}
-            SpMem64Para = {}
-            for k, v in SpMem256.items():
-                SpMem256Para[k] = v
-            for k, v in SpMem128.items():
-                SpMem128Para[k] = v
-            for k, v in SpMem64.items():
-                SpMem64Para[k] = v
-        else:
-            # generic
-            SpMem256Para, SpMem128Para, SpMem64Para = None, None, None
-
-        with torch.no_grad():
-            with THREAD_LOCK_DMDNET:
-                try:
-                    GenericResult, SpecificResult = self.model_dmdnet(lq = lq.to(self.torchdevice), loc = LQLocs.unsqueeze(0), sp_256 = SpMem256Para, sp_128 = SpMem128Para, sp_64 = SpMem64Para)
-                except Exception as e:
-                    print(f'Error {e} there may be something wrong with the detected component locations.')
-                    return temp_frame
-        
-        if SpecificResult is not None:
-            save_specific = SpecificResult * 0.5 + 0.5
-            save_specific = save_specific.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
-            save_specific = np.clip(save_specific.float().cpu().numpy(), 0, 1) * 255.0
-            temp_frame =  save_specific.astype("uint8")
-            if False:
-                save_generic = GenericResult * 0.5 + 0.5
-                save_generic = save_generic.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
-                save_generic = np.clip(save_generic.float().cpu().numpy(), 0, 1) * 255.0
-                check_lq = lq * 0.5 + 0.5
-                check_lq = check_lq.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
-                check_lq = np.clip(check_lq.float().cpu().numpy(), 0, 1) * 255.0
-                cv2.imwrite('dmdnet_comparison.png', cv2.cvtColor(np.hstack((check_lq, save_generic, save_specific)),cv2.COLOR_RGB2BGR))
-        else:
-            save_generic = GenericResult * 0.5 + 0.5
-            save_generic = save_generic.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
-            save_generic = np.clip(save_generic.float().cpu().numpy(), 0, 1) * 255.0
-            temp_frame =  save_generic.astype("uint8")
-        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_RGB2BGR)  # RGB
-        return temp_frame
-
-    
-
-    def create(self, devicename):
-        self.torchdevice = torch.device(devicename)
-        model_dmdnet = DMDNet().to(self.torchdevice)
-        weights = torch.load('./models/DMDNet.pth') 
-        model_dmdnet.load_state_dict(weights, strict=True)
-
-        model_dmdnet.eval()
-        num_params = 0
-        for param in model_dmdnet.parameters():
-            num_params += param.numel()
-        return model_dmdnet
-
-    # print('{:>8s} : {}'.format('Using device', device))
-    # print('{:>8s} : {:.2f}M'.format('Model params', num_params/1e6))
-
-
-
-def read_img_tensor(Img=None): #rgb -1~1 
-    Img = Img.transpose((2, 0, 1))/255.0
-    Img = torch.from_numpy(Img).float()
-    normalize(Img, [0.5,0.5,0.5], [0.5,0.5,0.5], inplace=True)
-    ImgTensor = Img.unsqueeze(0)
-    return ImgTensor
-
-
-def get_component_location(Landmarks, re_read=False):
-    if re_read:
-        ReadLandmark = []
-        with open(Landmarks,'r') as f:
-            for line in f:
-                tmp = [float(i) for i in line.split(' ') if i != '\n']
-                ReadLandmark.append(tmp)
-        ReadLandmark = np.array(ReadLandmark) #
-        Landmarks = np.reshape(ReadLandmark, [-1, 2]) # 68*2
-    Map_LE_B = list(np.hstack((range(17,22), range(36,42))))
-    Map_RE_B = list(np.hstack((range(22,27), range(42,48))))
-    Map_LE = list(range(36,42))
-    Map_RE = list(range(42,48))
-    Map_NO = list(range(29,36))
-    Map_MO = list(range(48,68))
-
-    Landmarks[Landmarks>504]=504
-    Landmarks[Landmarks<8]=8
-    
-    #left eye
-    Mean_LE = np.mean(Landmarks[Map_LE],0)
-    L_LE1 = Mean_LE[1] - np.min(Landmarks[Map_LE_B,1])
-    L_LE1 = L_LE1 * 1.3
-    L_LE2 = L_LE1 / 1.9
-    L_LE_xy = L_LE1 + L_LE2
-    L_LE_lt = [L_LE_xy/2, L_LE1]
-    L_LE_rb = [L_LE_xy/2, L_LE2]
-    Location_LE = np.hstack((Mean_LE - L_LE_lt + 1, Mean_LE + L_LE_rb)).astype(int)
-
-    #right eye
-    Mean_RE = np.mean(Landmarks[Map_RE],0)
-    L_RE1 = Mean_RE[1] - np.min(Landmarks[Map_RE_B,1])
-    L_RE1 = L_RE1 * 1.3
-    L_RE2 = L_RE1 / 1.9
-    L_RE_xy = L_RE1 + L_RE2
-    L_RE_lt = [L_RE_xy/2, L_RE1]
-    L_RE_rb = [L_RE_xy/2, L_RE2]
-    Location_RE = np.hstack((Mean_RE - L_RE_lt + 1, Mean_RE + L_RE_rb)).astype(int)
-
-    #nose
-    Mean_NO = np.mean(Landmarks[Map_NO],0)
-    L_NO1 =( np.max([Mean_NO[0] - Landmarks[31][0], Landmarks[35][0] - Mean_NO[0]])) * 1.25
-    L_NO2 = (Landmarks[33][1] - Mean_NO[1]) * 1.1
-    L_NO_xy = L_NO1 * 2
-    L_NO_lt = [L_NO_xy/2, L_NO_xy - L_NO2]
-    L_NO_rb = [L_NO_xy/2, L_NO2]
-    Location_NO = np.hstack((Mean_NO - L_NO_lt + 1, Mean_NO + L_NO_rb)).astype(int)
-    
-    #mouth
-    Mean_MO = np.mean(Landmarks[Map_MO],0)
-    L_MO = np.max((np.max(np.max(Landmarks[Map_MO],0) - np.min(Landmarks[Map_MO],0))/2,16)) * 1.1
-    MO_O = Mean_MO - L_MO + 1
-    MO_T = Mean_MO + L_MO
-    MO_T[MO_T>510]=510
-    Location_MO = np.hstack((MO_O, MO_T)).astype(int)
-    return torch.cat([torch.FloatTensor(Location_LE).unsqueeze(0), torch.FloatTensor(Location_RE).unsqueeze(0), torch.FloatTensor(Location_NO).unsqueeze(0), torch.FloatTensor(Location_MO).unsqueeze(0)], dim=0)
-
-
-
-
-def calc_mean_std_4D(feat, eps=1e-5):
-    # eps is a small value added to the variance to avoid divide-by-zero.
-    size = feat.size()
-    assert (len(size) == 4)
-    N, C = size[:2]
-    feat_var = feat.view(N, C, -1).var(dim=2) + eps
-    feat_std = feat_var.sqrt().view(N, C, 1, 1)
-    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
-    return feat_mean, feat_std
-
-def adaptive_instance_normalization_4D(content_feat, style_feat): # content_feat is ref feature, style is degradate feature
-    size = content_feat.size()
-    style_mean, style_std = calc_mean_std_4D(style_feat)
-    content_mean, content_std = calc_mean_std_4D(content_feat)
-    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
-    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-def convU(in_channels, out_channels,conv_layer, norm_layer, kernel_size=3, stride=1,dilation=1, bias=True):
-    return nn.Sequential(
-        SpectralNorm(conv_layer(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias)),
-        nn.LeakyReLU(0.2),
-        SpectralNorm(conv_layer(out_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias)),
-    )
-    
-
-class MSDilateBlock(nn.Module):
-    def __init__(self, in_channels,conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, kernel_size=3, dilation=[1,1,1,1], bias=True):
-        super(MSDilateBlock, self).__init__()
-        self.conv1 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[0], bias=bias)
-        self.conv2 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[1], bias=bias)
-        self.conv3 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[2], bias=bias)
-        self.conv4 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[3], bias=bias)
-        self.convi =  SpectralNorm(conv_layer(in_channels*4, in_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size-1)//2, bias=bias))
-    def forward(self, x):
-        conv1 = self.conv1(x)
-        conv2 = self.conv2(x)
-        conv3 = self.conv3(x)
-        conv4 = self.conv4(x)
-        cat  = torch.cat([conv1, conv2, conv3, conv4], 1)
-        out = self.convi(cat) + x
-        return out
-
-
-class AdaptiveInstanceNorm(nn.Module):
-    def __init__(self, in_channel):
-        super().__init__()
-        self.norm = nn.InstanceNorm2d(in_channel)
-
-    def forward(self, input, style):
-        style_mean, style_std = calc_mean_std_4D(style)
-        out = self.norm(input)
-        size = input.size()
-        out = style_std.expand(size) * out + style_mean.expand(size)
-        return out
-
-class NoiseInjection(nn.Module):
-    def __init__(self, channel):
-        super().__init__()
-        self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
-    def forward(self, image, noise):
-        if noise is None:
-            b, c, h, w = image.shape
-            noise = image.new_empty(b, 1, h, w).normal_()
-        return image + self.weight * noise
-
-class StyledUpBlock(nn.Module):
-    def __init__(self, in_channel, out_channel, kernel_size=3, padding=1,upsample=False, noise_inject=False):
-        super().__init__()
-
-        self.noise_inject = noise_inject
-        if upsample:
-            self.conv1 = nn.Sequential(
-                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
-                nn.LeakyReLU(0.2),
-            )
-        else:
-            self.conv1 = nn.Sequential(
-                SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
-                nn.LeakyReLU(0.2),
-                SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
-            )
-        self.convup = nn.Sequential(
-                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
-                nn.LeakyReLU(0.2),
-                SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
-            )
-        if self.noise_inject:
-            self.noise1 = NoiseInjection(out_channel)
-
-        self.lrelu1 = nn.LeakyReLU(0.2)
-
-        self.ScaleModel1 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(in_channel,out_channel,3, 1, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1))
-        )
-        self.ShiftModel1 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(in_channel,out_channel,3, 1, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)),
-        )
-       
-    def forward(self, input, style):
-        out = self.conv1(input)
-        out = self.lrelu1(out)
-        Shift1 = self.ShiftModel1(style)
-        Scale1 = self.ScaleModel1(style)
-        out = out * Scale1 + Shift1
-        if self.noise_inject:
-            out = self.noise1(out, noise=None)
-        outup = self.convup(out)
-        return outup
-
-
-####################################################################
-###############Face Dictionary Generator
-####################################################################
-def AttentionBlock(in_channel):
-    return nn.Sequential(
-        SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)),
-        nn.LeakyReLU(0.2),
-        SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)),
-    )
-
-class DilateResBlock(nn.Module):
-    def __init__(self, dim, dilation=[5,3] ):
-        super(DilateResBlock, self).__init__()
-        self.Res = nn.Sequential(
-            SpectralNorm(nn.Conv2d(dim, dim, 3, 1, ((3-1)//2)*dilation[0], dilation[0])),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(dim, dim, 3, 1, ((3-1)//2)*dilation[1], dilation[1])),
-        )
-    def forward(self, x):
-        out = x + self.Res(x)
-        return out
-
-
-class KeyValue(nn.Module):
-    def __init__(self, indim, keydim, valdim):
-        super(KeyValue, self).__init__()
-        self.Key = nn.Sequential(
-            SpectralNorm(nn.Conv2d(indim, keydim, kernel_size=(3,3), padding=(1,1), stride=1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(keydim, keydim, kernel_size=(3,3), padding=(1,1), stride=1)),
-        )
-        self.Value = nn.Sequential(
-            SpectralNorm(nn.Conv2d(indim, valdim, kernel_size=(3,3), padding=(1,1), stride=1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(valdim, valdim, kernel_size=(3,3), padding=(1,1), stride=1)),
-        )
-    def forward(self, x):  
-        return self.Key(x), self.Value(x)
-
-class MaskAttention(nn.Module):
-    def __init__(self, indim):
-        super(MaskAttention, self).__init__()
-        self.conv1 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
-        )
-        self.conv2 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
-        )
-        self.conv3 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
-        )
-        self.convCat = nn.Sequential(
-            SpectralNorm(nn.Conv2d(indim//3 * 3, indim, kernel_size=(3,3), padding=(1,1), stride=1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(indim, indim, kernel_size=(3,3), padding=(1,1), stride=1)),
-        )
-    def forward(self, x, y, z):
-        c1 = self.conv1(x)
-        c2 = self.conv2(y)
-        c3 = self.conv3(z)
-        return self.convCat(torch.cat([c1,c2,c3], dim=1))
-
-class Query(nn.Module):
-    def __init__(self, indim, quedim):
-        super(Query, self).__init__()
-        self.Query = nn.Sequential(
-            SpectralNorm(nn.Conv2d(indim, quedim, kernel_size=(3,3), padding=(1,1), stride=1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(quedim, quedim, kernel_size=(3,3), padding=(1,1), stride=1)),
-        )
-    def forward(self, x):
-        return self.Query(x)
-
-def roi_align_self(input, location, target_size):
-    test = (target_size.item(),target_size.item())
-    return torch.cat([F.interpolate(input[i:i+1,:,location[i,1]:location[i,3],location[i,0]:location[i,2]],test,mode='bilinear',align_corners=False) for i in range(input.size(0))],0)
-
-class FeatureExtractor(nn.Module):
-    def __init__(self, ngf = 64, key_scale = 4):#
-        super().__init__()
-
-        self.key_scale = 4
-        self.part_sizes = np.array([80,80,50,110]) #
-        self.feature_sizes = np.array([256,128,64]) # 
-
-        self.conv1 = nn.Sequential(
-                SpectralNorm(nn.Conv2d(3, ngf, 3, 2, 1)),
-                nn.LeakyReLU(0.2),
-                SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
-            )
-        self.conv2 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1))
-        )
-        self.res1 = DilateResBlock(ngf, [5,3])
-        self.res2 = DilateResBlock(ngf, [5,3])
-
-        
-        self.conv3 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(ngf, ngf*2, 3, 2, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1)),
-            )
-        self.conv4 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1))
-        )
-        self.res3 = DilateResBlock(ngf*2, [3,1])
-        self.res4 = DilateResBlock(ngf*2, [3,1])
-
-        self.conv5 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(ngf*2, ngf*4, 3, 2, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1)),
-        )
-        self.conv6 = nn.Sequential(
-            SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1))
-        )
-        self.res5 = DilateResBlock(ngf*4, [1,1])
-        self.res6 = DilateResBlock(ngf*4, [1,1])
-
-        self.LE_256_Q = Query(ngf, ngf // self.key_scale)
-        self.RE_256_Q = Query(ngf, ngf // self.key_scale)
-        self.MO_256_Q = Query(ngf, ngf // self.key_scale)
-        self.LE_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
-        self.RE_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
-        self.MO_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
-        self.LE_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
-        self.RE_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
-        self.MO_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
-
-
-    def forward(self, img, locs):
-        le_location = locs[:,0,:].int().cpu().numpy()
-        re_location = locs[:,1,:].int().cpu().numpy()
-        no_location = locs[:,2,:].int().cpu().numpy()
-        mo_location = locs[:,3,:].int().cpu().numpy()
-        
-
-        f1_0 = self.conv1(img) 
-        f1_1 = self.res1(f1_0)
-        f2_0 = self.conv2(f1_1)
-        f2_1 = self.res2(f2_0)
-
-        f3_0 = self.conv3(f2_1) 
-        f3_1 = self.res3(f3_0)
-        f4_0 = self.conv4(f3_1)
-        f4_1 = self.res4(f4_0)
-
-        f5_0 = self.conv5(f4_1) 
-        f5_1 = self.res5(f5_0)
-        f6_0 = self.conv6(f5_1)
-        f6_1 = self.res6(f6_0)
-
-
-        ####ROI Align
-        le_part_256 = roi_align_self(f2_1.clone(), le_location//2, self.part_sizes[0]//2)
-        re_part_256 = roi_align_self(f2_1.clone(), re_location//2, self.part_sizes[1]//2)
-        mo_part_256 = roi_align_self(f2_1.clone(), mo_location//2, self.part_sizes[3]//2)
-
-        le_part_128 = roi_align_self(f4_1.clone(), le_location//4, self.part_sizes[0]//4)
-        re_part_128 = roi_align_self(f4_1.clone(), re_location//4, self.part_sizes[1]//4)
-        mo_part_128 = roi_align_self(f4_1.clone(), mo_location//4, self.part_sizes[3]//4)
-
-        le_part_64 = roi_align_self(f6_1.clone(), le_location//8, self.part_sizes[0]//8)
-        re_part_64 = roi_align_self(f6_1.clone(), re_location//8, self.part_sizes[1]//8)
-        mo_part_64 = roi_align_self(f6_1.clone(), mo_location//8, self.part_sizes[3]//8)
-
-
-        le_256_q = self.LE_256_Q(le_part_256)
-        re_256_q = self.RE_256_Q(re_part_256)
-        mo_256_q = self.MO_256_Q(mo_part_256)
-
-        le_128_q = self.LE_128_Q(le_part_128)
-        re_128_q = self.RE_128_Q(re_part_128)
-        mo_128_q = self.MO_128_Q(mo_part_128)
-
-        le_64_q = self.LE_64_Q(le_part_64)
-        re_64_q = self.RE_64_Q(re_part_64)
-        mo_64_q = self.MO_64_Q(mo_part_64)
-
-        return {'f256': f2_1, 'f128': f4_1, 'f64': f6_1,\
-            'le256': le_part_256, 're256': re_part_256, 'mo256': mo_part_256, \
-            'le128': le_part_128, 're128': re_part_128, 'mo128': mo_part_128, \
-            'le64': le_part_64, 're64': re_part_64, 'mo64': mo_part_64, \
-            'le_256_q': le_256_q, 're_256_q': re_256_q, 'mo_256_q': mo_256_q,\
-            'le_128_q': le_128_q, 're_128_q': re_128_q, 'mo_128_q': mo_128_q,\
-            'le_64_q': le_64_q, 're_64_q': re_64_q, 'mo_64_q': mo_64_q}
-
-
-class DMDNet(nn.Module):
-    def __init__(self, ngf = 64, banks_num = 128):
-        super().__init__()
-        self.part_sizes = np.array([80,80,50,110]) # size for 512
-        self.feature_sizes = np.array([256,128,64]) # size for 512
-
-        self.banks_num = banks_num
-        self.key_scale = 4
-
-        self.E_lq = FeatureExtractor(key_scale = self.key_scale)
-        self.E_hq = FeatureExtractor(key_scale = self.key_scale)
-
-        self.LE_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
-        self.RE_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
-        self.MO_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
-
-        self.LE_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
-        self.RE_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
-        self.MO_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
-
-        self.LE_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
-        self.RE_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
-        self.MO_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
-
-
-        self.LE_256_Attention = AttentionBlock(64)
-        self.RE_256_Attention = AttentionBlock(64)
-        self.MO_256_Attention = AttentionBlock(64)
-
-        self.LE_128_Attention = AttentionBlock(128)
-        self.RE_128_Attention = AttentionBlock(128)
-        self.MO_128_Attention = AttentionBlock(128)
-
-        self.LE_64_Attention = AttentionBlock(256)
-        self.RE_64_Attention = AttentionBlock(256)
-        self.MO_64_Attention = AttentionBlock(256)
-
-        self.LE_256_Mask = MaskAttention(64)
-        self.RE_256_Mask = MaskAttention(64)
-        self.MO_256_Mask = MaskAttention(64)
-
-        self.LE_128_Mask = MaskAttention(128)
-        self.RE_128_Mask = MaskAttention(128)
-        self.MO_128_Mask = MaskAttention(128)
-
-        self.LE_64_Mask = MaskAttention(256)
-        self.RE_64_Mask = MaskAttention(256)
-        self.MO_64_Mask = MaskAttention(256)
-
-        self.MSDilate = MSDilateBlock(ngf*4, dilation = [4,3,2,1])
-
-        self.up1 = StyledUpBlock(ngf*4, ngf*2, noise_inject=False) #
-        self.up2 = StyledUpBlock(ngf*2, ngf, noise_inject=False) #
-        self.up3 = StyledUpBlock(ngf, ngf, noise_inject=False) #
-        self.up4 = nn.Sequential( 
-            SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
-            nn.LeakyReLU(0.2),
-            UpResBlock(ngf),
-            UpResBlock(ngf),
-            SpectralNorm(nn.Conv2d(ngf, 3, kernel_size=3, stride=1, padding=1)),
-            nn.Tanh()
-        )
- 
-        # define generic memory, revise register_buffer to register_parameter for backward update
-        self.register_buffer('le_256_mem_key', torch.randn(128,16,40,40))
-        self.register_buffer('re_256_mem_key', torch.randn(128,16,40,40))
-        self.register_buffer('mo_256_mem_key', torch.randn(128,16,55,55))
-        self.register_buffer('le_256_mem_value', torch.randn(128,64,40,40))
-        self.register_buffer('re_256_mem_value', torch.randn(128,64,40,40))
-        self.register_buffer('mo_256_mem_value', torch.randn(128,64,55,55))
-        
-
-        self.register_buffer('le_128_mem_key', torch.randn(128,32,20,20))
-        self.register_buffer('re_128_mem_key', torch.randn(128,32,20,20))
-        self.register_buffer('mo_128_mem_key', torch.randn(128,32,27,27))
-        self.register_buffer('le_128_mem_value', torch.randn(128,128,20,20))
-        self.register_buffer('re_128_mem_value', torch.randn(128,128,20,20))
-        self.register_buffer('mo_128_mem_value', torch.randn(128,128,27,27))
-
-        self.register_buffer('le_64_mem_key', torch.randn(128,64,10,10))
-        self.register_buffer('re_64_mem_key', torch.randn(128,64,10,10))
-        self.register_buffer('mo_64_mem_key', torch.randn(128,64,13,13))
-        self.register_buffer('le_64_mem_value', torch.randn(128,256,10,10))
-        self.register_buffer('re_64_mem_value', torch.randn(128,256,10,10))
-        self.register_buffer('mo_64_mem_value', torch.randn(128,256,13,13))
-
-    
-    def readMem(self, k, v, q):
-        sim = F.conv2d(q, k)
-        score = F.softmax(sim/sqrt(sim.size(1)), dim=1) #B * S * 1 * 1 6*128
-        sb,sn,sw,sh = score.size()
-        s_m = score.view(sb, -1).unsqueeze(1)#2*1*M
-        vb,vn,vw,vh = v.size()
-        v_in = v.view(vb, -1).repeat(sb,1,1)#2*M*(c*w*h)
-        mem_out = torch.bmm(s_m, v_in).squeeze(1).view(sb, vn, vw,vh)
-        max_inds = torch.argmax(score, dim=1).squeeze()
-        return mem_out, max_inds
-    
-
-    def memorize(self, img, locs):
-        fs = self.E_hq(img, locs)
-        LE256_key, LE256_value = self.LE_256_KV(fs['le256'])
-        RE256_key, RE256_value = self.RE_256_KV(fs['re256'])
-        MO256_key, MO256_value = self.MO_256_KV(fs['mo256'])
-
-        LE128_key, LE128_value = self.LE_128_KV(fs['le128'])
-        RE128_key, RE128_value = self.RE_128_KV(fs['re128'])
-        MO128_key, MO128_value = self.MO_128_KV(fs['mo128'])
-
-        LE64_key, LE64_value = self.LE_64_KV(fs['le64'])
-        RE64_key, RE64_value = self.RE_64_KV(fs['re64'])
-        MO64_key, MO64_value = self.MO_64_KV(fs['mo64'])
-
-        Mem256 = {'LE256Key': LE256_key, 'LE256Value': LE256_value, 'RE256Key': RE256_key, 'RE256Value': RE256_value,'MO256Key': MO256_key, 'MO256Value': MO256_value}
-        Mem128 = {'LE128Key': LE128_key, 'LE128Value': LE128_value, 'RE128Key': RE128_key, 'RE128Value': RE128_value,'MO128Key': MO128_key, 'MO128Value': MO128_value}
-        Mem64 = {'LE64Key': LE64_key, 'LE64Value': LE64_value, 'RE64Key': RE64_key, 'RE64Value': RE64_value,'MO64Key': MO64_key, 'MO64Value': MO64_value}
- 
-        FS256 = {'LE256F':fs['le256'], 'RE256F':fs['re256'], 'MO256F':fs['mo256']}
-        FS128 = {'LE128F':fs['le128'], 'RE128F':fs['re128'], 'MO128F':fs['mo128']}
-        FS64 = {'LE64F':fs['le64'], 'RE64F':fs['re64'], 'MO64F':fs['mo64']}
-        
-        return Mem256, Mem128, Mem64
-
-    def enhancer(self, fs_in, sp_256=None, sp_128=None, sp_64=None):
-        le_256_q = fs_in['le_256_q']
-        re_256_q = fs_in['re_256_q']
-        mo_256_q = fs_in['mo_256_q']
-
-        le_128_q = fs_in['le_128_q']
-        re_128_q = fs_in['re_128_q']
-        mo_128_q = fs_in['mo_128_q']
-
-        le_64_q = fs_in['le_64_q']
-        re_64_q = fs_in['re_64_q']
-        mo_64_q = fs_in['mo_64_q']
-
-        
-        ####for 256
-        le_256_mem_g, le_256_inds = self.readMem(self.le_256_mem_key, self.le_256_mem_value, le_256_q)
-        re_256_mem_g, re_256_inds = self.readMem(self.re_256_mem_key, self.re_256_mem_value, re_256_q)
-        mo_256_mem_g, mo_256_inds = self.readMem(self.mo_256_mem_key, self.mo_256_mem_value, mo_256_q)
-
-        le_128_mem_g, le_128_inds = self.readMem(self.le_128_mem_key, self.le_128_mem_value, le_128_q)
-        re_128_mem_g, re_128_inds = self.readMem(self.re_128_mem_key, self.re_128_mem_value, re_128_q)
-        mo_128_mem_g, mo_128_inds = self.readMem(self.mo_128_mem_key, self.mo_128_mem_value, mo_128_q)
-
-        le_64_mem_g, le_64_inds = self.readMem(self.le_64_mem_key, self.le_64_mem_value, le_64_q)
-        re_64_mem_g, re_64_inds = self.readMem(self.re_64_mem_key, self.re_64_mem_value, re_64_q)
-        mo_64_mem_g, mo_64_inds = self.readMem(self.mo_64_mem_key, self.mo_64_mem_value, mo_64_q)
-
-        if sp_256 is not None and sp_128 is not None and sp_64 is not None:
-            le_256_mem_s, _ = self.readMem(sp_256['LE256Key'], sp_256['LE256Value'], le_256_q)
-            re_256_mem_s, _ = self.readMem(sp_256['RE256Key'], sp_256['RE256Value'], re_256_q)
-            mo_256_mem_s, _ = self.readMem(sp_256['MO256Key'], sp_256['MO256Value'], mo_256_q)
-            le_256_mask = self.LE_256_Mask(fs_in['le256'],le_256_mem_s,le_256_mem_g)
-            le_256_mem = le_256_mask*le_256_mem_s + (1-le_256_mask)*le_256_mem_g
-            re_256_mask = self.RE_256_Mask(fs_in['re256'],re_256_mem_s,re_256_mem_g)
-            re_256_mem = re_256_mask*re_256_mem_s + (1-re_256_mask)*re_256_mem_g
-            mo_256_mask = self.MO_256_Mask(fs_in['mo256'],mo_256_mem_s,mo_256_mem_g)
-            mo_256_mem = mo_256_mask*mo_256_mem_s + (1-mo_256_mask)*mo_256_mem_g
-
-            le_128_mem_s, _ = self.readMem(sp_128['LE128Key'], sp_128['LE128Value'], le_128_q)
-            re_128_mem_s, _ = self.readMem(sp_128['RE128Key'], sp_128['RE128Value'], re_128_q)
-            mo_128_mem_s, _ = self.readMem(sp_128['MO128Key'], sp_128['MO128Value'], mo_128_q)
-            le_128_mask = self.LE_128_Mask(fs_in['le128'],le_128_mem_s,le_128_mem_g)
-            le_128_mem = le_128_mask*le_128_mem_s + (1-le_128_mask)*le_128_mem_g
-            re_128_mask = self.RE_128_Mask(fs_in['re128'],re_128_mem_s,re_128_mem_g)
-            re_128_mem = re_128_mask*re_128_mem_s + (1-re_128_mask)*re_128_mem_g
-            mo_128_mask = self.MO_128_Mask(fs_in['mo128'],mo_128_mem_s,mo_128_mem_g)
-            mo_128_mem = mo_128_mask*mo_128_mem_s + (1-mo_128_mask)*mo_128_mem_g
-
-            le_64_mem_s, _ = self.readMem(sp_64['LE64Key'], sp_64['LE64Value'], le_64_q)
-            re_64_mem_s, _ = self.readMem(sp_64['RE64Key'], sp_64['RE64Value'], re_64_q)
-            mo_64_mem_s, _ = self.readMem(sp_64['MO64Key'], sp_64['MO64Value'], mo_64_q)
-            le_64_mask = self.LE_64_Mask(fs_in['le64'],le_64_mem_s,le_64_mem_g)
-            le_64_mem = le_64_mask*le_64_mem_s + (1-le_64_mask)*le_64_mem_g
-            re_64_mask = self.RE_64_Mask(fs_in['re64'],re_64_mem_s,re_64_mem_g)
-            re_64_mem = re_64_mask*re_64_mem_s + (1-re_64_mask)*re_64_mem_g
-            mo_64_mask = self.MO_64_Mask(fs_in['mo64'],mo_64_mem_s,mo_64_mem_g)
-            mo_64_mem = mo_64_mask*mo_64_mem_s + (1-mo_64_mask)*mo_64_mem_g
-        else:
-            le_256_mem = le_256_mem_g
-            re_256_mem = re_256_mem_g
-            mo_256_mem = mo_256_mem_g
-            le_128_mem = le_128_mem_g
-            re_128_mem = re_128_mem_g
-            mo_128_mem = mo_128_mem_g
-            le_64_mem = le_64_mem_g
-            re_64_mem = re_64_mem_g
-            mo_64_mem = mo_64_mem_g
-
-        le_256_mem_norm = adaptive_instance_normalization_4D(le_256_mem, fs_in['le256'])
-        re_256_mem_norm = adaptive_instance_normalization_4D(re_256_mem, fs_in['re256'])
-        mo_256_mem_norm = adaptive_instance_normalization_4D(mo_256_mem, fs_in['mo256'])
-        
-        ####for 128
-        le_128_mem_norm = adaptive_instance_normalization_4D(le_128_mem, fs_in['le128'])
-        re_128_mem_norm = adaptive_instance_normalization_4D(re_128_mem, fs_in['re128'])
-        mo_128_mem_norm = adaptive_instance_normalization_4D(mo_128_mem, fs_in['mo128'])
-        
-        ####for 64
-        le_64_mem_norm = adaptive_instance_normalization_4D(le_64_mem, fs_in['le64'])
-        re_64_mem_norm = adaptive_instance_normalization_4D(re_64_mem, fs_in['re64'])
-        mo_64_mem_norm = adaptive_instance_normalization_4D(mo_64_mem, fs_in['mo64'])
-    
-
-        EnMem256 = {'LE256Norm': le_256_mem_norm, 'RE256Norm': re_256_mem_norm, 'MO256Norm': mo_256_mem_norm}
-        EnMem128 = {'LE128Norm': le_128_mem_norm, 'RE128Norm': re_128_mem_norm, 'MO128Norm': mo_128_mem_norm}
-        EnMem64 = {'LE64Norm': le_64_mem_norm, 'RE64Norm': re_64_mem_norm, 'MO64Norm': mo_64_mem_norm}
-        Ind256 = {'LE': le_256_inds, 'RE': re_256_inds, 'MO': mo_256_inds}
-        Ind128 = {'LE': le_128_inds, 'RE': re_128_inds, 'MO': mo_128_inds}
-        Ind64 = {'LE': le_64_inds, 'RE': re_64_inds, 'MO': mo_64_inds}
-        return EnMem256, EnMem128, EnMem64, Ind256, Ind128, Ind64
-
-    def reconstruct(self, fs_in, locs, memstar):
-        le_256_mem_norm, re_256_mem_norm, mo_256_mem_norm = memstar[0]['LE256Norm'], memstar[0]['RE256Norm'], memstar[0]['MO256Norm']
-        le_128_mem_norm, re_128_mem_norm, mo_128_mem_norm = memstar[1]['LE128Norm'], memstar[1]['RE128Norm'], memstar[1]['MO128Norm']
-        le_64_mem_norm, re_64_mem_norm, mo_64_mem_norm = memstar[2]['LE64Norm'], memstar[2]['RE64Norm'], memstar[2]['MO64Norm']
-
-        le_256_final = self.LE_256_Attention(le_256_mem_norm - fs_in['le256']) * le_256_mem_norm + fs_in['le256']
-        re_256_final = self.RE_256_Attention(re_256_mem_norm - fs_in['re256']) * re_256_mem_norm + fs_in['re256']
-        mo_256_final = self.MO_256_Attention(mo_256_mem_norm - fs_in['mo256']) * mo_256_mem_norm + fs_in['mo256']
-        
-        le_128_final = self.LE_128_Attention(le_128_mem_norm - fs_in['le128']) * le_128_mem_norm + fs_in['le128']
-        re_128_final = self.RE_128_Attention(re_128_mem_norm - fs_in['re128']) * re_128_mem_norm + fs_in['re128']
-        mo_128_final = self.MO_128_Attention(mo_128_mem_norm - fs_in['mo128']) * mo_128_mem_norm + fs_in['mo128']
-        
-        le_64_final = self.LE_64_Attention(le_64_mem_norm - fs_in['le64']) * le_64_mem_norm + fs_in['le64']
-        re_64_final = self.RE_64_Attention(re_64_mem_norm - fs_in['re64']) * re_64_mem_norm + fs_in['re64']
-        mo_64_final = self.MO_64_Attention(mo_64_mem_norm - fs_in['mo64']) * mo_64_mem_norm + fs_in['mo64']
-
-
-        le_location = locs[:,0,:]
-        re_location = locs[:,1,:]
-        mo_location = locs[:,3,:]
-
-        # Somehow with latest Torch it doesn't like numpy wrappers anymore
-        
-        # le_location = le_location.cpu().int().numpy()
-        # re_location = re_location.cpu().int().numpy()
-        # mo_location = mo_location.cpu().int().numpy()
-        le_location = le_location.cpu().int()
-        re_location = re_location.cpu().int()
-        mo_location = mo_location.cpu().int()
-
-        up_in_256 = fs_in['f256'].clone()# * 0
-        up_in_128 = fs_in['f128'].clone()# * 0
-        up_in_64 = fs_in['f64'].clone()# * 0
-
-        for i in range(fs_in['f256'].size(0)):
-            up_in_256[i:i+1,:,le_location[i,1]//2:le_location[i,3]//2,le_location[i,0]//2:le_location[i,2]//2] = F.interpolate(le_256_final[i:i+1,:,:,:].clone(), (le_location[i,3]//2-le_location[i,1]//2,le_location[i,2]//2-le_location[i,0]//2),mode='bilinear',align_corners=False)
-            up_in_256[i:i+1,:,re_location[i,1]//2:re_location[i,3]//2,re_location[i,0]//2:re_location[i,2]//2] = F.interpolate(re_256_final[i:i+1,:,:,:].clone(), (re_location[i,3]//2-re_location[i,1]//2,re_location[i,2]//2-re_location[i,0]//2),mode='bilinear',align_corners=False)
-            up_in_256[i:i+1,:,mo_location[i,1]//2:mo_location[i,3]//2,mo_location[i,0]//2:mo_location[i,2]//2] = F.interpolate(mo_256_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//2-mo_location[i,1]//2,mo_location[i,2]//2-mo_location[i,0]//2),mode='bilinear',align_corners=False)
-            
-            up_in_128[i:i+1,:,le_location[i,1]//4:le_location[i,3]//4,le_location[i,0]//4:le_location[i,2]//4] = F.interpolate(le_128_final[i:i+1,:,:,:].clone(), (le_location[i,3]//4-le_location[i,1]//4,le_location[i,2]//4-le_location[i,0]//4),mode='bilinear',align_corners=False)
-            up_in_128[i:i+1,:,re_location[i,1]//4:re_location[i,3]//4,re_location[i,0]//4:re_location[i,2]//4] = F.interpolate(re_128_final[i:i+1,:,:,:].clone(), (re_location[i,3]//4-re_location[i,1]//4,re_location[i,2]//4-re_location[i,0]//4),mode='bilinear',align_corners=False)
-            up_in_128[i:i+1,:,mo_location[i,1]//4:mo_location[i,3]//4,mo_location[i,0]//4:mo_location[i,2]//4] = F.interpolate(mo_128_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//4-mo_location[i,1]//4,mo_location[i,2]//4-mo_location[i,0]//4),mode='bilinear',align_corners=False)
-
-            up_in_64[i:i+1,:,le_location[i,1]//8:le_location[i,3]//8,le_location[i,0]//8:le_location[i,2]//8] = F.interpolate(le_64_final[i:i+1,:,:,:].clone(), (le_location[i,3]//8-le_location[i,1]//8,le_location[i,2]//8-le_location[i,0]//8),mode='bilinear',align_corners=False)
-            up_in_64[i:i+1,:,re_location[i,1]//8:re_location[i,3]//8,re_location[i,0]//8:re_location[i,2]//8] = F.interpolate(re_64_final[i:i+1,:,:,:].clone(), (re_location[i,3]//8-re_location[i,1]//8,re_location[i,2]//8-re_location[i,0]//8),mode='bilinear',align_corners=False)
-            up_in_64[i:i+1,:,mo_location[i,1]//8:mo_location[i,3]//8,mo_location[i,0]//8:mo_location[i,2]//8] = F.interpolate(mo_64_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//8-mo_location[i,1]//8,mo_location[i,2]//8-mo_location[i,0]//8),mode='bilinear',align_corners=False)
-        
-        ms_in_64 = self.MSDilate(fs_in['f64'].clone())
-        fea_up1 = self.up1(ms_in_64, up_in_64)
-        fea_up2 = self.up2(fea_up1, up_in_128) #
-        fea_up3 = self.up3(fea_up2, up_in_256) #
-        output = self.up4(fea_up3) #
-        return output
-
-    def generate_specific_dictionary(self, sp_imgs=None, sp_locs=None):
-        return self.memorize(sp_imgs, sp_locs)
-
-    def forward(self, lq=None, loc=None, sp_256 = None, sp_128 = None, sp_64 = None):
-        try:
-            fs_in = self.E_lq(lq, loc) # low quality images
-        except Exception as e:
-            print(e)
-
-        GeMemNorm256, GeMemNorm128, GeMemNorm64, Ind256, Ind128, Ind64 = self.enhancer(fs_in)
-        GeOut = self.reconstruct(fs_in, loc, memstar = [GeMemNorm256, GeMemNorm128, GeMemNorm64])
-        if sp_256 is not None and sp_128 is not None and sp_64 is not None:
-            GSMemNorm256, GSMemNorm128, GSMemNorm64, _, _, _ = self.enhancer(fs_in, sp_256, sp_128, sp_64)
-            GSOut = self.reconstruct(fs_in, loc, memstar = [GSMemNorm256, GSMemNorm128, GSMemNorm64])
-        else:
-            GSOut = None
-        return GeOut, GSOut
-
-class UpResBlock(nn.Module):
-    def __init__(self, dim, conv_layer = nn.Conv2d, norm_layer = nn.BatchNorm2d):
-        super(UpResBlock, self).__init__()
-        self.Model = nn.Sequential(
-            SpectralNorm(conv_layer(dim, dim, 3, 1, 1)),
-            nn.LeakyReLU(0.2),
-            SpectralNorm(conv_layer(dim, dim, 3, 1, 1)),
-        )
-    def forward(self, x):
-        out = x + self.Model(x)
-        return out

roop-unleashed-main/roop/processors/Enhance_GFPGAN.py

@@ -1,73 +0,0 @@
-from typing import Any, List, Callable
-import cv2 
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-class Enhance_GFPGAN():
-    plugin_options:dict = None
-
-    model_gfpgan = None
-    name = None
-    devicename = None
-
-    processorname = 'gfpgan'
-    type = 'enhance'
-
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_gfpgan is None:
-            model_path = resolve_relative_path('../models/GFPGANv1.4.onnx')
-            self.model_gfpgan = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-
-        self.name = self.model_gfpgan.get_inputs()[0].name
-
-    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
-        # preprocess
-        input_size = temp_frame.shape[1]
-        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
-
-        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
-        temp_frame = temp_frame.astype('float32') / 255.0
-        temp_frame = (temp_frame - 0.5) / 0.5
-        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-
-        io_binding = self.model_gfpgan.io_binding()           
-        io_binding.bind_cpu_input("input", temp_frame)
-        io_binding.bind_output("1288", self.devicename)
-        self.model_gfpgan.run_with_iobinding(io_binding)
-        ort_outs = io_binding.copy_outputs_to_cpu()
-        result = ort_outs[0][0]
-
-        # post-process
-        result = np.clip(result, -1, 1)
-        result = (result + 1) / 2
-        result = result.transpose(1, 2, 0) * 255.0
-        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
-        scale_factor = int(result.shape[1] / input_size)       
-        return result.astype(np.uint8), scale_factor
-
-
-    def Release(self):
-        self.model_gfpgan = None
-
-
-
-
-
-
-
-
-
-
-

roop-unleashed-main/roop/processors/Enhance_GPEN.py

@@ -1,63 +0,0 @@
-from typing import Any, List, Callable
-import cv2 
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-
-class Enhance_GPEN():
-    plugin_options:dict = None
-
-    model_gpen = None
-    name = None
-    devicename = None
-
-    processorname = 'gpen'
-    type = 'enhance'
-
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_gpen is None:
-            model_path = resolve_relative_path('../models/GPEN-BFR-512.onnx')
-            self.model_gpen = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-
-        self.name = self.model_gpen.get_inputs()[0].name
-
-    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
-        # preprocess
-        input_size = temp_frame.shape[1]
-        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
-
-        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
-        temp_frame = temp_frame.astype('float32') / 255.0
-        temp_frame = (temp_frame - 0.5) / 0.5
-        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-
-        io_binding = self.model_gpen.io_binding()           
-        io_binding.bind_cpu_input("input", temp_frame)
-        io_binding.bind_output("output", self.devicename)
-        self.model_gpen.run_with_iobinding(io_binding)
-        ort_outs = io_binding.copy_outputs_to_cpu()
-        result = ort_outs[0][0]
-
-        # post-process
-        result = np.clip(result, -1, 1)
-        result = (result + 1) / 2
-        result = result.transpose(1, 2, 0) * 255.0
-        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
-        scale_factor = int(result.shape[1] / input_size)       
-        return result.astype(np.uint8), scale_factor
-
-
-    def Release(self):
-        self.model_gpen = None

roop-unleashed-main/roop/processors/Enhance_RestoreFormerPPlus.py

@@ -1,64 +0,0 @@
-from typing import Any, List, Callable
-import cv2 
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-class Enhance_RestoreFormerPPlus():
-    plugin_options:dict = None
-    model_restoreformerpplus = None
-    devicename = None
-    name = None
-
-    processorname = 'restoreformer++'
-    type = 'enhance'
-    
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_restoreformerpplus is None:
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-            model_path = resolve_relative_path('../models/restoreformer_plus_plus.onnx')
-            self.model_restoreformerpplus = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            self.model_inputs = self.model_restoreformerpplus.get_inputs()
-            model_outputs = self.model_restoreformerpplus.get_outputs()
-            self.io_binding = self.model_restoreformerpplus.io_binding()
-            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
-    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
-        # preprocess
-        input_size = temp_frame.shape[1]
-        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
-        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
-        temp_frame = temp_frame.astype('float32') / 255.0
-        temp_frame = (temp_frame - 0.5) / 0.5
-        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-        
-        self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame) # .astype(np.float32)
-        self.model_restoreformerpplus.run_with_iobinding(self.io_binding)
-        ort_outs = self.io_binding.copy_outputs_to_cpu()
-        result = ort_outs[0][0]
-        del ort_outs 
-        
-        result = np.clip(result, -1, 1)
-        result = (result + 1) / 2
-        result = result.transpose(1, 2, 0) * 255.0
-        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
-        scale_factor = int(result.shape[1] / input_size)       
-        return result.astype(np.uint8), scale_factor
-
-
-    def Release(self):
-        del self.model_restoreformerpplus
-        self.model_restoreformerpplus = None
-        del self.io_binding
-        self.io_binding = None
-

roop-unleashed-main/roop/processors/FaceSwapInsightFace.py

@@ -1,60 +0,0 @@
-import roop.globals
-import numpy as np
-import onnx
-import onnxruntime
-
-from roop.typing import Face, Frame
-from roop.utilities import resolve_relative_path
-
-
-
-class FaceSwapInsightFace():
-    plugin_options:dict = None
-    model_swap_insightface = None
-
-    processorname = 'faceswap'
-    type = 'swap'
-
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"] or self.plugin_options["modelname"] != plugin_options["modelname"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_swap_insightface is None:
-            model_path = resolve_relative_path('../models/' + self.plugin_options["modelname"])
-            graph = onnx.load(model_path).graph
-            self.emap = onnx.numpy_helper.to_array(graph.initializer[-1])
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-            self.input_mean = 0.0
-            self.input_std = 255.0
-            #cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'}            
-            sess_options = onnxruntime.SessionOptions()
-            sess_options.enable_cpu_mem_arena = False
-            self.model_swap_insightface = onnxruntime.InferenceSession(model_path, sess_options, providers=roop.globals.execution_providers)
-
-
-
-    def Run(self, source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-        latent = source_face.normed_embedding.reshape((1,-1))
-        latent = np.dot(latent, self.emap)
-        latent /= np.linalg.norm(latent)
-        io_binding = self.model_swap_insightface.io_binding()           
-        io_binding.bind_cpu_input("target", temp_frame)
-        io_binding.bind_cpu_input("source", latent)
-        io_binding.bind_output("output", self.devicename)
-        self.model_swap_insightface.run_with_iobinding(io_binding)
-        ort_outs = io_binding.copy_outputs_to_cpu()[0]
-        return ort_outs[0]
-
-
-    def Release(self):
-        del self.model_swap_insightface
-        self.model_swap_insightface = None
-
-
-                
-
-
-

roop-unleashed-main/roop/processors/Frame_Colorizer.py

@@ -1,70 +0,0 @@
-import cv2 
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.utilities import resolve_relative_path
-from roop.typing import Frame
-
-class Frame_Colorizer():
-    plugin_options:dict = None
-    model_colorizer = None
-    devicename = None
-    prev_type = None
-
-    processorname = 'deoldify'
-    type = 'frame_colorizer'
-    
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.prev_type is not None and self.prev_type != self.plugin_options["subtype"]:
-            self.Release()
-        self.prev_type = self.plugin_options["subtype"]
-        if self.model_colorizer is None:
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-            if self.prev_type == "deoldify_artistic":
-                model_path = resolve_relative_path('../models/Frame/deoldify_artistic.onnx')
-            elif self.prev_type == "deoldify_stable":
-                model_path = resolve_relative_path('../models/Frame/deoldify_stable.onnx')
-
-            onnxruntime.set_default_logger_severity(3)
-            self.model_colorizer = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            self.model_inputs = self.model_colorizer.get_inputs()
-            model_outputs = self.model_colorizer.get_outputs()
-            self.io_binding = self.model_colorizer.io_binding()
-            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
-    def Run(self, input_frame: Frame) -> Frame:
-        temp_frame = cv2.cvtColor(input_frame, cv2.COLOR_BGR2GRAY)
-        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB)
-        temp_frame = cv2.resize(temp_frame, (256, 256))
-        temp_frame = temp_frame.transpose((2, 0, 1))
-        temp_frame = np.expand_dims(temp_frame, axis=0).astype(np.float32)
-        self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame)
-        self.model_colorizer.run_with_iobinding(self.io_binding)
-        ort_outs = self.io_binding.copy_outputs_to_cpu()
-        result = ort_outs[0][0]
-        del ort_outs
-        colorized_frame = result.transpose(1, 2, 0)
-        colorized_frame = cv2.resize(colorized_frame, (input_frame.shape[1], input_frame.shape[0]))
-        temp_blue_channel, _, _ = cv2.split(input_frame)
-        colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_BGR2RGB).astype(np.uint8)
-        colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_BGR2LAB)
-        _, color_green_channel, color_red_channel = cv2.split(colorized_frame)
-        colorized_frame = cv2.merge((temp_blue_channel, color_green_channel, color_red_channel))
-        colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_LAB2BGR)
-        return colorized_frame.astype(np.uint8)
-
-
-    def Release(self):
-        del self.model_colorizer
-        self.model_colorizer = None
-        del self.io_binding
-        self.io_binding = None
-

roop-unleashed-main/roop/processors/Frame_Filter.py

@@ -1,105 +0,0 @@
-import cv2
-import numpy as np
-
-from roop.typing import Frame
-
-class Frame_Filter():
-    processorname = 'generic_filter'
-    type = 'frame_processor'
-
-    plugin_options:dict = None
-
-    c64_palette = np.array([
-            [0, 0, 0],
-            [255, 255, 255],
-            [0x81, 0x33, 0x38],
-            [0x75, 0xce, 0xc8],
-            [0x8e, 0x3c, 0x97],
-            [0x56, 0xac, 0x4d],
-            [0x2e, 0x2c, 0x9b],
-            [0xed, 0xf1, 0x71],
-            [0x8e, 0x50, 0x29],
-            [0x55, 0x38, 0x00],
-            [0xc4, 0x6c, 0x71],
-            [0x4a, 0x4a, 0x4a],
-            [0x7b, 0x7b, 0x7b],
-            [0xa9, 0xff, 0x9f],
-            [0x70, 0x6d, 0xeb],
-            [0xb2, 0xb2, 0xb2]
-        ])
-
-
-    def RenderC64Screen(self, image):
-        # Simply round the color values to the nearest color in the palette
-        image = cv2.resize(image,(320,200))
-        palette = self.c64_palette / 255.0  # Normalize palette
-        img_normalized = image  / 255.0  # Normalize image
-
-        # Calculate the index in the palette that is closest to each pixel in the image
-        indices = np.sqrt(((img_normalized[:, :, None, :] - palette[None, None, :, :]) ** 2).sum(axis=3)).argmin(axis=2)
-        # Map the image to the palette colors
-        mapped_image = palette[indices]
-        return (mapped_image * 255).astype(np.uint8)  # Denormalize and return the image
-
-
-    def RenderDetailEnhance(self, image):
-        return cv2.detailEnhance(image)
-
-    def RenderStylize(self, image):
-        return cv2.stylization(image)
-    
-    def RenderPencilSketch(self, image):
-        imgray, imout = cv2.pencilSketch(image, sigma_s=60, sigma_r=0.07, shade_factor=0.05)
-        return imout
-    
-    def RenderCartoon(self, image):
-        numDownSamples = 2 # number of downscaling steps
-        numBilateralFilters = 7  # number of bilateral filtering steps
-
-        img_color = image
-        for _ in range(numDownSamples):
-            img_color = cv2.pyrDown(img_color)
-        for _ in range(numBilateralFilters):
-            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
-        for _ in range(numDownSamples):
-            img_color = cv2.pyrUp(img_color)
-        img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-        img_blur = cv2.medianBlur(img_gray, 7)
-        img_edge = cv2.adaptiveThreshold(img_blur, 255,
-            cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 2)
-        img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)
-        if img_color.shape != image.shape:
-            img_color = cv2.resize(img_color, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR)        
-        if img_color.shape != img_edge.shape:
-            img_edge = cv2.resize(img_edge, (img_color.shape[1], img_color.shape[0]), interpolation=cv2.INTER_LINEAR)        
-        return cv2.bitwise_and(img_color, img_edge)
-    
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-        self.plugin_options = plugin_options
-
-    def Run(self, temp_frame: Frame) -> Frame:
-        subtype = self.plugin_options["subtype"]
-        if subtype == "stylize":
-            return self.RenderStylize(temp_frame).astype(np.uint8)
-        if subtype == "detailenhance":
-            return self.RenderDetailEnhance(temp_frame).astype(np.uint8)
-        if subtype == "pencil":
-            return self.RenderPencilSketch(temp_frame).astype(np.uint8)
-        if subtype == "cartoon":
-            return self.RenderCartoon(temp_frame).astype(np.uint8)
-        if subtype == "C64":
-            return self.RenderC64Screen(temp_frame).astype(np.uint8)
-
-
-    def Release(self):
-        pass
-
-    def getProcessedResolution(self, width, height):
-        if self.plugin_options["subtype"] == "C64":
-            return (320,200)
-        return None
-

roop-unleashed-main/roop/processors/Frame_Masking.py

@@ -1,71 +0,0 @@
-import cv2 
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.utilities import resolve_relative_path
-from roop.typing import Frame
-
-class Frame_Masking():
-    plugin_options:dict = None
-    model_masking = None
-    devicename = None
-    name = None
-
-    processorname = 'removebg'
-    type = 'frame_masking'
-    
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_masking is None:
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"]
-            self.devicename = self.devicename.replace('mps', 'cpu')
-            model_path = resolve_relative_path('../models/Frame/isnet-general-use.onnx')
-            self.model_masking = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            self.model_inputs = self.model_masking.get_inputs()
-            model_outputs = self.model_masking.get_outputs()
-            self.io_binding = self.model_masking.io_binding()
-            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
-    def Run(self, temp_frame: Frame) -> Frame:
-        # Pre process:Resize, BGR->RGB, float32 cast
-        input_image = cv2.resize(temp_frame, (1024, 1024))
-        input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
-        mean = [0.5, 0.5, 0.5]
-        std = [1.0, 1.0, 1.0]
-        input_image = (input_image / 255.0 - mean) / std
-        input_image = input_image.transpose(2, 0, 1)
-        input_image = np.expand_dims(input_image, axis=0)
-        input_image = input_image.astype('float32')
-        
-        self.io_binding.bind_cpu_input(self.model_inputs[0].name, input_image)
-        self.model_masking.run_with_iobinding(self.io_binding)
-        ort_outs = self.io_binding.copy_outputs_to_cpu()
-        result = ort_outs[0][0]
-        del ort_outs
-        # Post process:squeeze, Sigmoid, Normarize, uint8 cast
-        mask = np.squeeze(result[0])
-        min_value = np.min(mask)
-        max_value = np.max(mask)
-        mask = (mask - min_value) / (max_value - min_value)
-        #mask = np.where(mask < score_th, 0, 1)
-        #mask *= 255
-        mask = cv2.resize(mask, (temp_frame.shape[1], temp_frame.shape[0]), interpolation=cv2.INTER_LINEAR)        
-        mask = np.reshape(mask, [mask.shape[0],mask.shape[1],1])
-        result = mask * temp_frame.astype(np.float32)
-        return result.astype(np.uint8)
-
-
-
-    def Release(self):
-        del self.model_masking
-        self.model_masking = None
-        del self.io_binding
-        self.io_binding = None
-

roop-unleashed-main/roop/processors/Frame_Upscale.py

@@ -1,129 +0,0 @@
-import cv2 
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.utilities import resolve_relative_path, conditional_thread_semaphore
-from roop.typing import Frame
-
-
-class Frame_Upscale():
-    plugin_options:dict = None
-    model_upscale = None
-    devicename = None
-    prev_type = None
-
-    processorname = 'upscale'
-    type = 'frame_enhancer'
-
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.prev_type is not None and self.prev_type != self.plugin_options["subtype"]:
-            self.Release()
-        self.prev_type = self.plugin_options["subtype"]
-        if self.model_upscale is None:
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-            if self.prev_type == "esrganx4":
-                model_path = resolve_relative_path('../models/Frame/real_esrgan_x4.onnx')
-                self.scale = 4
-            elif self.prev_type == "esrganx2":
-                model_path = resolve_relative_path('../models/Frame/real_esrgan_x2.onnx')
-                self.scale = 2
-            elif self.prev_type == "lsdirx4":
-                model_path = resolve_relative_path('../models/Frame/lsdir_x4.onnx')
-                self.scale = 4
-            onnxruntime.set_default_logger_severity(3)
-            self.model_upscale = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            self.model_inputs = self.model_upscale.get_inputs()
-            model_outputs = self.model_upscale.get_outputs()
-            self.io_binding = self.model_upscale.io_binding()
-            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
-    def getProcessedResolution(self, width, height):
-        return (width * self.scale, height * self.scale)
-
-# borrowed from facefusion -> https://github.com/facefusion/facefusion
-    def prepare_tile_frame(self, tile_frame : Frame) -> Frame:
-        tile_frame = np.expand_dims(tile_frame[:, :, ::-1], axis = 0)
-        tile_frame = tile_frame.transpose(0, 3, 1, 2)
-        tile_frame = tile_frame.astype(np.float32) / 255
-        return tile_frame
-
-
-    def normalize_tile_frame(self, tile_frame : Frame) -> Frame:
-        tile_frame = tile_frame.transpose(0, 2, 3, 1).squeeze(0) * 255
-        tile_frame = tile_frame.clip(0, 255).astype(np.uint8)[:, :, ::-1]
-        return tile_frame
-
-    def create_tile_frames(self, input_frame : Frame, size):
-        input_frame = np.pad(input_frame, ((size[1], size[1]), (size[1], size[1]), (0, 0)))
-        tile_width = size[0] - 2 * size[2]
-        pad_size_bottom = size[2] + tile_width - input_frame.shape[0] % tile_width
-        pad_size_right = size[2] + tile_width - input_frame.shape[1] % tile_width
-        pad_vision_frame = np.pad(input_frame, ((size[2], pad_size_bottom), (size[2], pad_size_right), (0, 0)))
-        pad_height, pad_width = pad_vision_frame.shape[:2]
-        row_range = range(size[2], pad_height - size[2], tile_width)
-        col_range = range(size[2], pad_width - size[2], tile_width)
-        tile_frames = []
-
-        for row_frame in row_range:
-            top = row_frame - size[2]
-            bottom = row_frame + size[2] + tile_width
-            for column_vision_frame in col_range:
-                left = column_vision_frame - size[2]
-                right = column_vision_frame + size[2] + tile_width
-                tile_frames.append(pad_vision_frame[top:bottom, left:right, :])
-        return tile_frames, pad_width, pad_height
-
-
-    def merge_tile_frames(self, tile_frames, temp_width : int, temp_height : int, pad_width : int, pad_height : int, size) -> Frame:
-        merge_frame = np.zeros((pad_height, pad_width, 3)).astype(np.uint8)
-        tile_width = tile_frames[0].shape[1] - 2 * size[2]
-        tiles_per_row = min(pad_width // tile_width, len(tile_frames))
-
-        for index, tile_frame in enumerate(tile_frames):
-            tile_frame = tile_frame[size[2]:-size[2], size[2]:-size[2]]
-            row_index = index // tiles_per_row
-            col_index = index % tiles_per_row
-            top = row_index * tile_frame.shape[0]
-            bottom = top + tile_frame.shape[0]
-            left = col_index * tile_frame.shape[1]
-            right = left + tile_frame.shape[1]
-            merge_frame[top:bottom, left:right, :] = tile_frame
-        merge_frame = merge_frame[size[1] : size[1] + temp_height, size[1]: size[1] + temp_width, :]
-        return merge_frame
-
-
-    def Run(self, temp_frame: Frame) -> Frame:
-        size = (128, 8, 2)
-        temp_height, temp_width = temp_frame.shape[:2]
-        upscale_tile_frames, pad_width, pad_height = self.create_tile_frames(temp_frame, size)
-
-        for index, tile_frame in enumerate(upscale_tile_frames):
-            tile_frame = self.prepare_tile_frame(tile_frame)
-            with conditional_thread_semaphore():
-                self.io_binding.bind_cpu_input(self.model_inputs[0].name, tile_frame)
-                self.model_upscale.run_with_iobinding(self.io_binding)
-                ort_outs = self.io_binding.copy_outputs_to_cpu()
-                result = ort_outs[0]
-            upscale_tile_frames[index] = self.normalize_tile_frame(result)
-        final_frame = self.merge_tile_frames(upscale_tile_frames, temp_width * self.scale
-                                                    , temp_height * self.scale
-                                                    , pad_width * self.scale, pad_height * self.scale
-                                                    , (size[0] * self.scale, size[1] * self.scale, size[2] * self.scale))
-        return final_frame.astype(np.uint8)
-
-
-
-    def Release(self):
-        del self.model_upscale
-        self.model_upscale = None
-        del self.io_binding
-        self.io_binding = None
-

roop-unleashed-main/roop/processors/Mask_Clip2Seg.py

@@ -1,94 +0,0 @@
-import cv2
-import numpy as np
-import torch
-import threading
-from torchvision import transforms
-from clip.clipseg import CLIPDensePredT
-import numpy as np
-
-from roop.typing import Frame
-
-THREAD_LOCK_CLIP = threading.Lock()
-
-
-class Mask_Clip2Seg():
-    plugin_options:dict = None
-    model_clip = None
-
-    processorname = 'clip2seg'
-    type = 'mask'
-
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_clip is None:
-            self.model_clip = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
-            self.model_clip.eval();
-            self.model_clip.load_state_dict(torch.load('models/CLIP/rd64-uni-refined.pth', map_location=torch.device('cpu')), strict=False)
-
-        device = torch.device(self.plugin_options["devicename"])
-        self.model_clip.to(device)
-
-
-    def Run(self, img1, keywords:str) -> Frame:
-        if keywords is None or len(keywords) < 1 or img1 is None:
-            return img1
-        
-        source_image_small = cv2.resize(img1, (256,256))
-        
-        img_mask = np.full((source_image_small.shape[0],source_image_small.shape[1]), 0, dtype=np.float32)
-        mask_border = 1
-        l = 0
-        t = 0
-        r = 1
-        b = 1
-        
-        mask_blur = 5
-        clip_blur = 5
-        
-        img_mask = cv2.rectangle(img_mask, (mask_border+int(l), mask_border+int(t)), 
-                                (256 - mask_border-int(r), 256-mask_border-int(b)), (255, 255, 255), -1)    
-        img_mask = cv2.GaussianBlur(img_mask, (mask_blur*2+1,mask_blur*2+1), 0)    
-        img_mask /= 255
-
-        
-        input_image = source_image_small
-
-        transform = transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-            transforms.Resize((256, 256)),
-        ])
-        img = transform(input_image).unsqueeze(0)
-
-        thresh = 0.5
-        prompts = keywords.split(',')
-        with THREAD_LOCK_CLIP:
-            with torch.no_grad():
-                preds = self.model_clip(img.repeat(len(prompts),1,1,1), prompts)[0]
-        clip_mask = torch.sigmoid(preds[0][0])
-        for i in range(len(prompts)-1):
-            clip_mask += torch.sigmoid(preds[i+1][0])
-           
-        clip_mask = clip_mask.data.cpu().numpy()
-        np.clip(clip_mask, 0, 1)
-        
-        clip_mask[clip_mask>thresh] = 1.0
-        clip_mask[clip_mask<=thresh] = 0.0
-        kernel = np.ones((5, 5), np.float32)
-        clip_mask = cv2.dilate(clip_mask, kernel, iterations=1)
-        clip_mask = cv2.GaussianBlur(clip_mask, (clip_blur*2+1,clip_blur*2+1), 0)
-       
-        img_mask *= clip_mask
-        img_mask[img_mask<0.0] = 0.0
-        return img_mask
-       
-
-
-    def Release(self):
-        self.model_clip = None
-

roop-unleashed-main/roop/processors/Mask_XSeg.py

@@ -1,58 +0,0 @@
-import numpy as np
-import cv2
-import onnxruntime
-import roop.globals
-
-from roop.typing import Frame
-from roop.utilities import resolve_relative_path, conditional_thread_semaphore
-
-
-
-class Mask_XSeg():
-    plugin_options:dict = None
-
-    model_xseg = None
-
-    processorname = 'mask_xseg'
-    type = 'mask'
-
-
-    def Initialize(self, plugin_options:dict):
-        if self.plugin_options is not None:
-            if self.plugin_options["devicename"] != plugin_options["devicename"]:
-                self.Release()
-
-        self.plugin_options = plugin_options
-        if self.model_xseg is None:
-            model_path = resolve_relative_path('../models/xseg.onnx')
-            onnxruntime.set_default_logger_severity(3)
-            self.model_xseg = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
-            self.model_inputs = self.model_xseg.get_inputs()
-            self.model_outputs = self.model_xseg.get_outputs()
-
-            # replace Mac mps with cpu for the moment
-            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-
-
-    def Run(self, img1, keywords:str) -> Frame:
-        temp_frame = cv2.resize(img1, (256, 256), cv2.INTER_CUBIC)
-        temp_frame = temp_frame.astype('float32') / 255.0
-        temp_frame = temp_frame[None, ...]
-        io_binding = self.model_xseg.io_binding()           
-        io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame)
-        io_binding.bind_output(self.model_outputs[0].name, self.devicename)
-        self.model_xseg.run_with_iobinding(io_binding)
-        ort_outs = io_binding.copy_outputs_to_cpu()
-        result = ort_outs[0][0]
-        result = np.clip(result, 0, 1.0)
-        result[result < 0.1] = 0
-        # invert values to mask areas to keep
-        result = 1.0 - result
-        return result       
-
-
-    def Release(self):
-        del self.model_xseg
-        self.model_xseg = None
-
-

roop-unleashed-main/roop/template_parser.py

@@ -1,23 +0,0 @@
-import re
-from datetime import datetime
-
-template_functions = {
-    "timestamp": lambda data: str(int(datetime.now().timestamp())),
-    "i": lambda data: data.get("index", False),
-    "file": lambda data: data.get("file", False),
-    "date": lambda data: datetime.now().strftime("%Y-%m-%d"),
-    "time": lambda data: datetime.now().strftime("%H-%M-%S"),
-}
-
-
-def parse(text: str, data: dict):
-    pattern = r"\{([^}]+)\}"
-
-    matches = re.findall(pattern, text)
-
-    for match in matches:
-        replacement = template_functions[match](data)
-        if replacement is not False:
-            text = text.replace(f"{{{match}}}", replacement)
-
-    return text

roop-unleashed-main/roop/typing.py

@@ -1,9 +0,0 @@
-from typing import Any
-
-from insightface.app.common import Face
-from roop.FaceSet import FaceSet
-import numpy
-
-Face = Face
-FaceSet = FaceSet
-Frame = numpy.ndarray[Any, Any]

roop-unleashed-main/roop/util_ffmpeg.py

@@ -1,130 +0,0 @@
-
-import os
-import subprocess
-import roop.globals
-import roop.utilities as util
-
-from typing import List, Any
-
-def run_ffmpeg(args: List[str]) -> bool:
-    commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-y', '-loglevel', roop.globals.log_level]
-    commands.extend(args)
-    print ("Running ffmpeg")
-    try:
-        subprocess.check_output(commands, stderr=subprocess.STDOUT)
-        return True
-    except Exception as e:
-        print("Running ffmpeg failed! Commandline:")
-        print (" ".join(commands))
-    return False
-
-
-
-def cut_video(original_video: str, cut_video: str, start_frame: int, end_frame: int, reencode: bool):
-    fps = util.detect_fps(original_video)
-    start_time = start_frame / fps
-    num_frames = end_frame - start_frame
-
-    if reencode:
-        run_ffmpeg(['-ss',  format(start_time, ".2f"), '-i', original_video, '-c:v', roop.globals.video_encoder, '-c:a', 'aac', '-frames:v', str(num_frames), cut_video])
-    else:
-        run_ffmpeg(['-ss',  format(start_time, ".2f"), '-i', original_video,  '-frames:v', str(num_frames), '-c:v' ,'copy','-c:a' ,'copy', cut_video])
-
-def join_videos(videos: List[str], dest_filename: str, simple: bool):
-    if simple:
-        txtfilename = util.resolve_relative_path('../temp')
-        txtfilename = os.path.join(txtfilename, 'joinvids.txt')
-        with open(txtfilename, "w", encoding="utf-8") as f:
-            for v in videos:
-                 v = v.replace('\\', '/')
-                 f.write(f"file {v}\n")
-        commands = ['-f', 'concat', '-safe', '0', '-i', f'{txtfilename}', '-vcodec', 'copy', f'{dest_filename}']
-        run_ffmpeg(commands)
-
-    else:
-        inputs = []
-        filter = ''
-        for i,v in enumerate(videos):
-            inputs.append('-i')
-            inputs.append(v)
-            filter += f'[{i}:v:0][{i}:a:0]'
-        run_ffmpeg([" ".join(inputs), '-filter_complex', f'"{filter}concat=n={len(videos)}:v=1:a=1[outv][outa]"', '-map', '"[outv]"', '-map', '"[outa]"', dest_filename])    
-
-        #     filter += f'[{i}:v:0][{i}:a:0]'
-        # run_ffmpeg([" ".join(inputs), '-filter_complex', f'"{filter}concat=n={len(videos)}:v=1:a=1[outv][outa]"', '-map', '"[outv]"', '-map', '"[outa]"', dest_filename])    
-
-
-
-def extract_frames(target_path : str, trim_frame_start, trim_frame_end, fps : float) -> bool:
-    util.create_temp(target_path)
-    temp_directory_path = util.get_temp_directory_path(target_path)
-    commands = ['-i', target_path, '-q:v', '1', '-pix_fmt', 'rgb24', ]
-    if trim_frame_start is not None and trim_frame_end is not None:
-        commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
-    commands.extend(['-vsync', '0', os.path.join(temp_directory_path, '%06d.' + roop.globals.CFG.output_image_format)])
-    return run_ffmpeg(commands)
-
-
-def create_video(target_path: str, dest_filename: str, fps: float = 24.0, temp_directory_path: str = None) -> None:
-    if temp_directory_path is None:
-        temp_directory_path = util.get_temp_directory_path(target_path)
-    run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, f'%06d.{roop.globals.CFG.output_image_format}'), '-c:v', roop.globals.video_encoder, '-crf', str(roop.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', dest_filename])
-    return dest_filename
-
-
-def create_gif_from_video(video_path: str, gif_path):
-    from roop.capturer import get_video_frame, release_video
-
-    fps = util.detect_fps(video_path)
-    frame = get_video_frame(video_path)
-    release_video()
-
-    scalex = frame.shape[0]
-    scaley = frame.shape[1]
-
-    if scalex >= scaley:
-        scaley = -1
-    else:
-        scalex = -1
-
-    run_ffmpeg(['-i', video_path, '-vf', f'fps={fps},scale={int(scalex)}:{int(scaley)}:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse', '-loop', '0', gif_path])
-
-
-
-def create_video_from_gif(gif_path: str, output_path):
-    fps = util.detect_fps(gif_path)
-    filter = """scale='trunc(in_w/2)*2':'trunc(in_h/2)*2',format=yuv420p,fps=10"""
-    run_ffmpeg(['-i', gif_path, '-vf', f'"{filter}"', '-movflags', '+faststart', '-shortest', output_path])
-
-
-def repair_video(original_video: str, final_video : str):
-    run_ffmpeg(['-i', original_video, '-movflags', 'faststart', '-acodec', 'copy', '-vcodec', 'copy', final_video])
-
-
-def restore_audio(intermediate_video: str, original_video: str, trim_frame_start, trim_frame_end, final_video : str) -> None:
-	fps = util.detect_fps(original_video)
-	commands = [ '-i', intermediate_video ]
-	if trim_frame_start is None and trim_frame_end is None:
-		commands.extend([ '-c:a', 'copy' ])
-	else:
-		# if trim_frame_start is not None:
-		# 	start_time = trim_frame_start / fps
-		# 	commands.extend([ '-ss', format(start_time, ".2f")])
-		# else:
-		# 	commands.extend([ '-ss', '0' ])
-		# if trim_frame_end is not None:
-		# 	end_time = trim_frame_end / fps
-		# 	commands.extend([ '-to', format(end_time, ".2f")])
-		# commands.extend([ '-c:a', 'aac' ])
-		if trim_frame_start is not None:
-			start_time = trim_frame_start / fps
-			commands.extend([ '-ss', format(start_time, ".2f")])
-		else:
-			commands.extend([ '-ss', '0' ])
-		if trim_frame_end is not None:
-			end_time = trim_frame_end / fps
-			commands.extend([ '-to', format(end_time, ".2f")])
-		commands.extend([ '-i', original_video, "-c",  "copy" ])
-
-	commands.extend([ '-map', '0:v:0', '-map', '1:a:0?', '-shortest', final_video ])
-	run_ffmpeg(commands)