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README.md
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<div align="center"> <div align="center">
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/ReActor_logo_red.png?raw=true" alt="logo" width="180px"/> <img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/ReActor_logo_NEW_EN.png?raw=true" alt="logo" width="180px"/>
![Version](https://img.shields.io/badge/version-0.5.0-brightgreen?style=for-the-badge&labelColor=darkgreen) ![Version](https://img.shields.io/badge/version-0.7.0_beta6-green?style=for-the-badge&labelColor=darkgreen)
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<img src="https://lovemet.ru/www/boosty.jpg" width="108" alt="Support Me on Boosty"/>
<br>
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Support This Project
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<hr> <hr>
@ -22,45 +28,91 @@
--- ---
<b> <b>
<a href="#installation">Installation</a> | <a href="#features">Features</a> | <a href="#usage">Usage</a> | <a href="#api">API</a> | <a href="#troubleshooting">Troubleshooting</a> | <a href="#updating">Updating</a> | <a href="#comfyui">ComfyUI</a> | <a href="#disclaimer">Disclaimer</a> <a href="#latestupdate">What's new</a> | <a href="#installation">Installation</a> | <a href="#features">Features</a> | <a href="#usage">Usage</a> | <a href="#api">API</a> | <a href="#troubleshooting">Troubleshooting</a> | <a href="#updating">Updating</a> | <a href="#comfyui">ComfyUI</a> | <a href="#disclaimer">Disclaimer</a>
</b> </b>
</div> </div>
--- ---
<table>
<tr>
<td width="134px">
<a href="https://boosty.to/artgourieff" target="_blank">
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Support This Project
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<td>
ReActor is an extension for Stable Diffusion WebUI that allows a very easy and accurate face-replacement (face swap) in images. Based on <a href="https://github.com/Gourieff/ReActor-UI" target="_blank">ReActor-UI</a>.
</td>
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<a href="https://paypal.me/artgourieff" target="_blank">
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<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/demo_crop.jpg?raw=true" alt="example"/> <img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/demo_crop.jpg?raw=true" alt="example"/>
<a name="latestupdate">
## What's new in the latest updates
<details>
<summary><a>Click to expand</a></summary>
### 0.7.0 <sub><sup>BETA2
- X/Y/Z is improved! One more parameter is ready: you can now select several face models to create a variation of swaps to choose the best one!
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-05.jpg?raw=true" alt="0.7.0-whatsnew-05" width="100%"/>
To use "Face Model" axis - you should enable ReActor and choose any face model as the Source:<br>
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-07.jpg?raw=true" alt="0.7.0-whatsnew-07" width="50%"/><img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-06.jpg?raw=true" alt="0.7.0-whatsnew-06" width="50%"/>
Full size demo image: [xyz_demo_2.png](https://raw.githubusercontent.com/Gourieff/Assets/main/sd-webui-reactor/xyz_demo_2.png)
### 0.7.0 <sub><sup>BETA1
- X/Y/Z Script support (up to 3 axes: CodeFormer Weight, Restorer Visibility, Face Mask Correction)
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-03.jpg?raw=true" alt="0.7.0-whatsnew-03" width="100%"/>
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-04.jpg?raw=true" alt="0.7.0-whatsnew-04" width="100%"/>
Full size demo image: [xyz_demo.png](https://raw.githubusercontent.com/Gourieff/Assets/main/sd-webui-reactor/xyz_demo.png)
__Don't forget to enable ReActor and set any source (to prevent "no source" error)__
### 0.7.0 <sub><sup>ALPHA1
- You can now blend faces to build blended face models ("Tools->Face Models->Blend") - due to popular demand
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-01.jpg?raw=true" alt="0.7.0-whatsnew-01" width="100%"/><img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-02.jpg?raw=true" alt="0.7.0-whatsnew-02" width="100%"/>
- CUDA 12 Support in the Installer script for 1.17.0 ORT-GPU library
- New tab "Detection" with "Threshold" and "Max Faces" parameters
### 0.6.1 <sub><sup>BETA3
- 'Force Upscale' option inside the 'Upscale' tab: ReActor will run the Upscaler even if there's no face is detected (FR https://github.com/Gourieff/sd-webui-reactor/issues/116)
- ReActor shows filenames of source images in-process when the multiple images mode or the folder mode (random as well) is selected
### 0.6.1 <sub><sup>BETA2
- 'Save original' option works fine now when you select 'Multiple Images' or 'Source Folder'
- Random Mode for 'Source Folder'
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/random_from_folder_demo_01.jpg?raw=true" alt="0.6.1-whatsnew-01" width="100%"/>
### 0.6.0
- New Logo
- Adaptation to A1111 1.7.0 (appropriate GFPGAN loader)
- New URL for the main model file
- UI reworked
- You can now load several source images (with reference faces) or set the path to the folder containing faces images
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/multiple_source_images_demo_01.png?raw=true" alt="0.6.0-whatsnew-01" width="100%"/>
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/multiple_source_images_demo_02.png?raw=true" alt="0.6.0-whatsnew-02" width="100%"/>
### 0.5.1
- You can save face models as "safetensors" files (stored in `<sd-web-ui-folder>\models\reactor\faces`) and load them into ReActor, keeping super lightweight face models of the faces you use;
- "Face Mask Correction" option - if you encounter some pixelation around face contours, this option will be useful;
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/face_model_demo_01.jpg?raw=true" alt="0.5.0-whatsnew-01" width="100%"/>
</details>
## Installation ## Installation
[Automatic1111](#a1111) | [Vladmandic SD.Next](#sdnext) | [Google Colab SD WebUI](#colab) [A1111 WebUI / WebUI-Forge](#a1111) | [SD.Next](#sdnext) | [Google Colab SD WebUI](#colab)
<a name="a1111">If you use [AUTOMATIC1111 web-ui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/): <a name="a1111">If you use [AUTOMATIC1111 SD WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui/) or [SD WebUI Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge):
1. (For Windows Users): 1. (For Windows Users):
- Install **Visual Studio 2022** (Community version, for example - you need this step to build some of dependencies): - Install **Visual Studio 2022** (Community version, for example - you need this step to build some of dependencies):
@ -68,8 +120,8 @@
- OR only **VS C++ Build Tools** (if you don't need the whole Visual Studio) and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile": - OR only **VS C++ Build Tools** (if you don't need the whole Visual Studio) and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile":
https://visualstudio.microsoft.com/visual-cpp-build-tools/ https://visualstudio.microsoft.com/visual-cpp-build-tools/
- OR if you don't want to install VS or VS C++ BT - follow [this steps (sec. VIII)](#insightfacebuild) - OR if you don't want to install VS or VS C++ BT - follow [this steps (sec. VIII)](#insightfacebuild)
2. In web-ui, go to the "Extensions" tab and use this URL `https://github.com/Gourieff/sd-webui-reactor` in the "Install from URL" tab and click "Install" 2. In web-ui, go to the "Extensions" tab, load "Available" extensions and type "ReActor" in the search field or use this URL `https://github.com/Gourieff/sd-webui-reactor` in the "Install from URL" tab - and click "Install"
3. Please, wait for several minutes until the installation process will be finished 3. Please, wait for several minutes until the installation process will be finished (be patient, don't interrupt the process)
4. Check the last message in your SD-WebUI Console: 4. Check the last message in your SD-WebUI Console:
* If you see the message "--- PLEASE, RESTART the Server! ---" - so, do it, stop the Server (CTRL+C or CMD+C) and start it again - or just go to the "Installed" tab, click "Apply and restart UI" * If you see the message "--- PLEASE, RESTART the Server! ---" - so, do it, stop the Server (CTRL+C or CMD+C) and start it again - or just go to the "Installed" tab, click "Apply and restart UI"
* If you see the message "Done!", just reload the UI * If you see the message "Done!", just reload the UI
@ -82,7 +134,7 @@
3. Go to (Windows)`automatic\venv\Scripts` or (MacOS/Linux)`automatic/venv/bin`, run Terminal or Console (cmd) for that folder and type `activate` 3. Go to (Windows)`automatic\venv\Scripts` or (MacOS/Linux)`automatic/venv/bin`, run Terminal or Console (cmd) for that folder and type `activate`
4. Run `pip install insightface==0.7.3` 4. Run `pip install insightface==0.7.3`
5. Run SD.Next, go to the "Extensions" tab and use this URL `https://github.com/Gourieff/sd-webui-reactor` in the "Install from URL" tab and click "Install" 5. Run SD.Next, go to the "Extensions" tab and use this URL `https://github.com/Gourieff/sd-webui-reactor` in the "Install from URL" tab and click "Install"
6. Please, wait for several minutes until the installation process will be finished 6. Please, wait for several minutes until the installation process will be finished (be patient, don't interrupt the process)
7. Check the last message in your SD.Next Console: 7. Check the last message in your SD.Next Console:
* If you see the message "--- PLEASE, RESTART the Server! ---" - stop the Server (CTRL+C or CMD+C) or just close your console * If you see the message "--- PLEASE, RESTART the Server! ---" - stop the Server (CTRL+C or CMD+C) or just close your console
8. Go to the `automatic\extensions\sd-webui-reactor` directory - if you see there `models\insightface` folder with the file `inswapper_128.onnx`, just move the file to the `automatic\models\insightface` folder 8. Go to the `automatic\extensions\sd-webui-reactor` directory - if you see there `models\insightface` folder with the file `inswapper_128.onnx`, just move the file to the `automatic\models\insightface` folder
@ -90,8 +142,8 @@
<a name="colab">If you use [Cagliostro Colab UI](https://github.com/Linaqruf/sd-notebook-collection): <a name="colab">If you use [Cagliostro Colab UI](https://github.com/Linaqruf/sd-notebook-collection):
1. In active WebUI, go to the "Extensions" tab and use this URL `https://github.com/Gourieff/sd-webui-reactor` in the "Install from URL" tab and click "Install" 1. In active WebUI, go to the "Extensions" tab, load "Available" extensions and type "ReActor" in the search field or use this URL `https://github.com/Gourieff/sd-webui-reactor` in the "Install from URL" tab - and click "Install"
2. Please, wait for several minutes until the installation process will be finished 2. Please, wait for several minutes until the installation process will be finished (be patient, don't interrupt the process)
3. When you see the message "--- PLEASE, RESTART the Server! ---" (in your Colab Notebook Start UI section "Start Cagliostro Colab UI") - just go to the "Installed" tab and click "Apply and restart UI" 3. When you see the message "--- PLEASE, RESTART the Server! ---" (in your Colab Notebook Start UI section "Start Cagliostro Colab UI") - just go to the "Installed" tab and click "Apply and restart UI"
4. Enjoy! 4. Enjoy!
@ -103,10 +155,12 @@
- Ability to **save original images** (made before swapping) - Ability to **save original images** (made before swapping)
- **Face restoration** of a swapped face - **Face restoration** of a swapped face
- **Upscaling** of a resulting image - **Upscaling** of a resulting image
- Saving ans loading **Safetensors Face Models**
- **Facial Mask Correction** to avoid any pixelation around face contours
- Ability to set the **Postprocessing order** - Ability to set the **Postprocessing order**
- **100% compatibility** with different **SD WebUIs**: Automatic1111, SD.Next, Cagliostro Colab UI - **100% compatibility** with different **SD WebUIs**: Automatic1111, SD.Next, Cagliostro Colab UI
- **Fast performance** even with CPU, ReActor for SD WebUI is absolutely not picky about how powerful your GPU is - **Fast performance** even with CPU, ReActor for SD WebUI is absolutely not picky about how powerful your GPU is
- **CUDA** acceleration support from the version 0.5.0 - **CUDA** acceleration support since version 0.5.0
- **[API](/API.md) support**: both SD WebUI built-in and external (via POST/GET requests) - **[API](/API.md) support**: both SD WebUI built-in and external (via POST/GET requests)
- **ComfyUI [support](https://github.com/Gourieff/comfyui-reactor-node)** - **ComfyUI [support](https://github.com/Gourieff/comfyui-reactor-node)**
- **Mac M1/M2 [support](https://github.com/Gourieff/sd-webui-reactor/issues/42)** - **Mac M1/M2 [support](https://github.com/Gourieff/sd-webui-reactor/issues/42)**
@ -198,7 +252,7 @@ Please, check the path where "inswapper_128.onnx" model is stored. It must be in
7. Then one-by-one: 7. Then one-by-one:
- `pip install insightface==0.7.3` - `pip install insightface==0.7.3`
- `pip install onnx` - `pip install onnx`
- `pip install onnxruntime-gpu>=1.16.1` - `pip install "onnxruntime-gpu>=1.16.1"`
- `pip install opencv-python` - `pip install opencv-python`
- `pip install tqdm` - `pip install tqdm`
8. Type `deactivate`, you can close your Terminal or Console and start your SD WebUI, ReActor should start OK - if not, welcome to the Issues section. 8. Type `deactivate`, you can close your Terminal or Console and start your SD WebUI, ReActor should start OK - if not, welcome to the Issues section.
@ -222,7 +276,7 @@ Probably, you need to disable the "SD-CN-Animation" extension (or perhaps some a
This error may occur if there's smth wrong with the model file `inswapper_128.onnx` This error may occur if there's smth wrong with the model file `inswapper_128.onnx`
Try to download it manually from [here](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) Try to download it manually from [here](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx)
and put it to the `stable-diffusion-webui\models\insightface` replacing existing one and put it to the `stable-diffusion-webui\models\insightface` replacing existing one
### **VI. "ValueError: This ORT build has ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'] enabled" OR "ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled"** ### **VI. "ValueError: This ORT build has ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'] enabled" OR "ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled"**
@ -233,7 +287,7 @@ and put it to the `stable-diffusion-webui\models\insightface` replacing existing
4. Then: 4. Then:
- `python -m pip install -U pip` - `python -m pip install -U pip`
- `pip uninstall -y onnxruntime onnxruntime-gpu onnxruntime-silicon onnxruntime-extensions` - `pip uninstall -y onnxruntime onnxruntime-gpu onnxruntime-silicon onnxruntime-extensions`
- `pip install onnxruntime-gpu>=1.16.1` - `pip install "onnxruntime-gpu>=1.16.1"`
If it didn't help - it seems that you have another extension reinstalling `onnxruntime` when SD WebUI checks requirements. Please see your extensions list. Some extensions can causes reinstalling of `onnxruntime-gpu` to `onnxruntime<1.16.1` every time SD WebUI runs.<br>ORT 1.16.0 has a bug https://github.com/microsoft/onnxruntime/issues/17631 - don't install it! If it didn't help - it seems that you have another extension reinstalling `onnxruntime` when SD WebUI checks requirements. Please see your extensions list. Some extensions can causes reinstalling of `onnxruntime-gpu` to `onnxruntime<1.16.1` every time SD WebUI runs.<br>ORT 1.16.0 has a bug https://github.com/microsoft/onnxruntime/issues/17631 - don't install it!
@ -246,7 +300,7 @@ If it didn't help - it seems that you have another extension reinstalling `onnxr
5. Then: 5. Then:
- `python -m pip install -U pip` - `python -m pip install -U pip`
- `pip uninstall protobuf` - `pip uninstall protobuf`
- `pip install protobuf>=3.20.3` - `pip install "protobuf>=3.20.3"`
If this method doesn't help - there is some other extension that has a wrong version of protobuf dependence and SD WebUI installs it on a startup requirements check If this method doesn't help - there is some other extension that has a wrong version of protobuf dependence and SD WebUI installs it on a startup requirements check
@ -255,10 +309,10 @@ If this method doesn't help - there is some other extension that has a wrong ver
### **VIII. (For Windows users) If you still cannot build Insightface for some reasons or just don't want to install Visual Studio or VS C++ Build Tools - do the following:** ### **VIII. (For Windows users) If you still cannot build Insightface for some reasons or just don't want to install Visual Studio or VS C++ Build Tools - do the following:**
1. Close (stop) your SD WebUI Server if it's running 1. Close (stop) your SD WebUI Server if it's running
2. Download and put [prebuilt Insightface package](https://github.com/Gourieff/sd-webui-reactor/raw/main/example/insightface-0.7.3-cp310-cp310-win_amd64.whl) into the stable-diffusion-webui (or SD.Next) root folder (where you have "webui-user.bat" file) 2. Download and put [prebuilt Insightface package](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp310-cp310-win_amd64.whl) into the stable-diffusion-webui (or SD.Next) root folder where you have "webui-user.bat" file or (A1111 Portable) "run.bat"
3. From stable-diffusion-webui (or SD.Next) root folder run CMD and `.\venv\Scripts\activate` 3. From stable-diffusion-webui (or SD.Next) root folder run CMD and `.\venv\Scripts\activate`<br>OR<br>(A1111 Portable) Run CMD
4. Then update your PIP: `python -m pip install -U pip` 4. Then update your PIP: `python -m pip install -U pip`<br>OR<br>(A1111 Portable)`system\python\python.exe -m pip install -U pip`
5. Then install Insightface: `pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` 5. Then install Insightface: `pip install insightface-0.7.3-cp310-cp310-win_amd64.whl`<br>OR<br>(A1111 Portable)`system\python\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl`
6. Enjoy! 6. Enjoy!
### **IX. 07-August-23 Update problem** ### **IX. 07-August-23 Update problem**
@ -292,7 +346,7 @@ For the installation instruction follow the [ReActor Node repo](https://github.c
This software is meant to be a productive contribution to the rapidly growing AI-generated media industry. It will help artists with tasks such as animating a custom character or using the character as a model for clothing etc. This software is meant to be a productive contribution to the rapidly growing AI-generated media industry. It will help artists with tasks such as animating a custom character or using the character as a model for clothing etc.
The developers of this software are aware of its possible unethical applicaitons and are committed to take preventative measures against them. We will continue to develop this project in the positive direction while adhering to law and ethics. The developers of this software are aware of its possible unethical application and are committed to take preventative measures against them. We will continue to develop this project in the positive direction while adhering to law and ethics.
Users of this software are expected to use this software responsibly while abiding the local law. If 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 and Contributors of this software are not responsible for actions of end-users.** Users of this software are expected to use this software responsibly while abiding the local law. If 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 and Contributors of this software are not responsible for actions of end-users.**

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<div align="center"> <div align="center">
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/ReActor_logo_red.png?raw=true" alt="logo" width="180px"/> <img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/ReActor_logo_NEW_RU.png?raw=true" alt="logo" width="180px"/>
![Version](https://img.shields.io/badge/версия-0.5.0-brightgreen?style=for-the-badge&labelColor=darkgreen) ![Version](https://img.shields.io/badge/версия-0.7.0_beta6-green?style=for-the-badge&labelColor=darkgreen)
<a href='https://ko-fi.com/gourieff' target='_blank'><img height='33' src='https://storage.ko-fi.com/cdn/kofi3.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> <a href="https://boosty.to/artgourieff" target="_blank">
<img src="https://lovemet.ru/www/boosty.jpg" width="108" alt="Поддержать проект на Boosty"/>
<br>
<sup>
Поддержать проект
</sup>
</a>
<hr> <hr>
@ -21,47 +27,89 @@
--- ---
<b> <b>
<a href="#installation">Установка</a> | <a href="#features">Возможности</a> | <a href="#usage">Использование</a> | <a href="#api">API</a> | <a href="#troubleshooting">Устранение проблем</a> | <a href="#updating">Обновление</a> | <a href="#comfyui">ComfyUI</a> | <a href="#disclaimer">Ответственность</a> <a href="#latestupdate">Что нового</a> | <a href="#installation">Установка</a> | <a href="#features">Возможности</a> | <a href="#usage">Использование</a> | <a href="#api">API</a> | <a href="#troubleshooting">Устранение проблем</a> | <a href="#updating">Обновление</a> | <a href="#comfyui">ComfyUI</a> | <a href="#disclaimer">Ответственность</a>
</b> </b>
</div> </div>
--- ---
<table>
<tr>
<td width="134px">
<a href="https://boosty.to/artgourieff" target="_blank">
<img src="https://lovemet.ru/www/boosty.jpg" width="108" alt="Поддержать проект на Boosty"/>
<br>
<sup>
Поддержать проект
</sup>
</a>
</td>
<td>
ReActor это расширение для Stable Diffusion WebUI, которое позволяет делать простую и точную замену лиц на изображениях. Сделано на основе <a href="https://github.com/Gourieff/ReActor-UI" target="_blank">ReActor-UI</a>.
</td>
<td width="144px">
<a href="https://paypal.me/artgourieff" target="_blank">
<img src="https://www.paypalobjects.com/digitalassets/c/website/logo/full-text/pp_fc_hl.svg" width="108" alt="Поддержать проект через PayPal"/>
<br>
<sup>
Помочь проекту
</sup>
</a>
</td>
</tr>
</table>
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/demo_crop.jpg?raw=true" alt="example"/> <img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/demo_crop.jpg?raw=true" alt="example"/>
<a name="latestupdate">
## Что нового в последних обновлениях
<details>
<summary><a>Нажмите, чтобы посмотреть</a></summary>
### 0.7.0 <sub><sup>BETA2
- X/Y/Z опция улучшена! Добавлен ещё один параметр: теперь вы можете выбрать несколько моделей лиц для создания вариации замененных лиц, чтобы выбрать наилучшие!
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-05.jpg?raw=true" alt="0.7.0-whatsnew-05" width="100%"/>
Чтобы использовать ось "Face Model" - активируйте РеАктор и выбирите любую модель лица в качестве источника:<br>
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-07.jpg?raw=true" alt="0.7.0-whatsnew-07" width="50%"/><img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-06.jpg?raw=true" alt="0.7.0-whatsnew-06" width="50%"/>
Полноразмерное демо-изображение: [xyz_demo_2.png](https://raw.githubusercontent.com/Gourieff/Assets/main/sd-webui-reactor/xyz_demo_2.png)
### 0.7.0 <sub><sup>BETA1
- Поддержка X/Y/Z скрипта (до 3-х параметров: CodeFormer Weight, Restorer Visibility, Face Mask Correction)
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-03.jpg?raw=true" alt="0.7.0-whatsnew-03" width="100%"/>
Полноразмерное демо-изображение: [xyz_demo.png](https://raw.githubusercontent.com/Gourieff/Assets/main/sd-webui-reactor/xyz_demo.png)
### 0.7.0 <sub><sup>ALPHA1
- По многочисленным просьбам появилась возможность строить смешанные модели лиц ("Tools->Face Models->Blend")
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-01.jpg?raw=true" alt="0.7.0-whatsnew-01" width="100%"/><img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/0.7.0-whatsnew-02.jpg?raw=true" alt="0.7.0-whatsnew-02" width="100%"/>
- Поддержка CUDA 12 в скрипте установщика для библиотеки ORT-GPU версии 1.17.0
- Новая вкладка "Detection" с параметрами "Threshold" и "Max Faces"
### 0.6.1 <sub><sup>BETA3
- Опция 'Force Upscale' внутри вкладки 'Upscale': апскейл выполнится, даже если не было обнаружено ни одного лица (FR https://github.com/Gourieff/sd-webui-reactor/issues/116)
- Отображение имён файлов используемых изображений, когда выбрано несколько изображений или папка (а также режим случайного изображения)
### 0.6.1 <sub><sup>BETA2
- Опция 'Save original' теперь работает правильно, когда вы выбираете 'Multiple Images' или 'Source Folder'
- Добавлен режим выбора случайного изображения для 'Source Folder'
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/random_from_folder_demo_01.jpg?raw=true" alt="0.6.1-whatsnew-01" width="100%"/>
### 0.6.0
- Новый логотип
- Адаптация к версии A1111 1.7.0 (правильная загрузка GFPGAN)
- Новая ссылка для файла основной модели
- UI переработан
- Появилась возможность загружать несколько исходных изображений с лицами или задавать путь к папке, содержащей такие изображения
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/multiple_source_images_demo_01.png?raw=true" alt="0.6.0-whatsnew-01" width="100%"/>
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/multiple_source_images_demo_02.png?raw=true" alt="0.6.0-whatsnew-02" width="100%"/>
### 0.5.1
- Теперь можно сохранять модели лиц в качестве файлов "safetensors" (находятся в `<sd-web-ui-folder>\models\reactor\faces`) и загружать их с ReActor, храня супер легкие модели лиц, которые вы чаще всего используете;
- Новые опция "Face Mask Correction" - если вы сталкиваетесь с пикселизацией вокруг контуров лица, эта опция будет полезной;
<img src="https://github.com/Gourieff/Assets/blob/main/sd-webui-reactor/face_model_demo_01.jpg?raw=true" alt="0.5.0-whatsnew-01" width="100%"/>
</details>
<a name="installation"> <a name="installation">
## Установка ## Установка
[Automatic1111](#a1111) | [Vladmandic SD.Next](#sdnext) | [Google Colab SD WebUI](#colab) [A1111 WebUI / WebUI-Forge](#a1111) | [SD.Next](#sdnext) | [Google Colab SD WebUI](#colab)
<a name="a1111">Если вы используете [AUTOMATIC1111 Web-UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui/): <a name="a1111">Если вы используете [AUTOMATIC1111 SD WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui/) или [SD WebUI Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge):
1. (Для пользователей Windows): 1. (Для пользователей Windows):
- Установите **Visual Studio 2022** (Например, версию Community - этот шаг нужен для правильной компиляции библиотеки Insightface): - Установите **Visual Studio 2022** (Например, версию Community - этот шаг нужен для правильной компиляции библиотеки Insightface):
@ -69,8 +117,8 @@
- ИЛИ только **VS C++ Build Tools** (если вам не нужен весь пакет Visual Studio), выберите "Desktop Development with C++" в разделе "Workloads -> Desktop & Mobile": - ИЛИ только **VS C++ Build Tools** (если вам не нужен весь пакет Visual Studio), выберите "Desktop Development with C++" в разделе "Workloads -> Desktop & Mobile":
https://visualstudio.microsoft.com/visual-cpp-build-tools/ https://visualstudio.microsoft.com/visual-cpp-build-tools/
- ИЛИ если же вы не хотите устанавливать что-либо из вышеуказанного - выполните [следующие шаги (пункт VIII)](#insightfacebuild) - ИЛИ если же вы не хотите устанавливать что-либо из вышеуказанного - выполните [следующие шаги (пункт VIII)](#insightfacebuild)
2. Внутри SD Web-UI перейдите во вкладку "Extensions" и вставьте ссылку `https://github.com/Gourieff/sd-webui-reactor` в "Install from URL" и нажмите "Install" 2. Внутри SD Web-UI перейдите во вкладку "Extensions", загрузите список доступных расширений (вкладка "Available") и введите "ReActor" в строке поиска или же вставьте ссылку `https://github.com/Gourieff/sd-webui-reactor` в "Install from URL" - и нажмите "Install"
3. Пожалуйста, подождите несколько минут, пока процесс установки полностью не завершится 3. Пожалуйста, подождите несколько минут, пока процесс установки полностью не завершится (наберитесь терпения, не прерывайте процесс)
4. Проверьте последнее сообщение в консоли SD-WebUI: 4. Проверьте последнее сообщение в консоли SD-WebUI:
* Если вы видите "--- PLEASE, RESTART the Server! ---" - остановите Сервер (CTRL+C или CMD+C) и запустите его заново - ИЛИ же перейдите во вкладку "Installed", нажмите "Apply and restart UI" * Если вы видите "--- PLEASE, RESTART the Server! ---" - остановите Сервер (CTRL+C или CMD+C) и запустите его заново - ИЛИ же перейдите во вкладку "Installed", нажмите "Apply and restart UI"
* Если вы видите "Done!", просто перезагрузите UI, нажав на "Reload UI" * Если вы видите "Done!", просто перезагрузите UI, нажав на "Reload UI"
@ -83,7 +131,7 @@
3. Перейдите в (Windows)`automatic\venv\Scripts` или (MacOS/Linux)`automatic/venv/bin`, запустите Терминал или Консоль (cmd) для данной папки и выполните `activate` 3. Перейдите в (Windows)`automatic\venv\Scripts` или (MacOS/Linux)`automatic/venv/bin`, запустите Терминал или Консоль (cmd) для данной папки и выполните `activate`
4. Выполните `pip install insightface==0.7.3` 4. Выполните `pip install insightface==0.7.3`
5. Запустите SD.Next, перейдите во вкладку "Extensions", вставьте эту ссылку `https://github.com/Gourieff/sd-webui-reactor` в "Install from URL" и нажмите "Install" 5. Запустите SD.Next, перейдите во вкладку "Extensions", вставьте эту ссылку `https://github.com/Gourieff/sd-webui-reactor` в "Install from URL" и нажмите "Install"
6. Пожалуйста, подождите несколько минут, пока процесс установки полностью не завершится 6. Пожалуйста, подождите несколько минут, пока процесс установки полностью не завершится (наберитесь терпения, не прерывайте процесс)
7. Проверьте последнее сообщение в консоли SD.Next: 7. Проверьте последнее сообщение в консоли SD.Next:
* Если вы видите "--- PLEASE, RESTART the Server! ---" - остановите Сервер (CTRL+C или CMD+C) или просто закройте консоль * Если вы видите "--- PLEASE, RESTART the Server! ---" - остановите Сервер (CTRL+C или CMD+C) или просто закройте консоль
8. Перейдите в директорию `automatic\extensions\sd-webui-reactor` - если вы видите там папку `models\insightface` с файлом `inswapper_128.onnx` внутри, переместите его в папку `automatic\models\insightface` 8. Перейдите в директорию `automatic\extensions\sd-webui-reactor` - если вы видите там папку `models\insightface` с файлом `inswapper_128.onnx` внутри, переместите его в папку `automatic\models\insightface`
@ -91,8 +139,8 @@
<a name="colab">Если вы используете [Cagliostro Colab UI](https://github.com/Linaqruf/sd-notebook-collection): <a name="colab">Если вы используете [Cagliostro Colab UI](https://github.com/Linaqruf/sd-notebook-collection):
1. В активном WebUI, перейдите во вкладку "Extensions", вставьте ссылку `https://github.com/Gourieff/sd-webui-reactor` в "Install from URL" и нажмите "Install" 1. В активном WebUI перейдите во вкладку "Extensions", загрузите список доступных расширений (вкладка "Available") и введите "ReActor" в строке поиска или же вставьте ссылку `https://github.com/Gourieff/sd-webui-reactor` в "Install from URL" - и нажмите "Install"
2. Пожалуйста, подождите некоторое время, пока процесс установки полностью не завершится 2. Пожалуйста, подождите некоторое время, пока процесс установки полностью не завершится (наберитесь терпения, не прерывайте процесс)
3. Когда вы увидите сообщение "--- PLEASE, RESTART the Server! ---" (в секции "Start UI" вашего ноутбука "Start Cagliostro Colab UI") - перейдите во вкладку "Installed" и нажмите "Apply and restart UI" 3. Когда вы увидите сообщение "--- PLEASE, RESTART the Server! ---" (в секции "Start UI" вашего ноутбука "Start Cagliostro Colab UI") - перейдите во вкладку "Installed" и нажмите "Apply and restart UI"
4. Готово! 4. Готово!
@ -106,6 +154,8 @@
- Функция **сохранения оригинального изображения** (сгенерированного до замены лица) - Функция **сохранения оригинального изображения** (сгенерированного до замены лица)
- **Восстановление лица** после замены - **Восстановление лица** после замены
- **Увеличение размера** полученного изображения - **Увеличение размера** полученного изображения
- Сохранение и загрузка **Моделей Лиц типа Safetensors**
- **Коррекция Маски Лица** для предотвращения какой-либо пикселизации вокруг контуров лиц
- Возможность задать **порядок постобработки** - Возможность задать **порядок постобработки**
- **100% совместимость** с разными **SD WebUI**: Automatic1111, SD.Next, Cagliostro Colab UI - **100% совместимость** с разными **SD WebUI**: Automatic1111, SD.Next, Cagliostro Colab UI
- **Отличная производительность** даже с использованием ЦПУ, ReActor для SD WebUI абсолютно не требователен к мощности вашей видеокарты - **Отличная производительность** даже с использованием ЦПУ, ReActor для SD WebUI абсолютно не требователен к мощности вашей видеокарты
@ -205,7 +255,7 @@ Inpainting также работает, но замена лица происх
7. Далее: 7. Далее:
- `pip install insightface==0.7.3` - `pip install insightface==0.7.3`
- `pip install onnx` - `pip install onnx`
- `pip install onnxruntime-gpu>=1.16.1` - `pip install "onnxruntime-gpu>=1.16.1"`
- `pip install opencv-python` - `pip install opencv-python`
- `pip install tqdm` - `pip install tqdm`
8. Выполните `deactivate`, закройте Терминал или Консоль и запустите SD WebUI, ReActor должен запуститься без к-л проблем - если же нет, добро пожаловать в раздел "Issues". 8. Выполните `deactivate`, закройте Терминал или Консоль и запустите SD WebUI, ReActor должен запуститься без к-л проблем - если же нет, добро пожаловать в раздел "Issues".
@ -229,7 +279,7 @@ Inpainting также работает, но замена лица происх
Эта ошибка появляется, если что-то не так с файлом модели `inswapper_128.onnx`. Эта ошибка появляется, если что-то не так с файлом модели `inswapper_128.onnx`.
Скачайте вручную по ссылке [here](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) Скачайте вручную по ссылке [here](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx)
и сохраните в директорию `stable-diffusion-webui\models\insightface`, заменив имеющийся файл. и сохраните в директорию `stable-diffusion-webui\models\insightface`, заменив имеющийся файл.
### **VI. "ValueError: This ORT build has ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'] enabled" ИЛИ "ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled"** ### **VI. "ValueError: This ORT build has ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'] enabled" ИЛИ "ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled"**
@ -240,7 +290,7 @@ Inpainting также работает, но замена лица происх
4. Затем: 4. Затем:
- `python -m pip install -U pip` - `python -m pip install -U pip`
- `pip uninstall -y onnxruntime onnxruntime-gpu onnxruntime-silicon onnxruntime-extensions` - `pip uninstall -y onnxruntime onnxruntime-gpu onnxruntime-silicon onnxruntime-extensions`
- `pip install onnxruntime-gpu>=1.16.1` - `pip install "onnxruntime-gpu>=1.16.1"`
Если это не помогло - значит какое-то другое расширение переустанавливает `onnxruntime` всякий раз, когда SD WebUI проверяет требования пакетов. Внимательно посмотрите список активных расширений. Некоторые расширения могут вызывать переустановку `onnxruntime-gpu` на версию `onnxruntime<1.16.1` при каждом запуске SD WebUI.<br>ORT 1.16.0 выкатили с ошибкой https://github.com/microsoft/onnxruntime/issues/17631 - не устанавливайте её! Если это не помогло - значит какое-то другое расширение переустанавливает `onnxruntime` всякий раз, когда SD WebUI проверяет требования пакетов. Внимательно посмотрите список активных расширений. Некоторые расширения могут вызывать переустановку `onnxruntime-gpu` на версию `onnxruntime<1.16.1` при каждом запуске SD WebUI.<br>ORT 1.16.0 выкатили с ошибкой https://github.com/microsoft/onnxruntime/issues/17631 - не устанавливайте её!
@ -253,7 +303,7 @@ Inpainting также работает, но замена лица происх
5. Затем: 5. Затем:
- `python -m pip install -U pip` - `python -m pip install -U pip`
- `pip uninstall protobuf` - `pip uninstall protobuf`
- `pip install protobuf>=3.20.3` - `pip install "protobuf>=3.20.3"`
Если это не помгло - значит, есть к-л другое расширение, которое использует неподходящую версию пакета protobuf, и SD WebUI устанавливает эту версию при каждом запуске. Если это не помгло - значит, есть к-л другое расширение, которое использует неподходящую версию пакета protobuf, и SD WebUI устанавливает эту версию при каждом запуске.
@ -262,10 +312,10 @@ Inpainting также работает, но замена лица происх
### **VIII. (Для пользователей Windows) Если вы до сих пор не можете установить пакет Insightface по каким-то причинам или же просто не желаете устанавливать Visual Studio или VS C++ Build Tools - сделайте следующее:** ### **VIII. (Для пользователей Windows) Если вы до сих пор не можете установить пакет Insightface по каким-то причинам или же просто не желаете устанавливать Visual Studio или VS C++ Build Tools - сделайте следующее:**
1. Закройте (остановите) SD WebUI Сервер, если он запущен 1. Закройте (остановите) SD WebUI Сервер, если он запущен
2. Скачайте готовый [пакет Insightface](https://github.com/Gourieff/sd-webui-reactor/raw/main/example/insightface-0.7.3-cp310-cp310-win_amd64.whl) и сохраните его в корневую директорию stable-diffusion-webui (или SD.Next) - туда, где лежит файл "webui-user.bat" 2. Скачайте готовый [пакет Insightface](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp310-cp310-win_amd64.whl) и сохраните его в корневую директорию stable-diffusion-webui (или SD.Next) - туда, где лежит файл "webui-user.bat" или (A1111 Portable) "run.bat"
3. Из корневой директории откройте Консоль (CMD) и выполните `.\venv\Scripts\activate` 3. Из корневой директории откройте Консоль (CMD) и выполните `.\venv\Scripts\activate`<br>ИЛИ<br>(A1111 Portable) Откройте Консоль (CMD)
4. Обновите PIP: `python -m pip install -U pip` 4. Обновите PIP: `python -m pip install -U pip`<br>ИЛИ<br>(A1111 Portable)`system\python\python.exe -m pip install -U pip`
5. Затем установите Insightface: `pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` 5. Затем установите Insightface: `pip install insightface-0.7.3-cp310-cp310-win_amd64.whl`<br>ИЛИ<br>(A1111 Portable)`system\python\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl`
6. Готово! 6. Готово!
### **IX. Ошибка обновления 07-Август-23** ### **IX. Ошибка обновления 07-Август-23**

11
example/api_example.py
View File

@ -31,7 +31,7 @@ args=[
1, #5 Restore visibility value 1, #5 Restore visibility value
True, #7 Restore face -> Upscale True, #7 Restore face -> Upscale
'4x_NMKD-Superscale-SP_178000_G', #8 Upscaler (type 'None' if doesn't need), see full list here: http://127.0.0.1:7860/sdapi/v1/script-info -> reactor -> sec.8 '4x_NMKD-Superscale-SP_178000_G', #8 Upscaler (type 'None' if doesn't need), see full list here: http://127.0.0.1:7860/sdapi/v1/script-info -> reactor -> sec.8
2, #9 Upscaler scale value 1.5, #9 Upscaler scale value
1, #10 Upscaler visibility (if scale = 1) 1, #10 Upscaler visibility (if scale = 1)
False, #11 Swap in source image False, #11 Swap in source image
True, #12 Swap in generated image True, #12 Swap in generated image
@ -43,6 +43,15 @@ args=[
False, #18 Source Image Hash Check, True - by default False, #18 Source Image Hash Check, True - by default
False, #19 Target Image Hash Check, False - by default False, #19 Target Image Hash Check, False - by default
"CUDA", #20 CPU or CUDA (if you have it), CPU - by default "CUDA", #20 CPU or CUDA (if you have it), CPU - by default
True, #21 Face Mask Correction
1, #22 Select Source, 0 - Image, 1 - Face Model, 2 - Source Folder
"elena.safetensors", #23 Filename of the face model (from "models/reactor/faces"), e.g. elena.safetensors, don't forger to set #22 to 1
"C:\PATH_TO_FACES_IMAGES", #24 The path to the folder containing source faces images, don't forger to set #22 to 2
None, #25 skip it for API
True, #26 Randomly select an image from the path
True, #27 Force Upscale even if no face found
0.6, #28 Face Detection Threshold
2, #29 Maximum number of faces to detect (0 is unlimited)
] ]
# The args for ReActor can be found by # The args for ReActor can be found by

10
example/api_external.curl
View File

@ -8,7 +8,7 @@ curl -X POST \
"source_faces_index": [0], "source_faces_index": [0],
"face_index": [2], "face_index": [2],
"upscaler": "None", "upscaler": "None",
"scale": 1, "scale": 1.5,
"upscale_visibility": 1, "upscale_visibility": 1,
"face_restorer": "CodeFormer", "face_restorer": "CodeFormer",
"restorer_visibility": 1, "restorer_visibility": 1,
@ -19,5 +19,11 @@ curl -X POST \
"gender_target": 0, "gender_target": 0,
"save_to_file": 1, "save_to_file": 1,
"result_file_path": "", "result_file_path": "",
"device": "CUDA" "device": "CUDA",
"mask_face": 1,
"select_source": 1,
"face_model": "elena.safetensors",
"source_folder": "C:/faces",
"random_image": 1,
"upscale_force": 1
}' }'

10
example/api_external.json
View File

@ -4,7 +4,7 @@
"source_faces_index": [0], "source_faces_index": [0],
"face_index": [2], "face_index": [2],
"upscaler": "None", "upscaler": "None",
"scale": 1, "scale": 1.5,
"upscale_visibility": 1, "upscale_visibility": 1,
"face_restorer": "CodeFormer", "face_restorer": "CodeFormer",
"restorer_visibility": 1, "restorer_visibility": 1,
@ -15,5 +15,11 @@
"gender_target": 0, "gender_target": 0,
"save_to_file": 1, "save_to_file": 1,
"result_file_path": "", "result_file_path": "",
"device": "CUDA" "device": "CUDA",
"mask_face": 1,
"select_source": 1,
"face_model": "elena.safetensors",
"source_folder": "C:/faces",
"random_image": 1,
"upscale_force": 1
} }

BIN
example/insightface-0.7.3-cp310-cp310-win_amd64.whl
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58
install.py
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@ -12,30 +12,30 @@ except:
try: try:
from modules.paths import models_path from modules.paths import models_path
except: except:
model_path = os.path.abspath("models") models_path = os.path.abspath("models")
BASE_PATH = os.path.dirname(os.path.realpath(__file__)) BASE_PATH = os.path.dirname(os.path.realpath(__file__))
req_file = os.path.join(BASE_PATH, "requirements.txt") req_file = os.path.join(BASE_PATH, "requirements.txt")
models_dir_old = os.path.join(models_path, "roop")
models_dir = os.path.join(models_path, "insightface") models_dir = os.path.join(models_path, "insightface")
# Check the deprecated 'roop' folder # DEPRECATED:
if os.path.exists(models_dir_old): # models_dir_old = os.path.join(models_path, "roop")
if not os.listdir(models_dir_old) and (not os.listdir(models_dir) or not os.path.exists(models_dir)): # if os.path.exists(models_dir_old):
os.rename(models_dir_old, models_dir) # if not os.listdir(models_dir_old) and (not os.listdir(models_dir) or not os.path.exists(models_dir)):
else: # os.rename(models_dir_old, models_dir)
import shutil # else:
for file in os.listdir(models_dir_old): # import shutil
shutil.move(os.path.join(models_dir_old, file), os.path.join(models_dir, file)) # for file in os.listdir(models_dir_old):
try: # shutil.move(os.path.join(models_dir_old, file), os.path.join(models_dir, file))
os.rmdir(models_dir_old) # try:
except Exception as e: # os.rmdir(models_dir_old)
print(f"OSError: {e}") # except Exception as e:
# print(f"OSError: {e}")
model_url = "https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx" model_url = "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx"
model_name = os.path.basename(model_url) model_name = os.path.basename(model_url)
model_path = os.path.join(models_dir, model_name) model_path = os.path.join(models_dir, model_name)
@ -75,16 +75,18 @@ if not os.path.exists(models_dir):
if not os.path.exists(model_path): if not os.path.exists(model_path):
download(model_url, model_path) download(model_url, model_path)
print("ReActor preheating...", end=' ') # print("ReActor preheating...", end=' ')
last_device = None last_device = None
first_run = False first_run = False
available_devices = ["CPU", "CUDA"]
try: try:
last_device_log = os.path.join(BASE_PATH, "last_device.txt") last_device_log = os.path.join(BASE_PATH, "last_device.txt")
with open(last_device_log) as f: with open(last_device_log) as f:
for el in f: last_device = f.readline().strip()
last_device = el.strip() if last_device not in available_devices:
last_device = None
except: except:
last_device = "CPU" last_device = "CPU"
first_run = True first_run = True
@ -95,31 +97,39 @@ with open(req_file) as file:
install_count = 0 install_count = 0
ort = "onnxruntime-gpu" ort = "onnxruntime-gpu"
import torch import torch
cuda_version = None
try: try:
if torch.cuda.is_available(): if torch.cuda.is_available():
if first_run: cuda_version = torch.version.cuda
print(f"CUDA {cuda_version}")
if first_run or last_device is None:
last_device = "CUDA" last_device = "CUDA"
elif torch.backends.mps.is_available() or hasattr(torch,'dml'): elif torch.backends.mps.is_available() or hasattr(torch,'dml') or hasattr(torch,'privateuseone'):
ort = "onnxruntime" ort = "onnxruntime"
# to prevent errors when ORT-GPU is installed but we want ORT instead: # to prevent errors when ORT-GPU is installed but we want ORT instead:
if first_run: if first_run:
pip_uninstall("onnxruntime", "onnxruntime-gpu") pip_uninstall("onnxruntime", "onnxruntime-gpu")
# just in case: # just in case:
if last_device == "CUDA": if last_device == "CUDA" or last_device is None:
last_device = "CPU" last_device = "CPU"
else: else:
if last_device == "CUDA": if last_device == "CUDA" or last_device is None:
last_device = "CPU" last_device = "CPU"
with open(os.path.join(BASE_PATH, "last_device.txt"), "w") as txt: with open(os.path.join(BASE_PATH, "last_device.txt"), "w") as txt:
txt.write(last_device) txt.write(last_device)
if not is_installed(ort,"1.16.1",False): if cuda_version is not None and float(cuda_version)>=12: # CU12
if not is_installed(ort,"1.17.1",False):
install_count += 1
pip_uninstall("onnxruntime", "onnxruntime-gpu")
pip_install(ort,"--extra-index-url","https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/")
elif not is_installed(ort,"1.16.1",False):
install_count += 1 install_count += 1
pip_install(ort, "-U") pip_install(ort, "-U")
except Exception as e: except Exception as e:
print(e) print(e)
print(f"\nERROR: Failed to install {ort} - ReActor won't start") print(f"\nERROR: Failed to install {ort} - ReActor won't start")
raise e raise e
print(f"Device: {last_device}") # print(f"Device: {last_device}")
strict = True strict = True
for package in file: for package in file:
package_version = None package_version = None

176
reactor_modules/reactor_mask.py Normal file
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@ -0,0 +1,176 @@
import cv2
import numpy as np
from PIL import Image, ImageDraw
from torchvision.transforms.functional import to_pil_image
from scripts.reactor_logger import logger
from scripts.reactor_inferencers.bisenet_mask_generator import BiSeNetMaskGenerator
from scripts.reactor_entities.face import FaceArea
from scripts.reactor_entities.rect import Rect
colors = [
(255, 0, 0),
(0, 255, 0),
(0, 0, 255),
(255, 255, 0),
(255, 0, 255),
(0, 255, 255),
(255, 255, 255),
(128, 0, 0),
(0, 128, 0),
(128, 128, 0),
(0, 0, 128),
(0, 128, 128),
]
def color_generator(colors):
while True:
for color in colors:
yield color
def process_face_image(
face: FaceArea,
**kwargs,
) -> Image:
image = np.array(face.image)
overlay = image.copy()
color_iter = color_generator(colors)
cv2.rectangle(overlay, (0, 0), (image.shape[1], image.shape[0]), next(color_iter), -1)
l, t, r, b = face.face_area_on_image
cv2.rectangle(overlay, (l, t), (r, b), (0, 0, 0), 10)
if face.landmarks_on_image is not None:
for landmark in face.landmarks_on_image:
cv2.circle(overlay, (int(landmark.x), int(landmark.y)), 6, (0, 0, 0), 10)
alpha = 0.3
output = cv2.addWeighted(image, 1 - alpha, overlay, alpha, 0)
return Image.fromarray(output)
def apply_face_mask(swapped_image:np.ndarray,target_image:np.ndarray,target_face,entire_mask_image:np.array)->np.ndarray:
logger.status("Correcting Face Mask")
mask_generator = BiSeNetMaskGenerator()
face = FaceArea(target_image,Rect.from_ndarray(np.array(target_face.bbox)),1.6,512,"")
face_image = np.array(face.image)
process_face_image(face)
face_area_on_image = face.face_area_on_image
mask = mask_generator.generate_mask(
face_image,
face_area_on_image=face_area_on_image,
affected_areas=["Face"],
mask_size=0,
use_minimal_area=True
)
mask = cv2.blur(mask, (12, 12))
# """entire_mask_image = np.zeros_like(target_image)"""
larger_mask = cv2.resize(mask, dsize=(face.width, face.height))
entire_mask_image[
face.top : face.bottom,
face.left : face.right,
] = larger_mask
result = Image.composite(Image.fromarray(swapped_image),Image.fromarray(target_image), Image.fromarray(entire_mask_image).convert("L"))
return np.array(result)
def rotate_array(image: np.ndarray, angle: float) -> np.ndarray:
if angle == 0:
return image
h, w = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
return cv2.warpAffine(image, M, (w, h))
def rotate_image(image: Image, angle: float) -> Image:
if angle == 0:
return image
return Image.fromarray(rotate_array(np.array(image), angle))
def correct_face_tilt(angle: float) -> bool:
angle = abs(angle)
if angle > 180:
angle = 360 - angle
return angle > 40
def _dilate(arr: np.ndarray, value: int) -> np.ndarray:
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
return cv2.dilate(arr, kernel, iterations=1)
def _erode(arr: np.ndarray, value: int) -> np.ndarray:
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
return cv2.erode(arr, kernel, iterations=1)
def dilate_erode(img: Image.Image, value: int) -> Image.Image:
"""
The dilate_erode function takes an image and a value.
If the value is positive, it dilates the image by that amount.
If the value is negative, it erodes the image by that amount.
Parameters
----------
img: PIL.Image.Image
the image to be processed
value: int
kernel size of dilation or erosion
Returns
-------
PIL.Image.Image
The image that has been dilated or eroded
"""
if value == 0:
return img
arr = np.array(img)
arr = _dilate(arr, value) if value > 0 else _erode(arr, -value)
return Image.fromarray(arr)
def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]:
"""
Parameters
----------
masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
The device can be CUDA, but `to_pil_image` takes care of that.
shape: tuple[int, int]
(width, height) of the original image
"""
n = masks.shape[0]
return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]
def create_mask_from_bbox(
bboxes: list[list[float]], shape: tuple[int, int]
) -> list[Image.Image]:
"""
Parameters
----------
bboxes: list[list[float]]
list of [x1, y1, x2, y2]
bounding boxes
shape: tuple[int, int]
shape of the image (width, height)
Returns
-------
masks: list[Image.Image]
A list of masks
"""
masks = []
for bbox in bboxes:
mask = Image.new("L", shape, 0)
mask_draw = ImageDraw.Draw(mask)
mask_draw.rectangle(bbox, fill=255)
masks.append(mask)
return masks

5
reactor_ui/__init__.py Normal file
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@ -0,0 +1,5 @@
import reactor_ui.reactor_upscale_ui as ui_upscale
import reactor_ui.reactor_tools_ui as ui_tools
import reactor_ui.reactor_settings_ui as ui_settings
import reactor_ui.reactor_main_ui as ui_main
import reactor_ui.reactor_detection_ui as ui_detection

54
reactor_ui/reactor_detection_ui.py Normal file
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@ -0,0 +1,54 @@
import gradio as gr
from scripts.reactor_swapper import (
clear_faces,
clear_faces_list,
clear_faces_target,
clear_faces_all
)
# TAB DETECTION
def show(show_br: bool = True):
with gr.Tab("Detection"):
with gr.Row():
det_thresh = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.5,
step=0.01,
label="Threshold",
info="The higher the value, the more sensitive the detection is to what is considered a face (0.5 by default)",
scale=2
)
det_maxnum = gr.Slider(
minimum=0,
maximum=20,
value=0,
step=1,
label="Max Faces",
info="Maximum number of faces to detect (0 is unlimited)",
scale=1
)
# gr.Markdown("<br>", visible=show_br)
gr.Markdown("Hashed images get processed with previously set detection parameters (the face is hashed with all available parameters to bypass the analyzer and speed up the process). Please clear the hash if you want to apply new detection settings.", visible=show_br)
with gr.Row():
imgs_hash_clear_single = gr.Button(
value="Clear Source Images Hash (Single)",
scale=1
)
imgs_hash_clear_multiple = gr.Button(
value="Clear Source Images Hash (Multiple)",
scale=1
)
imgs_hash_clear_target = gr.Button(
value="Clear Target Image Hash",
scale=1
)
imgs_hash_clear_all = gr.Button(
value="Clear All Hash"
)
progressbar_area = gr.Markdown("")
imgs_hash_clear_single.click(clear_faces,None,[progressbar_area])
imgs_hash_clear_multiple.click(clear_faces_list,None,[progressbar_area])
imgs_hash_clear_target.click(clear_faces_target,None,[progressbar_area])
imgs_hash_clear_all.click(clear_faces_all,None,[progressbar_area])
return det_thresh, det_maxnum

192
reactor_ui/reactor_main_ui.py Normal file
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@ -0,0 +1,192 @@
import gradio as gr
from scripts.reactor_helpers import (
get_model_names,
get_facemodels
)
from scripts.reactor_swapper import (
clear_faces_list,
)
from modules import shared
# SAVE_ORIGINAL: bool = False
def update_fm_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=get_model_names(get_facemodels)
)
# TAB MAIN
def show(is_img2img: bool, show_br: bool = True, **msgs):
# def on_select_source(selected: bool, evt: gr.SelectData):
def on_select_source(evt: gr.SelectData):
# global SAVE_ORIGINAL
if evt.index == 2:
# if SAVE_ORIGINAL != selected:
# SAVE_ORIGINAL = selected
return {
control_col_1: gr.Column.update(visible=False),
control_col_2: gr.Column.update(visible=False),
control_col_3: gr.Column.update(visible=True),
# save_original: gr.Checkbox.update(value=False,visible=False),
imgs_hash_clear: gr.Button.update(visible=True)
}
if evt.index == 0:
return {
control_col_1: gr.Column.update(visible=True),
control_col_2: gr.Column.update(visible=False),
control_col_3: gr.Column.update(visible=False),
# save_original: gr.Checkbox.update(value=SAVE_ORIGINAL,visible=show_br),
imgs_hash_clear: gr.Button.update(visible=False)
}
if evt.index == 1:
return {
control_col_1: gr.Column.update(visible=False),
control_col_2: gr.Column.update(visible=True),
control_col_3: gr.Column.update(visible=False),
# save_original: gr.Checkbox.update(value=SAVE_ORIGINAL,visible=show_br),
imgs_hash_clear: gr.Button.update(visible=False)
}
progressbar_area = gr.Markdown("")
with gr.Tab("Main"):
with gr.Column():
with gr.Row():
select_source = gr.Radio(
["Image(s)","Face Model","Folder"],
value="Image(s)",
label="Select Source",
type="index",
scale=1,
)
with gr.Column(visible=False) as control_col_2:
with gr.Row():
face_models = get_model_names(get_facemodels)
face_model = gr.Dropdown(
choices=face_models,
label="Choose Face Model",
value="None",
scale=1,
)
fm_update = gr.Button(
value="🔄",
variant="tool",
)
fm_update.click(
update_fm_list,
inputs=[face_model],
outputs=[face_model],
)
imgs_hash_clear = gr.Button(
value="Clear Source Images Hash",
scale=1,
visible=False,
)
imgs_hash_clear.click(clear_faces_list,None,[progressbar_area])
gr.Markdown("<br>", visible=show_br)
with gr.Column(visible=True) as control_col_1:
gr.Markdown("<center>🔽🔽🔽 Single Image has priority when both Areas in use 🔽🔽🔽</center>")
with gr.Row():
img = gr.Image(
type="pil",
label="Single Source Image",
)
imgs = gr.Files(
label=f"Multiple Source Images{msgs['extra_multiple_source']}",
file_types=["image"],
)
with gr.Column(visible=False) as control_col_3:
gr.Markdown("<span style='display:block;text-align:right;padding-right:3px;margin: -15px 0;font-size:1.1em'><sup>Clear Hash if you see the previous face was swapped instead of the new one</sup></span>")
with gr.Row():
source_folder = gr.Textbox(
value="",
placeholder="Paste here the path to the folder containing source faces images",
label=f"Source Folder{msgs['extra_multiple_source']}",
scale=2,
)
random_image = gr.Checkbox(
False,
label="Random Image",
info="Randomly select an image from the path",
scale=1,
)
setattr(face_model, "do_not_save_to_config", True)
if is_img2img:
save_original = gr.Checkbox(
False,
label="Save Original (Swap in generated only)",
info="Save the original image(s) made before swapping"
)
else:
save_original = gr.Checkbox(
False,
label="Save Original",
info="Save the original image(s) made before swapping",
visible=show_br
)
# imgs.upload(on_files_upload_uncheck_so,[save_original],[save_original],show_progress=False)
# imgs.clear(on_files_clear,None,[save_original],show_progress=False)
imgs.clear(clear_faces_list,None,None,show_progress=False)
mask_face = gr.Checkbox(
False,
label="Face Mask Correction",
info="Apply this option if you see some pixelation around face contours"
)
gr.Markdown("<br>", visible=show_br)
gr.Markdown("Source Image (above):")
with gr.Row():
source_faces_index = gr.Textbox(
value="0",
placeholder="Which face(s) to use as Source (comma separated)",
label="Comma separated face number(s); Example: 0,2,1",
)
gender_source = gr.Radio(
["No", "Female Only", "Male Only"],
value="No",
label="Gender Detection (Source)",
type="index",
)
gr.Markdown("<br>", visible=show_br)
gr.Markdown("Target Image (result):")
with gr.Row():
faces_index = gr.Textbox(
value="0",
placeholder="Which face(s) to Swap into Target (comma separated)",
label="Comma separated face number(s); Example: 1,0,2",
)
gender_target = gr.Radio(
["No", "Female Only", "Male Only"],
value="No",
label="Gender Detection (Target)",
type="index",
)
gr.Markdown("<br>", visible=show_br)
with gr.Row():
face_restorer_name = gr.Radio(
label="Restore Face",
choices=["None"] + [x.name() for x in shared.face_restorers],
value=shared.face_restorers[0].name(),
type="value",
)
with gr.Column():
face_restorer_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Restore Face Visibility"
)
codeformer_weight = gr.Slider(
0, 1, 0.5, step=0.1, label="CodeFormer Weight (Fidelity)", info="0 = far from original (max restoration), 1 = close to original (min restoration)"
)
gr.Markdown("<br>", visible=show_br)
swap_in_source = gr.Checkbox(
False,
label="Swap in source image",
visible=is_img2img,
)
swap_in_generated = gr.Checkbox(
True,
label="Swap in generated image",
visible=is_img2img,
)
# select_source.select(on_select_source,[save_original],[control_col_1,control_col_2,control_col_3,save_original,imgs_hash_clear],show_progress=False)
select_source.select(on_select_source,None,[control_col_1,control_col_2,control_col_3,imgs_hash_clear],show_progress=False)
return img, imgs, select_source, face_model, source_folder, save_original, mask_face, source_faces_index, gender_source, faces_index, gender_target, face_restorer_name, face_restorer_visibility, codeformer_weight, swap_in_source, swap_in_generated, random_image

77
reactor_ui/reactor_settings_ui.py Normal file
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@ -0,0 +1,77 @@
import gradio as gr
from scripts.reactor_logger import logger
from scripts.reactor_helpers import get_models, set_Device
from scripts.reactor_globals import DEVICE, DEVICE_LIST
try:
import torch.cuda as cuda
EP_is_visible = True if cuda.is_available() else False
except:
EP_is_visible = False
def update_models_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=get_models()
)
def show(hash_check_block: bool = True):
# TAB SETTINGS
with gr.Tab("Settings"):
models = get_models()
with gr.Row(visible=EP_is_visible):
device = gr.Radio(
label="Execution Provider",
choices=DEVICE_LIST,
value=DEVICE,
type="value",
info="Click 'Save' to apply. If you already run 'Generate' - RESTART is required: (A1111) Extensions Tab -> 'Apply and restart UI' or (SD.Next) close the Server and start it again",
scale=2,
)
save_device_btn = gr.Button("Save", scale=0)
save = gr.Markdown("", visible=EP_is_visible)
setattr(device, "do_not_save_to_config", True)
save_device_btn.click(
set_Device,
inputs=[device],
outputs=[save],
)
with gr.Row():
if len(models) == 0:
logger.warning(
"You should at least have one model in models directory, please read the doc here: https://github.com/Gourieff/sd-webui-reactor/"
)
model = gr.Dropdown(
choices=models,
label="Model not found, please download one and refresh the list"
)
else:
model = gr.Dropdown(
choices=models, label="Model", value=models[0]
)
models_update = gr.Button(
value="🔄",
variant="tool",
)
models_update.click(
update_models_list,
inputs=[model],
outputs=[model],
)
console_logging_level = gr.Radio(
["No log", "Minimum", "Default"],
value="Minimum",
label="Console Log Level",
type="index"
)
gr.Markdown("<br>", visible=hash_check_block)
with gr.Row(visible=hash_check_block):
source_hash_check = gr.Checkbox(
True,
label="Source Image Hash Check",
info="Recommended to keep it ON. Processing is faster when Source Image is the same."
)
target_hash_check = gr.Checkbox(
False,
label="Target Image Hash Check",
info="Affects if you use Extras tab or img2img with only 'Swap in source image' on."
)
return model, device, console_logging_level, source_hash_check, target_hash_check

61
reactor_ui/reactor_tools_ui.py Normal file
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@ -0,0 +1,61 @@
import gradio as gr
from scripts.reactor_swapper import build_face_model, blend_faces
# TAB TOOLS
def show():
with gr.Tab("Tools"):
with gr.Tab("Face Models"):
with gr.Tab("Single"):
gr.Markdown("Load an image containing one person, name it and click 'Build and Save'")
img_fm = gr.Image(
type="pil",
label="Load an Image to build -Face Model-",
)
with gr.Row(equal_height=True):
fm_name = gr.Textbox(
value="",
placeholder="Please type any name (e.g. Elena)",
label="Face Model Name",
)
save_fm_btn = gr.Button("Build and Save")
save_fm = gr.Markdown("You can find saved models in 'models/reactor/faces'")
save_fm_btn.click(
build_face_model,
inputs=[img_fm, fm_name],
outputs=[save_fm],
)
with gr.Tab("Blend"):
gr.Markdown("Load a set of images containing any person, name it and click 'Build and Save'")
with gr.Row():
imgs_fm = gr.Files(
label=f"Load Images to build -Blended Face Model-",
file_types=["image"]
)
with gr.Column():
compute_method = gr.Radio(
["Mean", "Median", "Mode"],
value="Mean",
label="Compute Method",
type="index",
info="Mean (recommended) - Average value (best result 👍); Median* - Mid-point value (may be funny 😅); Mode - Most common value (may be scary 😨); *Mean and Median will be simillar if you load two images"
)
shape_check = gr.Checkbox(
False,
label="Check -Embedding Shape- on Similarity",
info="(Experimental) Turn it ON if you want to skip the faces which are too much different from the first one in the list to prevent some probable 'shape mismatches'"
)
with gr.Row(equal_height=True):
fm_name = gr.Textbox(
value="",
placeholder="Please type any name (e.g. Elena)",
label="Face Model Name",
)
save_fm_btn = gr.Button("Build and Save")
save_fm = gr.Markdown("You can find saved models in 'models/reactor/faces'")
save_fm_btn.click(
blend_faces,
inputs=[imgs_fm, fm_name, compute_method, shape_check],
outputs=[save_fm],
)

47
reactor_ui/reactor_upscale_ui.py Normal file
View File

@ -0,0 +1,47 @@
import gradio as gr
from modules import shared
def update_upscalers_list(selected: str):
return gr.Dropdown.update(
value=selected, choices=[upscaler.name for upscaler in shared.sd_upscalers]
)
# TAB UPSCALE
def show(show_br: bool = True):
with gr.Tab("Upscale"):
with gr.Row():
restore_first = gr.Checkbox(
True,
label="1. Restore Face -> 2. Upscale (-Uncheck- if you want vice versa)",
info="Postprocessing Order",
scale=2
)
upscale_force = gr.Checkbox(
False,
label="Force Upscale",
info="Upscale anyway - even if no face found",
scale=1
)
with gr.Row():
upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers],
label="Upscaler",
value="None",
info="Won't scale if you choose -Swap in Source- via img2img, only 1x-postprocessing will affect (texturing, denoising, restyling etc.)"
)
upscalers_update = gr.Button(
value="🔄",
variant="tool",
)
upscalers_update.click(
update_upscalers_list,
inputs=[upscaler_name],
outputs=[upscaler_name],
)
gr.Markdown("<br>", visible=show_br)
with gr.Row():
upscaler_scale = gr.Slider(1, 8, 1, step=0.1, label="Scale by")
upscaler_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Upscaler Visibility (if scale = 1)"
)
return restore_first, upscaler_name, upscaler_scale, upscaler_visibility, upscale_force

76
scripts/reactor_api.py
View File

@ -1,12 +1,18 @@
''' '''
Thanks SpenserCai for the original version of the roop api script Thanks SpenserCai for the original version of the roop api script
----------------------------------- -----------------------------------
--- ReActor External API v1.0.1 --- --- ReActor External API v1.0.7 ---
----------------------------------- -----------------------------------
''' '''
import os, glob import os, glob
from datetime import datetime, date from datetime import datetime, date
from fastapi import FastAPI, Body from fastapi import FastAPI, Body
# from fastapi.exceptions import HTTPException
# from io import BytesIO
# from PIL import Image
# import base64
# import numpy as np
# import cv2
# from modules.api.models import * # from modules.api.models import *
from modules import scripts, shared from modules import scripts, shared
@ -14,8 +20,18 @@ from modules.api import api
import gradio as gr import gradio as gr
from scripts.reactor_swapper import EnhancementOptions, swap_face from scripts.reactor_swapper import EnhancementOptions, swap_face, DetectionOptions
from scripts.reactor_logger import logger from scripts.reactor_logger import logger
from scripts.reactor_helpers import get_facemodels
# XYZ init:
from scripts.reactor_xyz import run
try:
import modules.script_callbacks as script_callbacks
script_callbacks.on_before_ui(run)
# script_callbacks.on_app_started(reactor_api)
except:
pass
def default_file_path(): def default_file_path():
@ -52,6 +68,19 @@ def get_full_model(model_name):
return model return model
return None return None
# def decode_base64_to_image_rgba(encoding):
# if encoding.startswith("data:image/"):
# encoding = encoding.split(";")[1].split(",")[1]
# try:
# im_bytes = base64.b64decode(encoding)
# im_arr = np.frombuffer(im_bytes, dtype=np.uint8) # im_arr is one-dim Numpy array
# img = cv2.imdecode(im_arr, flags=cv2.IMREAD_UNCHANGED)
# img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA)
# image = Image.fromarray(img, mode="RGBA")
# return image
# except Exception as e:
# raise HTTPException(status_code=500, detail="Invalid encoded image") from e
def reactor_api(_: gr.Blocks, app: FastAPI): def reactor_api(_: gr.Blocks, app: FastAPI):
@app.post("/reactor/image") @app.post("/reactor/image")
async def reactor_image( async def reactor_image(
@ -60,7 +89,7 @@ def reactor_api(_: gr.Blocks, app: FastAPI):
source_faces_index: list[int] = Body([0],title="Comma separated face number(s) from swap-source image"), source_faces_index: list[int] = Body([0],title="Comma separated face number(s) from swap-source image"),
face_index: list[int] = Body([0],title="Comma separated face number(s) for target image (result)"), face_index: list[int] = Body([0],title="Comma separated face number(s) for target image (result)"),
upscaler: str = Body("None",title="Upscaler"), upscaler: str = Body("None",title="Upscaler"),
scale: int = Body(1,title="Scale by"), scale: float = Body(1,title="Scale by"),
upscale_visibility: float = Body(1,title="Upscaler visibility (if scale = 1)"), upscale_visibility: float = Body(1,title="Upscaler visibility (if scale = 1)"),
face_restorer: str = Body("None",title="Restore Face: 0 - None; 1 - CodeFormer; 2 - GFPGA"), face_restorer: str = Body("None",title="Restore Face: 0 - None; 1 - CodeFormer; 2 - GFPGA"),
restorer_visibility: float = Body(1,title="Restore visibility value"), restorer_visibility: float = Body(1,title="Restore visibility value"),
@ -71,29 +100,53 @@ def reactor_api(_: gr.Blocks, app: FastAPI):
gender_target: int = Body(0,title="Gender Detection (Target) (0 - No, 1 - Female Only, 2 - Male Only)"), gender_target: int = Body(0,title="Gender Detection (Target) (0 - No, 1 - Female Only, 2 - Male Only)"),
save_to_file: int = Body(0,title="Save Result to file, 0 - No, 1 - Yes"), save_to_file: int = Body(0,title="Save Result to file, 0 - No, 1 - Yes"),
result_file_path: str = Body("",title="(if 'save_to_file = 1') Result file path"), result_file_path: str = Body("",title="(if 'save_to_file = 1') Result file path"),
device: str = Body("CPU",title="CPU or CUDA (if you have it)") device: str = Body("CPU",title="CPU or CUDA (if you have it)"),
mask_face: int = Body(0,title="Face Mask Correction, 1 - True, 0 - False"),
select_source: int = Body(0,title="Select Source, 0 - Image, 1 - Face Model, 2 - Source Folder"),
face_model: str = Body("None",title="Filename of the face model (from 'models/reactor/faces'), e.g. elena.safetensors"),
source_folder: str = Body("",title="The path to the folder containing source faces images"),
random_image: int = Body(0,title="Randomly select an image from the path"),
upscale_force: int = Body(0,title="Force Upscale even if no face found"),
det_thresh: float = Body(0.5,title="Face Detection Threshold"),
det_maxnum: int = Body(0,title="Maximum number of faces to detect (0 is unlimited)"),
): ):
s_image = api.decode_base64_to_image(source_image) s_image = api.decode_base64_to_image(source_image) if select_source == 0 else None
t_image = api.decode_base64_to_image(target_image) t_image = api.decode_base64_to_image(target_image)
if t_image.mode == 'RGBA':
_, _, _, alpha = t_image.split()
else:
alpha = None
sf_index = source_faces_index sf_index = source_faces_index
f_index = face_index f_index = face_index
gender_s = gender_source gender_s = gender_source
gender_t = gender_target gender_t = gender_target
restore_first_bool = True if restore_first == 1 else False restore_first_bool = True if restore_first == 1 else False
up_options = EnhancementOptions(do_restore_first=restore_first_bool, scale=scale, upscaler=get_upscaler(upscaler), upscale_visibility=upscale_visibility,face_restorer=get_face_restorer(face_restorer),restorer_visibility=restorer_visibility,codeformer_weight=codeformer_weight) mask_face = True if mask_face == 1 else False
random_image = False if random_image == 0 else True
upscale_force = False if upscale_force == 0 else True
up_options = EnhancementOptions(do_restore_first=restore_first_bool, scale=scale, upscaler=get_upscaler(upscaler), upscale_visibility=upscale_visibility,face_restorer=get_face_restorer(face_restorer),restorer_visibility=restorer_visibility,codeformer_weight=codeformer_weight,upscale_force=upscale_force)
det_options = DetectionOptions(det_thresh=det_thresh, det_maxnum=det_maxnum)
use_model = get_full_model(model) use_model = get_full_model(model)
if use_model is None: if use_model is None:
Exception("Model not found") Exception("Model not found")
result = swap_face(s_image, t_image, use_model, sf_index, f_index, up_options, gender_s, gender_t, True, True, device) result = swap_face(s_image, t_image, use_model, sf_index, f_index, up_options, gender_s, gender_t, True, True, device, mask_face, select_source, face_model, source_folder, None, random_image,det_options)
result_img = result[0]
if alpha is not None:
result_img = result_img.convert("RGBA")
result_img.putalpha(alpha)
if save_to_file == 1: if save_to_file == 1:
if result_file_path == "": if result_file_path == "":
result_file_path = default_file_path() result_file_path = default_file_path()
try: try:
result[0].save(result_file_path, format='PNG') result_img.save(result_file_path, format='PNG')
logger.status("Result has been saved to: %s", result_file_path) logger.status("Result has been saved to: %s", result_file_path)
except Exception as e: except Exception as e:
logger.error("Error while saving result: %s",e) logger.error("Error while saving result: %s",e)
return {"image": api.encode_pil_to_base64(result[0])} return {"image": api.encode_pil_to_base64(result_img)}
@app.get("/reactor/models") @app.get("/reactor/models")
async def reactor_models(): async def reactor_models():
@ -105,6 +158,11 @@ def reactor_api(_: gr.Blocks, app: FastAPI):
names = [upscaler.name for upscaler in shared.sd_upscalers] names = [upscaler.name for upscaler in shared.sd_upscalers]
return {"upscalers": names} return {"upscalers": names}
@app.get("/reactor/facemodels")
async def reactor_facemodels():
facemodels = [os.path.split(model)[1].split(".")[0] for model in get_facemodels()]
return {"facemodels": facemodels}
try: try:
import modules.script_callbacks as script_callbacks import modules.script_callbacks as script_callbacks

4
scripts/entities/face.py → scripts/reactor_entities/face.py
View File

@ -6,10 +6,10 @@ from modules import images
from PIL import Image from PIL import Image
from scripts.entities.rect import Point, Rect from scripts.reactor_entities.rect import Point, Rect
class Face: class FaceArea:
def __init__(self, entire_image: np.ndarray, face_area: Rect, face_margin: float, face_size: int, upscaler: str): def __init__(self, entire_image: np.ndarray, face_area: Rect, face_margin: float, face_size: int, upscaler: str):
self.face_area = face_area self.face_area = face_area
self.center = face_area.center self.center = face_area.center

0
scripts/entities/rect.py → scripts/reactor_entities/rect.py
View File

722
scripts/reactor_faceswap.py
View File

@ -2,17 +2,12 @@ import os, glob
import gradio as gr import gradio as gr
import tempfile import tempfile
from PIL import Image from PIL import Image
try:
import torch.cuda as cuda
EP_is_visible = True if cuda.is_available() else False
except:
EP_is_visible = False
from typing import List from typing import List
from PIL import Image from PIL import Image
import modules.scripts as scripts import modules.scripts as scripts
from modules.upscaler import Upscaler, UpscalerData from modules.upscaler import Upscaler, UpscalerData
from modules import scripts, shared, images, scripts_postprocessing from modules import scripts, shared, images, scripts_postprocessing, ui_components
from modules.processing import ( from modules.processing import (
Processed, Processed,
StableDiffusionProcessing, StableDiffusionProcessing,
@ -20,38 +15,33 @@ from modules.processing import (
) )
from modules.face_restoration import FaceRestoration from modules.face_restoration import FaceRestoration
from modules.images import save_image from modules.images import save_image
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
model_path = os.path.abspath("models")
from reactor_ui import (
ui_main,
ui_upscale,
ui_tools,
ui_settings,
ui_detection,
ui_masking,
)
from scripts.reactor_logger import logger from scripts.reactor_logger import logger
from scripts.reactor_swapper import EnhancementOptions,MaskOptions,MaskOption, swap_face, check_process_halt, reset_messaged from scripts.reactor_swapper import (
EnhancementOptions,
DetectionOptions,
swap_face,
check_process_halt,
reset_messaged,
MaskOptions,
MaskOption,
)
from scripts.reactor_version import version_flag, app_title from scripts.reactor_version import version_flag, app_title
from scripts.console_log_patch import apply_logging_patch from scripts.console_log_patch import apply_logging_patch
from scripts.reactor_helpers import make_grid, get_image_path, set_Device from scripts.reactor_helpers import (
from scripts.reactor_globals import DEVICE, DEVICE_LIST make_grid,
set_Device,
get_SDNEXT,
)
MODELS_PATH = None from scripts.reactor_globals import SWAPPER_MODELS_PATH #, DEVICE, DEVICE_LIST
def get_models():
global MODELS_PATH
models_path_init = os.path.join(models_path, "insightface/*")
models = glob.glob(models_path_init)
models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
models_names = []
for model in models:
model_path = os.path.split(model)
if MODELS_PATH is None:
MODELS_PATH = model_path[0]
model_name = model_path[1]
models_names.append(model_name)
return models_names
class FaceSwapScript(scripts.Script): class FaceSwapScript(scripts.Script):
@ -62,161 +52,46 @@ class FaceSwapScript(scripts.Script):
return scripts.AlwaysVisible return scripts.AlwaysVisible
def ui(self, is_img2img): def ui(self, is_img2img):
with gr.Accordion(f"{app_title}", open=False): with ui_components.InputAccordion(False, label=f"{app_title}") as enable:
enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}") # with gr.Accordion(f"{app_title}", open=False):
gr.Markdown("<br>")
with gr.Tab("Main"):
with gr.Column(): # def on_files_upload_uncheck_so(selected: bool):
with gr.Tab("Single Source Image"): # global SAVE_ORIGINAL
img = gr.Image(type="pil") # SAVE_ORIGINAL = selected
with gr.Tab("Multiple Source Images"): # return gr.Checkbox.update(value=False,visible=False)
face_files = gr.File(label="Multiple Source Face Files",file_count="multiple",file_types=["image"],info="Upload multiple face files and each file will be processed in post processing") # def on_files_clear():
save_original = gr.Checkbox(False, label="Save Original", info="Save the original image(s) made before swapping; If you use \"img2img\" - this option will affect with \"Swap in generated\" only") # clear_faces_list()
mask_face = gr.Checkbox(False, label="Mask Faces", info="Attempt to mask only the faces and eliminate pixelation of the image around the contours. Additional settings in the Masking tab.") # return gr.Checkbox.update(value=SAVE_ORIGINAL,visible=True)
gr.Markdown("<br>") # SD.Next fix
gr.Markdown("Source Image (above):") if get_SDNEXT():
with gr.Row(): enable = gr.Checkbox(False, label="Enable")
source_faces_index = gr.Textbox(
value="0",
placeholder="Which face(s) to use as Source (comma separated)",
label="Comma separated face number(s); Example: 0,2,1",
)
gender_source = gr.Radio(
["No", "Female Only", "Male Only"],
value="No",
label="Gender Detection (Source)",
type="index",
)
gr.Markdown("<br>")
gr.Markdown("Target Image (result):")
with gr.Row():
faces_index = gr.Textbox(
value="0",
placeholder="Which face(s) to Swap into Target (comma separated)",
label="Comma separated face number(s); Example: 1,0,2",
)
gender_target = gr.Radio(
["No", "Female Only", "Male Only"],
value="No",
label="Gender Detection (Target)",
type="index",
)
gr.Markdown("<br>")
with gr.Row():
face_restorer_name = gr.Radio(
label="Restore Face",
choices=["None"] + [x.name() for x in shared.face_restorers],
value=shared.face_restorers[0].name(),
type="value",
)
with gr.Column():
face_restorer_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Restore Face Visibility"
)
codeformer_weight = gr.Slider(
0, 1, 0.5, step=0.1, label="CodeFormer Weight", info="0 = maximum effect, 1 = minimum effect"
)
gr.Markdown("<br>")
swap_in_source = gr.Checkbox(
False,
label="Swap in source image",
visible=is_img2img,
)
swap_in_generated = gr.Checkbox(
True,
label="Swap in generated image",
visible=is_img2img,
)
with gr.Tab("Upscale"):
restore_first = gr.Checkbox(
True,
label="1. Restore Face -> 2. Upscale (-Uncheck- if you want vice versa)",
info="Postprocessing Order"
)
upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers],
label="Upscaler",
value="None",
info="Won't scale if you choose -Swap in Source- via img2img, only 1x-postprocessing will affect (texturing, denoising, restyling etc.)"
)
gr.Markdown("<br>")
with gr.Row():
upscaler_scale = gr.Slider(1, 8, 1, step=0.1, label="Scale by")
upscaler_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Upscaler Visibility (if scale = 1)"
)
with gr.Tab("Masking"):
save_face_mask = gr.Checkbox(False, label="Save Face Mask", info="Save the face mask as a separate image with alpha transparency.")
use_minimal_area = gr.Checkbox(MaskOption.DEFAULT_USE_MINIMAL_AREA, label="Use Minimal Area", info="Use the least amount of area for the mask as possible. This is good for multiple faces that are close together or for preserving the most of the surrounding image.")
mask_areas = gr.CheckboxGroup( # enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
label="Mask areas", choices=["Face", "Hair", "Hat", "Neck"], type="value", value= MaskOption.DEFAULT_FACE_AREAS gr.Markdown(f"<sup>The Fast and Simple FaceSwap Extension - {version_flag}</sup>")
)
face_size = gr.Radio(
label = "Face Size", choices = [512,256,128],value=MaskOption.DEFAULT_FACE_SIZE,type="value", info="Size of the masked area. Use larger numbers if the face is expected to be large, smaller if small. Default is 512."
)
mask_blur = gr.Slider(label="Mask blur", minimum=0, maximum=64, step=1, value=12,info="The number of pixels from the outer edge of the mask to blur.")
mask_vignette_fallback_threshold = gr.Slider( # TAB MAIN
minimum=0.1, msgs: dict = {
maximum=1.0, "extra_multiple_source": "",
step=0.01, }
value=MaskOption.DEFAULT_VIGNETTE_THRESHOLD, img, imgs, select_source, face_model, source_folder, save_original, mask_face, source_faces_index, gender_source, faces_index, gender_target, face_restorer_name, face_restorer_visibility, codeformer_weight, swap_in_source, swap_in_generated, random_image = ui_main.show(is_img2img=is_img2img, **msgs)
label="Vignette fallback threshold",
info="Switch to a rectangular vignette mask when masked area is only this specified percentage of Face Size." # TAB DETECTION
) det_thresh, det_maxnum = ui_detection.show()
with gr.Tab("Settings"):
models = get_models() # TAB UPSCALE
with gr.Row(visible=EP_is_visible): restore_first, upscaler_name, upscaler_scale, upscaler_visibility, upscale_force = ui_upscale.show()
device = gr.Radio(
label="Execution Provider", # TAB MASKING
choices=DEVICE_LIST, save_face_mask, use_minimal_area, mask_areas, face_size, mask_blur, mask_vignette_fallback_threshold = ui_masking.show()
value=DEVICE,
type="value", # TAB TOOLS
info="If you already run 'Generate' - RESTART is required to apply. Click 'Save', (A1111) Extensions Tab -> 'Apply and restart UI' or (SD.Next) close the Server and start it again", ui_tools.show()
scale=2,
) # TAB SETTINGS
save_device_btn = gr.Button("Save", scale=0) model, device, console_logging_level, source_hash_check, target_hash_check = ui_settings.show()
save = gr.Markdown("", visible=EP_is_visible)
setattr(device, "do_not_save_to_config", True) gr.Markdown("<span style='display:block;text-align:right;padding:3px;font-size:0.666em;margin-bottom:-12px;'>by <a style='font-weight:normal' href='https://github.com/Gourieff' target='_blank'>Eugene Gourieff</a></span>")
save_device_btn.click(
set_Device,
inputs=[device],
outputs=[save],
)
with gr.Row():
if len(models) == 0:
logger.warning(
"You should at least have one model in models directory, please read the doc here : https://github.com/Gourieff/sd-webui-reactor/"
)
model = gr.Dropdown(
choices=models,
label="Model not found, please download one and reload WebUI",
)
else:
model = gr.Dropdown(
choices=models, label="Model", value=models[0]
)
console_logging_level = gr.Radio(
["No log", "Minimum", "Default"],
value="Minimum",
label="Console Log Level",
type="index",
)
gr.Markdown("<br>")
with gr.Row():
source_hash_check = gr.Checkbox(
True,
label="Source Image Hash Check",
info="Recommended to keep it ON. Processing is faster when Source Image is the same."
)
target_hash_check = gr.Checkbox(
False,
label="Target Image Hash Check",
info="Affects if you use Extras tab or img2img with only 'Swap in source image' on."
)
return [ return [
img, img,
@ -241,13 +116,20 @@ class FaceSwapScript(scripts.Script):
target_hash_check, target_hash_check,
device, device,
mask_face, mask_face,
select_source,
face_model,
source_folder,
imgs,
random_image,
upscale_force,
det_thresh,
det_maxnum,
save_face_mask, save_face_mask,
mask_areas, mask_areas,
mask_blur, mask_blur,
use_minimal_area, use_minimal_area,
face_size, face_size,
mask_vignette_fallback_threshold, mask_vignette_fallback_threshold,
face_files
] ]
@ -275,6 +157,14 @@ class FaceSwapScript(scripts.Script):
upscale_visibility=self.upscaler_visibility, upscale_visibility=self.upscaler_visibility,
restorer_visibility=self.face_restorer_visibility, restorer_visibility=self.face_restorer_visibility,
codeformer_weight=self.codeformer_weight, codeformer_weight=self.codeformer_weight,
upscale_force=self.upscale_force
)
@property
def detection_options(self) -> DetectionOptions:
return DetectionOptions(
det_thresh=self.det_thresh,
det_maxnum=self.det_maxnum
) )
@property @property
def mask_options(self) -> MaskOptions: def mask_options(self) -> MaskOptions:
@ -312,23 +202,31 @@ class FaceSwapScript(scripts.Script):
target_hash_check, target_hash_check,
device, device,
mask_face, mask_face,
select_source,
face_model,
source_folder,
imgs,
random_image,
upscale_force,
det_thresh,
det_maxnum,
save_face_mask:bool, save_face_mask:bool,
mask_areas, mask_areas,
mask_blur:int, mask_blur:int,
mask_use_minimal_area, mask_use_minimal_area,
mask_face_size, mask_face_size,
mask_vignette_fallback_threshold, mask_vignette_fallback_threshold
face_files
): ):
self.enable = enable self.enable = enable
if self.enable: if self.enable:
logger.debug("*** Start process")
reset_messaged() reset_messaged()
if check_process_halt(): if check_process_halt():
return return
global MODELS_PATH global SWAPPER_MODELS_PATH
self.source = img self.source = img
self.face_restorer_name = face_restorer_name self.face_restorer_name = face_restorer_name
self.upscaler_scale = upscaler_scale self.upscaler_scale = upscaler_scale
@ -338,7 +236,7 @@ class FaceSwapScript(scripts.Script):
self.upscaler_name = upscaler_name self.upscaler_name = upscaler_name
self.swap_in_source = swap_in_source self.swap_in_source = swap_in_source
self.swap_in_generated = swap_in_generated self.swap_in_generated = swap_in_generated
self.model = os.path.join(MODELS_PATH,model) self.model = os.path.join(SWAPPER_MODELS_PATH,model)
self.console_logging_level = console_logging_level self.console_logging_level = console_logging_level
self.gender_source = gender_source self.gender_source = gender_source
self.gender_target = gender_target self.gender_target = gender_target
@ -348,13 +246,20 @@ class FaceSwapScript(scripts.Script):
self.target_hash_check = target_hash_check self.target_hash_check = target_hash_check
self.device = device self.device = device
self.mask_face = mask_face self.mask_face = mask_face
self.select_source = select_source
self.face_model = face_model
self.source_folder = source_folder
self.source_imgs = imgs
self.random_image = random_image
self.upscale_force = upscale_force
self.det_thresh=det_thresh
self.det_maxnum=det_maxnum
self.save_face_mask = save_face_mask self.save_face_mask = save_face_mask
self.mask_blur = mask_blur self.mask_blur = mask_blur
self.mask_areas = mask_areas self.mask_areas = mask_areas
self.mask_face_size = mask_face_size self.mask_face_size = mask_face_size
self.mask_vignette_fallback_threshold = mask_vignette_fallback_threshold self.mask_vignette_fallback_threshold = mask_vignette_fallback_threshold
self.mask_use_minimal_area = mask_use_minimal_area self.mask_use_minimal_area = mask_use_minimal_area
self.face_files = face_files
if self.gender_source is None or self.gender_source == "No": if self.gender_source is None or self.gender_source == "No":
self.gender_source = 0 self.gender_source = 0
if self.gender_target is None or self.gender_target == "No": if self.gender_target is None or self.gender_target == "No":
@ -375,13 +280,34 @@ class FaceSwapScript(scripts.Script):
self.source_hash_check = True self.source_hash_check = True
if self.target_hash_check is None: if self.target_hash_check is None:
self.target_hash_check = False self.target_hash_check = False
if self.mask_face is None:
self.mask_face = False
if self.random_image is None:
self.random_image = False
if self.upscale_force is None:
self.upscale_force = False
if shared.state.job_count > 0:
# logger.debug(f"Job count: {shared.state.job_count}")
self.face_restorer_visibility = shared.opts.data['restorer_visibility'] if 'restorer_visibility' in shared.opts.data.keys() else face_restorer_visibility
self.codeformer_weight = shared.opts.data['codeformer_weight'] if 'codeformer_weight' in shared.opts.data.keys() else codeformer_weight
self.mask_face = shared.opts.data['mask_face'] if 'mask_face' in shared.opts.data.keys() else mask_face
self.face_model = shared.opts.data['face_model'] if 'face_model' in shared.opts.data.keys() else face_model
logger.debug("*** Set Device")
set_Device(self.device) set_Device(self.device)
apply_logging_patch(console_logging_level)
if self.source is not None:
if (self.save_original is None or not self.save_original) and (self.select_source == 2 or self.source_imgs is not None):
p.do_not_save_samples = True
if ((self.source is not None or self.source_imgs is not None) and self.select_source == 0) or ((self.face_model is not None and self.face_model != "None") and self.select_source == 1) or ((self.source_folder is not None and self.source_folder != "") and self.select_source == 2):
logger.debug("*** Log patch")
apply_logging_patch(console_logging_level)
if isinstance(p, StableDiffusionProcessingImg2Img) and self.swap_in_source: if isinstance(p, StableDiffusionProcessingImg2Img) and self.swap_in_source:
logger.debug("*** Check process")
logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index) logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
for i in range(len(p.init_images)): for i in range(len(p.init_images)):
@ -399,7 +325,13 @@ class FaceSwapScript(scripts.Script):
source_hash_check=self.source_hash_check, source_hash_check=self.source_hash_check,
target_hash_check=self.target_hash_check, target_hash_check=self.target_hash_check,
device=self.device, device=self.device,
mask_face=mask_face, mask_face=self.mask_face,
select_source=self.select_source,
face_model = self.face_model,
source_folder = None,
source_imgs = None,
random_image = False,
detection_options=self.detection_options,
mask_options=self.mask_options mask_options=self.mask_options
) )
p.init_images[i] = result p.init_images[i] = result
@ -413,10 +345,13 @@ class FaceSwapScript(scripts.Script):
elif self.face_files is None or len(self.face_files) == 0: elif self.face_files is None or len(self.face_files) == 0:
logger.error("Please provide a source face") logger.error("Please provide a source face")
return
def postprocess(self, p: StableDiffusionProcessing, processed: Processed, *args): def postprocess(self, p: StableDiffusionProcessing, processed: Processed, *args):
if self.enable: if self.enable:
logger.debug("*** Check postprocess - before IF")
reset_messaged() reset_messaged()
if check_process_halt(): if check_process_halt():
return return
@ -424,17 +359,21 @@ class FaceSwapScript(scripts.Script):
orig_images : List[Image.Image] = processed.images[processed.index_of_first_image:] orig_images : List[Image.Image] = processed.images[processed.index_of_first_image:]
orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:] orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:]
result_images:List[Image.Image] = []
if self.save_original or ((self.select_source == 2 and self.source_folder is not None and self.source_folder != "") or (self.select_source == 0 and self.source_imgs is not None and self.source is None)):
if self.save_original: logger.debug("*** Check postprocess - after IF")
result_images: List = processed.images result_images: List = processed.images
# result_info: List = processed.infotexts # result_info: List = processed.infotexts
if self.swap_in_generated: if self.swap_in_generated:
logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index) logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
if self.source is not None: if self.source is not None:
# self.source_folder = None
self.source_imgs = None
for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)): for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)):
if check_process_halt(): if check_process_halt():
@ -442,7 +381,7 @@ class FaceSwapScript(scripts.Script):
break break
if len(orig_images) > 1: if len(orig_images) > 1:
logger.status("Swap in %s", i) logger.status("Swap in %s", i)
result, output, swapped, masked_faces = swap_face( result, output, swapped = swap_face(
self.source, self.source,
img, img,
source_faces_index=self.source_faces_index, source_faces_index=self.source_faces_index,
@ -455,8 +394,33 @@ class FaceSwapScript(scripts.Script):
target_hash_check=self.target_hash_check, target_hash_check=self.target_hash_check,
device=self.device, device=self.device,
mask_face=self.mask_face, mask_face=self.mask_face,
select_source=self.select_source,
face_model = self.face_model,
source_folder = self.source_folder,
source_imgs = self.source_imgs,
random_image = self.random_image,
detection_options=self.detection_options,
mask_options=self.mask_options mask_options=self.mask_options
) )
if self.select_source == 2 or (self.select_source == 0 and self.source_imgs is not None and self.source is None):
if len(result) > 0 and swapped > 0:
# result_images.extend(result)
if self.save_original:
result_images.extend(result)
else:
result_images = result
suffix = "-swapped"
for i,x in enumerate(result):
try:
img_path = save_image(result[i], p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png", info=info, p=p, suffix=suffix)
except:
logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)")
elif len(result) == 0:
logger.error("Cannot create a result image")
else:
if result is not None and swapped > 0: if result is not None and swapped > 0:
result_images.append(result) result_images.append(result)
suffix = "-swapped" suffix = "-swapped"
@ -464,13 +428,13 @@ class FaceSwapScript(scripts.Script):
img_path = save_image(result, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png", info=info, p=p, suffix=suffix) img_path = save_image(result, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png", info=info, p=p, suffix=suffix)
except: except:
logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)") logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)")
if self.mask_face and self.save_face_mask and masked_faces is not None: # if self.mask_face and self.save_face_mask and masked_faces is not None:
result_images.append(masked_faces) # result_images.append(masked_faces)
suffix = "-mask" # suffix = "-mask"
try: # try:
img_path = save_image(masked_faces, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix) # img_path = save_image(masked_faces, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix)
except: # except:
logger.error("Cannot save a Masked Face image - please, check SD WebUI Settings (Saving and Paths)") # logger.error("Cannot save a Masked Face image - please, check SD WebUI Settings (Saving and Paths)")
elif result is None: elif result is None:
logger.error("Cannot create a result image") logger.error("Cannot create a result image")
@ -480,48 +444,7 @@ class FaceSwapScript(scripts.Script):
# fullfn = split_fullfn[0] + ".txt" # fullfn = split_fullfn[0] + ".txt"
# with open(fullfn, 'w', encoding="utf8") as f: # with open(fullfn, 'w', encoding="utf8") as f:
# f.writelines(output) # f.writelines(output)
if self.face_files is not None and len(self.face_files) > 0:
for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)):
for j,f_img in enumerate(self.face_files):
if check_process_halt():
postprocess_run = False
break
if len(self.face_files) > 1:
logger.status("Swap in face file #%s", j+1)
result, output, swapped, masked_faces = swap_face(
Image.open(os.path.abspath(f_img.name)),
img,
source_faces_index=self.source_faces_index,
faces_index=self.faces_index,
model=self.model,
enhancement_options=self.enhancement_options,
gender_source=self.gender_source,
gender_target=self.gender_target,
source_hash_check=self.source_hash_check,
target_hash_check=self.target_hash_check,
device=self.device,
mask_face=self.mask_face,
mask_options=self.mask_options
)
if result is not None and swapped > 0:
result_images.append(result)
suffix = f"-swapped-ff-{j+1}"
try:
img_path = save_image(result, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix)
except:
logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)")
if self.mask_face and self.save_face_mask and masked_faces is not None:
result_images.append(masked_faces)
suffix = f"-mask-ff-{j+1}"
try:
img_path = save_image(masked_faces, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix)
except:
logger.error("Cannot save a Masked Face image - please, check SD WebUI Settings (Saving and Paths)")
elif result is None:
logger.error("Cannot create a result image")
if shared.opts.return_grid and len(result_images) > 2 and postprocess_run: if shared.opts.return_grid and len(result_images) > 2 and postprocess_run:
grid = make_grid(result_images) grid = make_grid(result_images)
result_images.insert(0, grid) result_images.insert(0, grid)
@ -532,62 +455,31 @@ class FaceSwapScript(scripts.Script):
processed.images = result_images processed.images = result_images
# processed.infotexts = result_info # processed.infotexts = result_info
elif self.face_files is not None and len(self.face_files) > 0:
for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)):
for j,f_img in enumerate(self.face_files):
if check_process_halt(): elif self.select_source == 0 and self.source is not None and self.source_imgs is not None:
postprocess_run = False
break logger.debug("*** Check postprocess - after ELIF")
if len(self.face_files) > 1:
logger.status("Swap in face file #%s", j+1) if self.result is not None:
result, output, swapped, masked_faces = swap_face( orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:]
Image.open(os.path.abspath(f_img.name)), processed.images = [self.result]
img,
source_faces_index=self.source_faces_index,
faces_index=self.faces_index,
model=self.model,
enhancement_options=self.enhancement_options,
gender_source=self.gender_source,
gender_target=self.gender_target,
source_hash_check=self.source_hash_check,
target_hash_check=self.target_hash_check,
device=self.device,
mask_face=self.mask_face,
mask_options=self.mask_options
)
if result is not None and swapped > 0:
result_images.append(result)
suffix = f"-swapped-ff-{j+1}"
try: try:
img_path = save_image(result, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix) img_path = save_image(self.result, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png", info=orig_infotexts[0], p=p, suffix="")
except: except:
logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)") logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)")
if self.mask_face and self.save_face_mask and masked_faces is not None: else:
result_images.append(masked_faces)
suffix = f"-mask-ff-{j+1}"
try:
img_path = save_image(masked_faces, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "png",info=info, p=p, suffix=suffix)
except:
logger.error("Cannot save a Masked Face image - please, check SD WebUI Settings (Saving and Paths)")
elif result is None:
logger.error("Cannot create a result image") logger.error("Cannot create a result image")
if shared.opts.return_grid and len(result_images) > 2 and postprocess_run:
grid = make_grid(result_images)
result_images.insert(0, grid)
try:
save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=info, short_filename=not shared.opts.grid_extended_filename, p=p, grid=True)
except:
logger.error("Cannot save a grid - please, check SD WebUI Settings (Saving and Paths)")
processed.images = result_images
def postprocess_batch(self, p, *args, **kwargs): def postprocess_batch(self, p, *args, **kwargs):
if self.enable and not self.save_original: if self.enable and not self.save_original:
logger.debug("*** Check postprocess_batch")
images = kwargs["images"] images = kwargs["images"]
def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args): def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
if self.enable and self.swap_in_generated and not ( self.save_original or ( self.face_files is not None and len(self.face_files) > 0)): if self.enable and self.swap_in_generated and not self.save_original and ((self.select_source == 0 and self.source is not None) or self.select_source == 1):
logger.debug("*** Check postprocess_image")
current_job_number = shared.state.job_no + 1 current_job_number = shared.state.job_no + 1
job_count = shared.state.job_count job_count = shared.state.job_count
@ -596,10 +488,10 @@ class FaceSwapScript(scripts.Script):
if check_process_halt(): if check_process_halt():
return return
if self.source is not None: # if (self.source is not None and self.select_source == 0) or ((self.face_model is not None and self.face_model != "None") and self.select_source == 1):
logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index) logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
image: Image.Image = script_pp.image image: Image.Image = script_pp.image
result, output, swapped, masked_faces = swap_face( result, output, swapped = swap_face(
self.source, self.source,
image, image,
source_faces_index=self.source_faces_index, source_faces_index=self.source_faces_index,
@ -612,8 +504,15 @@ class FaceSwapScript(scripts.Script):
target_hash_check=self.target_hash_check, target_hash_check=self.target_hash_check,
device=self.device, device=self.device,
mask_face=self.mask_face, mask_face=self.mask_face,
select_source=self.select_source,
face_model = self.face_model,
source_folder = None,
source_imgs = None,
random_image = False,
detection_options=self.detection_options,
mask_options=self.mask_options mask_options=self.mask_options
) )
self.result = result
try: try:
pp = scripts_postprocessing.PostprocessedImage(result) pp = scripts_postprocessing.PostprocessedImage(result)
pp.info = {} pp.info = {}
@ -634,130 +533,38 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
order = 20000 order = 20000
def ui(self): def ui(self):
with gr.Accordion(f"{app_title}", open=False): with ui_components.InputAccordion(False, label=f"{app_title}") as enable:
with gr.Tab("Main"): # with gr.Accordion(f"{app_title}", open=False):
with gr.Column():
img = gr.Image(type="pil")
#face_files = gr.File(file_count="multiple",file_types=["image"],info="Upload multiple face files and each file will be processed in post processing")
enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
mask_face = gr.Checkbox(False, label="Mask Faces", info="Attempt to mask only the faces and eliminate pixelation of the image around the contours. Additional settings in the Masking tab.")
gr.Markdown("Source Image (above):")
with gr.Row():
source_faces_index = gr.Textbox(
value="0",
placeholder="Which face(s) to use as Source (comma separated)",
label="Comma separated face number(s); Example: 0,2,1",
)
gender_source = gr.Radio(
["No", "Female Only", "Male Only"],
value="No",
label="Gender Detection (Source)",
type="index",
)
gr.Markdown("Target Image (result):")
with gr.Row():
faces_index = gr.Textbox(
value="0",
placeholder="Which face(s) to Swap into Target (comma separated)",
label="Comma separated face number(s); Example: 1,0,2",
)
gender_target = gr.Radio(
["No", "Female Only", "Male Only"],
value="No",
label="Gender Detection (Target)",
type="index",
)
with gr.Row():
face_restorer_name = gr.Radio(
label="Restore Face",
choices=["None"] + [x.name() for x in shared.face_restorers],
value=shared.face_restorers[0].name(),
type="value",
)
with gr.Column():
face_restorer_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Restore Face Visibility"
)
codeformer_weight = gr.Slider(
0, 1, 0.5, step=0.1, label="CodeFormer Weight", info="0 = maximum effect, 1 = minimum effect"
)
with gr.Tab("Upscale"): # SD.Next fix
restore_first = gr.Checkbox( if get_SDNEXT():
True, enable = gr.Checkbox(False, label="Enable")
label="1. Restore Face -> 2. Upscale (-Uncheck- if you want vice versa)",
info="Postprocessing Order"
)
upscaler_name = gr.Dropdown(
choices=[upscaler.name for upscaler in shared.sd_upscalers],
label="Upscaler",
value="None",
info="Won't scale if you choose -Swap in Source- via img2img, only 1x-postprocessing will affect (texturing, denoising, restyling etc.)"
)
with gr.Row():
upscaler_scale = gr.Slider(1, 8, 1, step=0.1, label="Scale by")
upscaler_visibility = gr.Slider(
0, 1, 1, step=0.1, label="Upscaler Visibility (if scale = 1)"
)
with gr.Tab("Masking"):
#save_face_mask = gr.Checkbox(False, label="Save Face Mask", info="Save the face mask as a separate image with alpha transparency.")
use_minimal_area = gr.Checkbox(MaskOption.DEFAULT_USE_MINIMAL_AREA, label="Use Minimal Area", info="Use the least amount of area for the mask as possible. This is good for multiple faces that are close together or for preserving the most of the surrounding image.")
mask_areas = gr.CheckboxGroup( # enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
label="Mask areas", choices=["Face", "Hair", "Hat", "Neck"], type="value", value= MaskOption.DEFAULT_FACE_AREAS gr.Markdown(f"<span style='display:block;font-size:0.75em;margin-bottom:-24px;'>The Fast and Simple FaceSwap Extension - {version_flag}</span>")
)
face_size = gr.Radio(
label = "Face Size", choices = [512,256,128],value=MaskOption.DEFAULT_FACE_SIZE,type="value", info="Size of the masked area. Use larger numbers if the face is expected to be large, smaller if small. Default is 512."
)
mask_blur = gr.Slider(label="Mask blur", minimum=0, maximum=64, step=1, value=12,info="The number of pixels from the outer edge of the mask to blur.")
mask_vignette_fallback_threshold = gr.Slider( # TAB MAIN
minimum=0.1, msgs: dict = {
maximum=1.0, "extra_multiple_source": " | Сomparison grid as a result",
step=0.01, }
value=MaskOption.DEFAULT_VIGNETTE_THRESHOLD, img, imgs, select_source, face_model, source_folder, save_original, mask_face, source_faces_index, gender_source, faces_index, gender_target, face_restorer_name, face_restorer_visibility, codeformer_weight, swap_in_source, swap_in_generated, random_image = ui_main.show(is_img2img=False, show_br=False, **msgs)
label="Vignette fallback threshold",
info="Switch to a rectangular vignette mask when masked area is only this specified percentage of Face Size."
)
with gr.Tab("Settings"): # TAB DETECTION
models = get_models() det_thresh, det_maxnum = ui_detection.show()
with gr.Row(visible=EP_is_visible):
device = gr.Radio( # TAB UPSCALE
label="Execution Provider", restore_first, upscaler_name, upscaler_scale, upscaler_visibility, upscale_force = ui_upscale.show(show_br=False)
choices=DEVICE_LIST,
value=DEVICE, # TAB MASKING
type="value", save_face_mask, use_minimal_area, mask_areas, face_size, mask_blur, mask_vignette_fallback_threshold = ui_masking.show()
info="If you already run 'Generate' - RESTART is required to apply. Click 'Save', (A1111) Extensions Tab -> 'Apply and restart UI' or (SD.Next) close the Server and start it again",
scale=2, # TAB TOOLS
) ui_tools.show()
save_device_btn = gr.Button("Save", scale=0)
save = gr.Markdown("", visible=EP_is_visible) # TAB SETTINGS
setattr(device, "do_not_save_to_config", True) model, device, console_logging_level, source_hash_check, target_hash_check = ui_settings.show(hash_check_block=False)
save_device_btn.click(
set_Device, gr.Markdown("<span style='display:block;text-align:right;padding-right:3px;font-size:0.666em;margin: -9px 0'>by <a style='font-weight:normal' href='https://github.com/Gourieff' target='_blank'>Eugene Gourieff</a></span>")
inputs=[device],
outputs=[save],
)
with gr.Row():
if len(models) == 0:
logger.warning(
"You should at least have one model in models directory, please read the doc here : https://github.com/Gourieff/sd-webui-reactor/"
)
model = gr.Dropdown(
choices=models,
label="Model not found, please download one and reload WebUI",
)
else:
model = gr.Dropdown(
choices=models, label="Model", value=models[0]
)
console_logging_level = gr.Radio(
["No log", "Minimum", "Default"],
value="Minimum",
label="Console Log Level",
type="index",
)
args = { args = {
'img': img, 'img': img,
@ -777,13 +584,19 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
'codeformer_weight': codeformer_weight, 'codeformer_weight': codeformer_weight,
'device': device, 'device': device,
'mask_face': mask_face, 'mask_face': mask_face,
'select_source': select_source,
'face_model': face_model,
'source_folder': source_folder,
'imgs': imgs,
'random_image': random_image,
'upscale_force': upscale_force,
'det_thresh': det_thresh,
'det_maxnum': det_maxnum,
'mask_areas':mask_areas, 'mask_areas':mask_areas,
'mask_blur':mask_blur, 'mask_blur':mask_blur,
'mask_vignette_fallback_threshold':mask_vignette_fallback_threshold, 'mask_vignette_fallback_threshold':mask_vignette_fallback_threshold,
'face_size':face_size, 'face_size':face_size,
'use_minimal_area':use_minimal_area, 'use_minimal_area':use_minimal_area,
} }
return args return args
@ -811,6 +624,14 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
upscale_visibility=self.upscaler_visibility, upscale_visibility=self.upscaler_visibility,
restorer_visibility=self.face_restorer_visibility, restorer_visibility=self.face_restorer_visibility,
codeformer_weight=self.codeformer_weight, codeformer_weight=self.codeformer_weight,
upscale_force=self.upscale_force,
)
@property
def detection_options(self) -> DetectionOptions:
return DetectionOptions(
det_thresh=self.det_thresh,
det_maxnum=self.det_maxnum
) )
@property @property
def mask_options(self) -> MaskOptions: def mask_options(self) -> MaskOptions:
@ -827,8 +648,8 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
reset_messaged() reset_messaged()
if check_process_halt(): if check_process_halt():
return return
global MODELS_PATH
global SWAPPER_MODELS_PATH
self.source = args['img'] self.source = args['img']
self.face_restorer_name = args['face_restorer_name'] self.face_restorer_name = args['face_restorer_name']
self.upscaler_scale = args['upscaler_scale'] self.upscaler_scale = args['upscaler_scale']
@ -836,20 +657,27 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
self.face_restorer_visibility = args['face_restorer_visibility'] self.face_restorer_visibility = args['face_restorer_visibility']
self.restore_first = args['restore_first'] self.restore_first = args['restore_first']
self.upscaler_name = args['upscaler_name'] self.upscaler_name = args['upscaler_name']
self.model = os.path.join(MODELS_PATH, args['model']) self.model = os.path.join(SWAPPER_MODELS_PATH, args['model'])
self.console_logging_level = args['console_logging_level'] self.console_logging_level = args['console_logging_level']
self.gender_source = args['gender_source'] self.gender_source = args['gender_source']
self.gender_target = args['gender_target'] self.gender_target = args['gender_target']
self.codeformer_weight = args['codeformer_weight'] self.codeformer_weight = args['codeformer_weight']
self.device = args['device'] self.device = args['device']
self.mask_face = args['mask_face'] self.mask_face = args['mask_face']
self.select_source = args['select_source']
self.face_model = args['face_model']
self.source_folder = args['source_folder']
self.source_imgs = args['imgs']
self.random_image = args['random_image']
self.upscale_force = args['upscale_force']
self.det_thresh = args['det_thresh']
self.det_maxnum = args['det_maxnum']
self.save_face_mask = None self.save_face_mask = None
self.mask_areas= args['mask_areas'] self.mask_areas= args['mask_areas']
self.mask_blur= args['mask_blur'] self.mask_blur= args['mask_blur']
self.mask_vignette_fallback_threshold= args['mask_vignette_fallback_threshold'] self.mask_vignette_fallback_threshold= args['mask_vignette_fallback_threshold']
self.face_size= args['face_size'] self.face_size= args['face_size']
self.use_minimal_area= args['use_minimal_area'] self.use_minimal_area= args['use_minimal_area']
self.face_files = None
if self.gender_source is None or self.gender_source == "No": if self.gender_source is None or self.gender_source == "No":
self.gender_source = 0 self.gender_source = 0
if self.gender_target is None or self.gender_target == "No": if self.gender_target is None or self.gender_target == "No":
@ -864,6 +692,12 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
self.source_faces_index = [0] self.source_faces_index = [0]
if len(self.faces_index) == 0: if len(self.faces_index) == 0:
self.faces_index = [0] self.faces_index = [0]
if self.mask_face is None:
self.mask_face = False
if self.random_image is None:
self.random_image = False
if self.upscale_force is None:
self.upscale_force = False
current_job_number = shared.state.job_no + 1 current_job_number = shared.state.job_no + 1
job_count = shared.state.job_count job_count = shared.state.job_count
@ -871,12 +705,30 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
reset_messaged() reset_messaged()
set_Device(self.device) set_Device(self.device)
apply_logging_patch(self.console_logging_level)
if self.source is not None:
logger.debug("We're here: process() 1")
if (self.source is not None and self.select_source == 0) or ((self.face_model is not None and self.face_model != "None") and self.select_source == 1) or ((self.source_folder is not None and self.source_folder != "") and self.select_source == 2) or ((self.source_imgs is not None and self.source is None) and self.select_source == 0):
logger.debug("We're here: process() 2")
if self.source is not None and self.select_source == 0:
self.source_imgs = None
apply_logging_patch(self.console_logging_level)
logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index) logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
# if self.select_source != 2:
image: Image.Image = pp.image image: Image.Image = pp.image
result, output, swapped,masked_faces = swap_face(
# Extract alpha channel
logger.debug(f"image = {image}")
if image.mode == 'RGBA':
_, _, _, alpha = image.split()
else:
alpha = None
logger.debug(f"alpha = {alpha}")
result, output, swapped = swap_face(
self.source, self.source,
image, image,
source_faces_index=self.source_faces_index, source_faces_index=self.source_faces_index,
@ -889,10 +741,36 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
target_hash_check=True, target_hash_check=True,
device=self.device, device=self.device,
mask_face=self.mask_face, mask_face=self.mask_face,
mask_options=self.mask_options select_source=self.select_source,
face_model=self.face_model,
source_folder=self.source_folder,
source_imgs=self.source_imgs,
random_image=self.random_image,
detection_options=self.detection_options,
mask_options=self.mask_options,
) )
if self.select_source == 2 or (self.select_source == 0 and self.source_imgs is not None and self.source is None):
if len(result) > 0 and swapped > 0:
image = result[0]
if len(result) > 1:
grid = make_grid(result)
result.insert(0, grid)
image = grid
pp.info["ReActor"] = True
pp.image = image
logger.status("---Done!---")
else:
logger.error("Cannot create a result image")
else:
try: try:
pp.info["ReActor"] = True pp.info["ReActor"] = True
if alpha is not None:
logger.debug(f"result = {result}")
result = result.convert("RGBA")
result.putalpha(alpha)
logger.debug(f"result_alpha = {result}")
pp.image = result pp.image = result
logger.status("---Done!---") logger.status("---Done!---")
except Exception: except Exception:

32
scripts/reactor_globals.py
View File

@ -1,18 +1,42 @@
import os import os
from pathlib import Path from pathlib import Path
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
models_path = os.path.abspath("models")
IS_RUN: bool = False IS_RUN: bool = False
BASE_PATH = os.path.join(Path(__file__).parents[1]) BASE_PATH = os.path.join(Path(__file__).parents[1])
DEVICE_LIST: list = ["CPU", "CUDA"] DEVICE_LIST: list = ["CPU", "CUDA"]
MODELS_PATH = models_path
SWAPPER_MODELS_PATH = os.path.join(MODELS_PATH, "insightface")
REACTOR_MODELS_PATH = os.path.join(MODELS_PATH, "reactor")
FACE_MODELS_PATH = os.path.join(REACTOR_MODELS_PATH, "faces")
IS_SDNEXT = False
if not os.path.exists(REACTOR_MODELS_PATH):
os.makedirs(REACTOR_MODELS_PATH)
if not os.path.exists(FACE_MODELS_PATH):
os.makedirs(FACE_MODELS_PATH)
def updateDevice(): def updateDevice():
try: try:
LAST_DEVICE_PATH = os.path.join(BASE_PATH, "last_device.txt") LAST_DEVICE_PATH = os.path.join(BASE_PATH, "last_device.txt")
with open(LAST_DEVICE_PATH) as f: with open(LAST_DEVICE_PATH) as f:
for el in f: device = f.readline().strip()
device = el.strip() if device not in DEVICE_LIST:
except: print(f"Error: Device {device} is not in DEVICE_LIST")
device = "CPU" device = DEVICE_LIST[0]
print(f"Execution Provider has been set to {device}")
except Exception as e:
device = DEVICE_LIST[0]
print(f"Error: {e}\nExecution Provider has been set to {device}")
return device return device
DEVICE = updateDevice() DEVICE = updateDevice()

104
scripts/reactor_helpers.py
View File

@ -1,14 +1,27 @@
import os import os, glob, random
from collections import Counter from collections import Counter
from PIL import Image from PIL import Image
from math import isqrt, ceil from math import isqrt, ceil
from typing import List from typing import List
import logging import logging
import hashlib import hashlib
import torch
from safetensors.torch import save_file, safe_open
from insightface.app.common import Face
from modules.images import FilenameGenerator, get_next_sequence_number from modules.images import FilenameGenerator, get_next_sequence_number
from modules import shared, script_callbacks from modules import shared, script_callbacks
from scripts.reactor_globals import DEVICE, BASE_PATH from scripts.reactor_globals import DEVICE, BASE_PATH, FACE_MODELS_PATH, IS_SDNEXT
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
model_path = os.path.abspath("models")
MODELS_PATH = None
def set_Device(value): def set_Device(value):
global DEVICE global DEVICE
@ -20,6 +33,14 @@ def get_Device():
global DEVICE global DEVICE
return DEVICE return DEVICE
def set_SDNEXT():
global IS_SDNEXT
IS_SDNEXT = True
def get_SDNEXT():
global IS_SDNEXT
return IS_SDNEXT
def make_grid(image_list: List): def make_grid(image_list: List):
# Count the occurrences of each image size in the image_list # Count the occurrences of each image size in the image_list
@ -133,3 +154,82 @@ def addLoggingLevel(levelName, levelNum, methodName=None):
def get_image_md5hash(image: Image.Image): def get_image_md5hash(image: Image.Image):
md5hash = hashlib.md5(image.tobytes()) md5hash = hashlib.md5(image.tobytes())
return md5hash.hexdigest() return md5hash.hexdigest()
def save_face_model(face: Face, filename: str) -> None:
try:
tensors = {
"bbox": torch.tensor(face["bbox"]),
"kps": torch.tensor(face["kps"]),
"det_score": torch.tensor(face["det_score"]),
"landmark_3d_68": torch.tensor(face["landmark_3d_68"]),
"pose": torch.tensor(face["pose"]),
"landmark_2d_106": torch.tensor(face["landmark_2d_106"]),
"embedding": torch.tensor(face["embedding"]),
"gender": torch.tensor(face["gender"]),
"age": torch.tensor(face["age"]),
}
save_file(tensors, filename)
# print(f"Face model has been saved to '{filename}'")
except Exception as e:
print(f"Error: {e}")
def get_models():
global MODELS_PATH
models_path_init = os.path.join(models_path, "insightface/*")
models = glob.glob(models_path_init)
models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
models_names = []
for model in models:
model_path = os.path.split(model)
if MODELS_PATH is None:
MODELS_PATH = model_path[0]
model_name = model_path[1]
models_names.append(model_name)
return models_names
def load_face_model(filename: str):
face = {}
model_path = os.path.join(FACE_MODELS_PATH, filename)
with safe_open(model_path, framework="pt") as f:
for k in f.keys():
face[k] = f.get_tensor(k).numpy()
return Face(face)
def get_facemodels():
models_path = os.path.join(FACE_MODELS_PATH, "*")
models = glob.glob(models_path)
models = [x for x in models if x.endswith(".safetensors")]
return models
def get_model_names(get_models):
models = get_models()
names = ["None"]
for x in models:
names.append(os.path.basename(x))
return names
def get_images_from_folder(path: str):
files_path = os.path.join(path, "*")
files = glob.glob(files_path)
images = []
images_names = []
for x in files:
if x.endswith(('jpg', 'png', 'jpeg', 'webp', 'bmp')):
images.append(Image.open(x))
images_names.append(os.path.basename(x))
return images,images_names
# return [Image.open(x) for x in images if x.endswith(('jpg', 'png', 'jpeg', 'webp', 'bmp'))],[os.path.basename(x) for x in images if x.endswith(('jpg', 'png', 'jpeg', 'webp', 'bmp'))]
def get_random_image_from_folder(path: str):
images,names = get_images_from_folder(path)
random_image_index = random.randint(0, len(images) - 1)
return [images[random_image_index]],[names[random_image_index]]
def get_images_from_list(imgs: List):
images = []
images_names = []
for x in imgs:
images.append(Image.open(os.path.abspath(x.name)))
images_names.append(os.path.basename(x.name))
return images,images_names
# return [Image.open(os.path.abspath(x.name)) for x in imgs],[os.path.basename(x.name) for x in imgs]

12
scripts/inferencers/bisenet_mask_generator.py → scripts/reactor_inferencers/bisenet_mask_generator.py
View File

@ -9,9 +9,8 @@ from facexlib.parsing import init_parsing_model
from facexlib.utils.misc import img2tensor from facexlib.utils.misc import img2tensor
from torchvision.transforms.functional import normalize from torchvision.transforms.functional import normalize
from PIL import Image from PIL import Image
from scripts.inferencers.vignette_mask_generator import VignetteMaskGenerator from scripts.reactor_inferencers.vignette_mask_generator import VignetteMaskGenerator
from scripts.inferencers.mask_generator import MaskGenerator from scripts.reactor_inferencers.mask_generator import MaskGenerator
class BiSeNetMaskGenerator(MaskGenerator): class BiSeNetMaskGenerator(MaskGenerator):
def __init__(self) -> None: def __init__(self) -> None:
@ -31,7 +30,7 @@ class BiSeNetMaskGenerator(MaskGenerator):
fallback_ratio: float = 0.10, fallback_ratio: float = 0.10,
**kwargs, **kwargs,
) -> np.ndarray: ) -> np.ndarray:
original_face_image = face_image # original_face_image = face_image
face_image = face_image.copy() face_image = face_image.copy()
face_image = face_image[:, :, ::-1] face_image = face_image[:, :, ::-1]
@ -67,6 +66,11 @@ class BiSeNetMaskGenerator(MaskGenerator):
mask = self.fallback_mask_generator.generate_mask( mask = self.fallback_mask_generator.generate_mask(
original_face_image, face_area_on_image, use_minimal_area=True original_face_image, face_area_on_image, use_minimal_area=True
) )
# """if MaskGenerator.calculate_mask_coverage(mask) < fallback_ratio:
# logger.info("Use fallback mask generator")
# mask = self.fallback_mask_generator.generate_mask(
# original_face_image, face_area_on_image, use_minimal_area=True
# )"""
return mask return mask

1
scripts/inferencers/mask_generator.py → scripts/reactor_inferencers/mask_generator.py
View File

@ -4,7 +4,6 @@ from typing import Tuple
import cv2 import cv2
import numpy as np import numpy as np
class MaskGenerator(ABC): class MaskGenerator(ABC):
@abstractmethod @abstractmethod
def name(self) -> str: def name(self) -> str:

2
scripts/inferencers/vignette_mask_generator.py → scripts/reactor_inferencers/vignette_mask_generator.py
View File

@ -3,7 +3,7 @@ from typing import Tuple
import cv2 import cv2
import numpy as np import numpy as np
from scripts.inferencers.mask_generator import MaskGenerator from scripts.reactor_inferencers.mask_generator import MaskGenerator
class VignetteMaskGenerator(MaskGenerator): class VignetteMaskGenerator(MaskGenerator):

449
scripts/reactor_swapper.py
View File

@ -5,22 +5,35 @@ from typing import List, Tuple, Union
import cv2 import cv2
import numpy as np import numpy as np
from numpy import uint8 from PIL import Image
from PIL import Image, ImageDraw from scipy import stats
from scripts.inferencers.bisenet_mask_generator import BiSeNetMaskGenerator
from scripts.entities.face import Face
from scripts.entities.rect import Rect
import insightface import insightface
from torchvision.transforms.functional import to_pil_image from insightface.app.common import Face
from scripts.reactor_helpers import get_image_md5hash, get_Device
from scripts.reactor_globals import FACE_MODELS_PATH
from scripts.reactor_helpers import (
get_image_md5hash,
get_Device,
save_face_model,
load_face_model,
get_images_from_folder,
get_random_image_from_folder,
get_images_from_list,
set_SDNEXT
)
from scripts.console_log_patch import apply_logging_patch
from modules.face_restoration import FaceRestoration from modules.face_restoration import FaceRestoration
try: # A1111 try: # A1111
from modules import codeformer_model from modules import codeformer_model, gfpgan_model
except: # SD.Next except: # SD.Next
from modules.postprocess import codeformer_model from modules.postprocess import codeformer_model, gfpgan_model
set_SDNEXT()
from modules.upscaler import UpscalerData from modules.upscaler import UpscalerData
from modules.shared import state from modules.shared import state
from scripts.reactor_logger import logger from scripts.reactor_logger import logger
from reactor_modules.reactor_mask import apply_face_mask
try: try:
from modules.paths_internal import models_path from modules.paths_internal import models_path
@ -28,7 +41,7 @@ except:
try: try:
from modules.paths import models_path from modules.paths import models_path
except: except:
model_path = os.path.abspath("models") models_path = os.path.abspath("models")
import warnings import warnings
@ -57,7 +70,12 @@ class EnhancementOptions:
face_restorer: FaceRestoration = None face_restorer: FaceRestoration = None
restorer_visibility: float = 0.5 restorer_visibility: float = 0.5
codeformer_weight: float = 0.5 codeformer_weight: float = 0.5
upscale_force: bool = False
@dataclass
class DetectionOptions:
det_thresh: float = 0.5
det_maxnum: int = 0
@dataclass @dataclass
class MaskOptions: class MaskOptions:
@ -94,16 +112,46 @@ def check_process_halt(msgforced: bool = False):
FS_MODEL = None FS_MODEL = None
ANALYSIS_MODEL = None
MASK_MODEL = None MASK_MODEL = None
CURRENT_FS_MODEL_PATH = None CURRENT_FS_MODEL_PATH = None
CURRENT_MASK_MODEL_PATH = None CURRENT_MASK_MODEL_PATH = None
ANALYSIS_MODEL = None
SOURCE_FACES = None SOURCE_FACES = None
SOURCE_IMAGE_HASH = None SOURCE_IMAGE_HASH = None
TARGET_FACES = None TARGET_FACES = None
TARGET_IMAGE_HASH = None TARGET_IMAGE_HASH = None
SOURCE_FACES_LIST = []
SOURCE_IMAGE_LIST_HASH = []
def clear_faces():
global SOURCE_FACES, SOURCE_IMAGE_HASH
SOURCE_FACES = None
SOURCE_IMAGE_HASH = None
logger.status("Source Images Hash has been reset (for Single Source or Face Model)")
def clear_faces_list():
global SOURCE_FACES_LIST, SOURCE_IMAGE_LIST_HASH
SOURCE_FACES_LIST = []
SOURCE_IMAGE_LIST_HASH = []
logger.status("Source Images Hash has been reset (for Multiple or Folder Source)")
def clear_faces_target():
global TARGET_FACES, TARGET_IMAGE_HASH
TARGET_FACES = None
TARGET_IMAGE_HASH = None
logger.status("Target Images Hash has been reset")
def clear_faces_all():
global SOURCE_FACES, SOURCE_IMAGE_HASH, SOURCE_FACES_LIST, SOURCE_IMAGE_LIST_HASH, TARGET_FACES, TARGET_IMAGE_HASH
SOURCE_FACES = None
SOURCE_IMAGE_HASH = None
TARGET_FACES = None
TARGET_IMAGE_HASH = None
SOURCE_FACES_LIST = []
SOURCE_IMAGE_LIST_HASH = []
logger.status("All Images Hash has been reset")
def getAnalysisModel(): def getAnalysisModel():
global ANALYSIS_MODEL global ANALYSIS_MODEL
@ -124,8 +172,6 @@ def getFaceSwapModel(model_path: str):
return FS_MODEL return FS_MODEL
def restore_face(image: Image, enhancement_options: EnhancementOptions): def restore_face(image: Image, enhancement_options: EnhancementOptions):
result_image = image result_image = image
@ -134,14 +180,16 @@ def restore_face(image: Image, enhancement_options: EnhancementOptions):
if enhancement_options.face_restorer is not None: if enhancement_options.face_restorer is not None:
original_image = result_image.copy() original_image = result_image.copy()
logger.status("Restoring the face with %s", enhancement_options.face_restorer.name())
numpy_image = np.array(result_image) numpy_image = np.array(result_image)
if enhancement_options.face_restorer.name() == "CodeFormer": if enhancement_options.face_restorer.name() == "CodeFormer":
logger.status("Restoring the face with %s (weight: %s)", enhancement_options.face_restorer.name(), enhancement_options.codeformer_weight)
numpy_image = codeformer_model.codeformer.restore( numpy_image = codeformer_model.codeformer.restore(
numpy_image, w=enhancement_options.codeformer_weight numpy_image, w=enhancement_options.codeformer_weight
) )
else: else: # GFPGAN:
numpy_image = enhancement_options.face_restorer.restore(numpy_image) logger.status("Restoring the face with %s", enhancement_options.face_restorer.name())
numpy_image = gfpgan_model.gfpgan_fix_faces(numpy_image)
# numpy_image = enhancement_options.face_restorer.restore(numpy_image)
restored_image = Image.fromarray(numpy_image) restored_image = Image.fromarray(numpy_image)
result_image = Image.blend( result_image = Image.blend(
original_image, restored_image, enhancement_options.restorer_visibility original_image, restored_image, enhancement_options.restorer_visibility
@ -269,13 +317,13 @@ def half_det_size(det_size):
logger.status("Trying to halve 'det_size' parameter") logger.status("Trying to halve 'det_size' parameter")
return (det_size[0] // 2, det_size[1] // 2) return (det_size[0] // 2, det_size[1] // 2)
def analyze_faces(img_data: np.ndarray, det_size=(640, 640)): def analyze_faces(img_data: np.ndarray, det_size=(640, 640), det_thresh=0.5, det_maxnum=0):
logger.info("Applied Execution Provider: %s", PROVIDERS[0]) logger.info("Applied Execution Provider: %s", PROVIDERS[0])
face_analyser = copy.deepcopy(getAnalysisModel()) face_analyser = copy.deepcopy(getAnalysisModel())
face_analyser.prepare(ctx_id=0, det_size=det_size) face_analyser.prepare(ctx_id=0, det_thresh=det_thresh, det_size=det_size)
return face_analyser.get(img_data) return face_analyser.get(img_data, max_num=det_maxnum)
def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0): def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0, det_thresh=0.5, det_maxnum=0):
buffalo_path = os.path.join(models_path, "insightface/models/buffalo_l.zip") buffalo_path = os.path.join(models_path, "insightface/models/buffalo_l.zip")
if os.path.exists(buffalo_path): if os.path.exists(buffalo_path):
@ -298,20 +346,20 @@ def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640
if gender_source != 0: if gender_source != 0:
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320: if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size) det_size_half = half_det_size(det_size)
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target) return get_face_single(img_data, analyze_faces(img_data, det_size_half, det_thresh, det_maxnum), face_index, det_size_half, gender_source, gender_target, det_thresh, det_maxnum)
faces, wrong_gender = get_face_gender(face,face_index,gender_source,"Source",gender_detected) faces, wrong_gender = get_face_gender(face,face_index,gender_source,"Source",gender_detected)
return faces, wrong_gender, face_age, face_gender return faces, wrong_gender, face_age, face_gender
if gender_target != 0: if gender_target != 0:
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320: if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size) det_size_half = half_det_size(det_size)
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target) return get_face_single(img_data, analyze_faces(img_data, det_size_half, det_thresh, det_maxnum), face_index, det_size_half, gender_source, gender_target, det_thresh, det_maxnum)
faces, wrong_gender = get_face_gender(face,face_index,gender_target,"Target",gender_detected) faces, wrong_gender = get_face_gender(face,face_index,gender_target,"Target",gender_detected)
return faces, wrong_gender, face_age, face_gender return faces, wrong_gender, face_age, face_gender
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320: if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size) det_size_half = half_det_size(det_size)
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target) return get_face_single(img_data, analyze_faces(img_data, det_size_half, det_thresh, det_maxnum), face_index, det_size_half, gender_source, gender_target, det_thresh, det_maxnum)
try: try:
return sorted(face, key=lambda x: x.bbox[0])[face_index], 0, face_age, face_gender return sorted(face, key=lambda x: x.bbox[0])[face_index], 0, face_age, face_gender
@ -332,9 +380,16 @@ def swap_face(
target_hash_check: bool = False, target_hash_check: bool = False,
device: str = "CPU", device: str = "CPU",
mask_face: bool = False, mask_face: bool = False,
mask_options:Union[MaskOptions, None]= None select_source: int = 0,
face_model: str = "None",
source_folder: str = "",
source_imgs: Union[List, None] = None,
random_image: bool = False,
detection_options: Union[DetectionOptions, None] = None,
mask_options:Union[MaskOptions, None]= None,
): ):
global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS, SOURCE_FACES_LIST, SOURCE_IMAGE_LIST_HASH
result_image = target_img result_image = target_img
masked_faces = None masked_faces = None
PROVIDERS = ["CUDAExecutionProvider"] if device == "CUDA" else ["CPUExecutionProvider"] PROVIDERS = ["CUDAExecutionProvider"] if device == "CUDA" else ["CPUExecutionProvider"]
@ -359,14 +414,139 @@ def swap_face(
source_img = Image.open(io.BytesIO(img_bytes)) source_img = Image.open(io.BytesIO(img_bytes))
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR) target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
target_img_orig = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR) target_img_orig = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
entire_mask_image = np.zeros_like(np.array(target_img)) entire_mask_image = np.zeros_like(np.array(target_img))
output: List = [] output: List = []
output_info: str = "" output_info: str = ""
swapped = 0 swapped = 0
# *****************
# SWAP from FOLDER or MULTIPLE images:
if (select_source == 0 and source_imgs is not None) or (select_source == 2 and (source_folder is not None and source_folder != "")):
result = []
if random_image and select_source == 2:
source_images,source_images_names = get_random_image_from_folder(source_folder)
logger.status(f"Processing with Random Image from the folder: {source_images_names[0]}")
else:
source_images,source_images_names = get_images_from_folder(source_folder) if select_source == 2 else get_images_from_list(source_imgs)
if len(source_images) > 0:
source_img_ff = []
source_faces_ff = []
for i, source_image in enumerate(source_images):
source_image = cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)
source_img_ff.append(source_image)
if source_hash_check:
source_image_md5hash = get_image_md5hash(source_image)
if len(SOURCE_IMAGE_LIST_HASH) == 0:
SOURCE_IMAGE_LIST_HASH = [source_image_md5hash]
source_image_same = False
elif len(SOURCE_IMAGE_LIST_HASH) == i:
SOURCE_IMAGE_LIST_HASH.append(source_image_md5hash)
source_image_same = False
else:
source_image_same = True if SOURCE_IMAGE_LIST_HASH[i] == source_image_md5hash else False
if not source_image_same:
SOURCE_IMAGE_LIST_HASH[i] = source_image_md5hash
logger.info("(Image %s) Source Image MD5 Hash = %s", i, SOURCE_IMAGE_LIST_HASH[i])
logger.info("(Image %s) Source Image the Same? %s", i, source_image_same)
if len(SOURCE_FACES_LIST) == 0:
logger.status(f"Analyzing Source Image {i}: {source_images_names[i]}...")
source_faces = analyze_faces(source_image, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
SOURCE_FACES_LIST = [source_faces]
elif len(SOURCE_FACES_LIST) == i and not source_image_same:
logger.status(f"Analyzing Source Image {i}: {source_images_names[i]}...")
source_faces = analyze_faces(source_image, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
SOURCE_FACES_LIST.append(source_faces)
elif len(SOURCE_FACES_LIST) != i and not source_image_same:
logger.status(f"Analyzing Source Image {i}: {source_images_names[i]}...")
source_faces = analyze_faces(source_image, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
SOURCE_FACES_LIST[i] = source_faces
elif source_image_same:
logger.status("(Image %s) Using Hashed Source Face(s) Model...", i)
source_faces = SOURCE_FACES_LIST[i]
else:
logger.status(f"Analyzing Source Image {i}...")
source_faces = analyze_faces(source_image, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
if source_faces is not None:
source_faces_ff.append(source_faces)
if len(source_faces_ff) > 0:
if target_hash_check:
target_image_md5hash = get_image_md5hash(target_img)
if TARGET_IMAGE_HASH is None:
TARGET_IMAGE_HASH = target_image_md5hash
target_image_same = False
else:
target_image_same = True if TARGET_IMAGE_HASH == target_image_md5hash else False
if not target_image_same:
TARGET_IMAGE_HASH = target_image_md5hash
logger.info("Target Image MD5 Hash = %s", TARGET_IMAGE_HASH)
logger.info("Target Image the Same? %s", target_image_same)
if TARGET_FACES is None or not target_image_same:
logger.status("Analyzing Target Image...")
target_faces = analyze_faces(target_img, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
TARGET_FACES = target_faces
elif target_image_same:
logger.status("Using Hashed Target Face(s) Model...")
target_faces = TARGET_FACES
else:
logger.status("Analyzing Target Image...")
target_faces = analyze_faces(target_img, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
for i,source_faces in enumerate(source_faces_ff):
logger.status("(Image %s) Detecting Source Face, Index = %s", i, source_faces_index[0])
source_face, wrong_gender, source_age, source_gender = get_face_single(source_img_ff[i], source_faces, face_index=source_faces_index[0], gender_source=gender_source, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
if source_age != "None" or source_gender != "None":
logger.status("(Image %s) Detected: -%s- y.o. %s", i, source_age, source_gender)
if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
logger.status("Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.")
elif source_face is not None:
result_image, output, swapped = operate(source_img_ff[i],target_img,target_img_orig,model,source_faces_index,faces_index,source_faces,target_faces,gender_source,gender_target,source_face,wrong_gender,source_age,source_gender,output,swapped,mask_face,entire_mask_image,enhancement_options,detection_options)
result.append(result_image)
result = [result_image] if len(result) == 0 else result
return result, output, swapped
# END
# *****************
# ***********************
# SWAP from IMG or MODEL:
else:
if select_source == 0 and source_img is not None:
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
if source_hash_check: if source_hash_check:
source_image_md5hash = get_image_md5hash(source_img) source_image_md5hash = get_image_md5hash(source_img)
@ -384,15 +564,28 @@ def swap_face(
if SOURCE_FACES is None or not source_image_same: if SOURCE_FACES is None or not source_image_same:
logger.status("Analyzing Source Image...") logger.status("Analyzing Source Image...")
source_faces = analyze_faces(source_img) source_faces = analyze_faces(source_img, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
SOURCE_FACES = source_faces SOURCE_FACES = source_faces
elif source_image_same: elif source_image_same:
logger.status("Using Ready Source Face(s) Model...") logger.status("Using Hashed Source Face(s) Model...")
source_faces = SOURCE_FACES source_faces = SOURCE_FACES
else: else:
logger.status("Analyzing Source Image...") logger.status("Analyzing Source Image...")
source_faces = analyze_faces(source_img) source_faces = analyze_faces(source_img, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
elif select_source == 1 and (face_model is not None and face_model != "None"):
source_face_model = [load_face_model(face_model)]
if source_face_model is not None:
source_faces_index = [0]
source_faces = source_face_model
logger.status(f"Using Loaded Source Face Model: {face_model}")
else:
logger.error(f"Cannot load Face Model File: {face_model}")
else:
logger.error("Cannot detect any Source")
return result_image, [], 0
if source_faces is not None: if source_faces is not None:
@ -413,18 +606,25 @@ def swap_face(
if TARGET_FACES is None or not target_image_same: if TARGET_FACES is None or not target_image_same:
logger.status("Analyzing Target Image...") logger.status("Analyzing Target Image...")
target_faces = analyze_faces(target_img) target_faces = analyze_faces(target_img, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
TARGET_FACES = target_faces TARGET_FACES = target_faces
elif target_image_same: elif target_image_same:
logger.status("Using Ready Target Face(s) Model...") logger.status("Using Hashed Target Face(s) Model...")
target_faces = TARGET_FACES target_faces = TARGET_FACES
else: else:
logger.status("Analyzing Target Image...") logger.status("Analyzing Target Image...")
target_faces = analyze_faces(target_img) target_faces = analyze_faces(target_img, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
logger.status("Detecting Source Face, Index = %s", source_faces_index[0]) logger.status("Detecting Source Face, Index = %s", source_faces_index[0])
source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source) if select_source == 0 and source_img is not None:
source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
else:
source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
wrong_gender = 0
source_age = source_face["age"]
source_gender = "Female" if source_face["gender"] == 0 else "Male"
if source_age != "None" or source_gender != "None": if source_age != "None" or source_gender != "None":
logger.status("Detected: -%s- y.o. %s", source_age, source_gender) logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
@ -433,8 +633,133 @@ def swap_face(
if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index): if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
logger.status("Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.") logger.status("Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.")
elif source_face is not None: elif source_face is not None:
result_image, output, swapped = operate(source_img,target_img,target_img_orig,model,source_faces_index,faces_index,source_faces,target_faces,gender_source,gender_target,source_face,wrong_gender,source_age,source_gender,output,swapped,mask_face,entire_mask_image,enhancement_options,detection_options)
else:
logger.status("No source face(s) in the provided Index")
else:
logger.status("No source face(s) found")
return result_image, output, swapped
# END
# **********************
return result_image, [], 0
def build_face_model(image: Image.Image, name: str, save_model: bool = True, det_size=(640, 640)):
if image is None:
error_msg = "Please load an Image"
logger.error(error_msg)
return error_msg
if name is None:
error_msg = "Please filled out the 'Face Model Name' field"
logger.error(error_msg)
return error_msg
apply_logging_patch(1)
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
if save_model:
logger.status("Building Face Model...")
face_model = analyze_faces(image, det_size)
if len(face_model) == 0:
det_size_half = half_det_size(det_size)
face_model = analyze_faces(image, det_size_half)
if face_model is not None and len(face_model) > 0:
if save_model:
face_model_path = os.path.join(FACE_MODELS_PATH, name + ".safetensors")
save_face_model(face_model[0],face_model_path)
logger.status("--Done!--")
done_msg = f"Face model has been saved to '{face_model_path}'"
logger.status(done_msg)
return done_msg
else:
return face_model[0]
else:
no_face_msg = "No face found, please try another image"
logger.error(no_face_msg)
return no_face_msg
def blend_faces(images_list: List, name: str, compute_method: int = 0, shape_check: bool = False):
faces = []
embeddings = []
images: List[Image.Image] = []
images, images_names = get_images_from_list(images_list)
for i,image in enumerate(images):
logger.status(f"Building Face Model for {images_names[i]}...")
face = build_face_model(image,str(i),save_model=False)
if isinstance(face, str):
# logger.error(f"No faces found in {images_names[i]}, skipping")
continue
if shape_check:
if i == 0:
embedding_shape = face.embedding.shape
elif face.embedding.shape != embedding_shape:
logger.error(f"Embedding Shape Mismatch for {images_names[i]}, skipping")
continue
faces.append(face)
embeddings.append(face.embedding)
if len(faces) > 0:
# if shape_check:
# embedding_shape = embeddings[0].shape
# for embedding in embeddings:
# if embedding.shape != embedding_shape:
# logger.error("Embedding Shape Mismatch")
# break
compute_method_name = "Mean" if compute_method == 0 else "Median" if compute_method == 1 else "Mode"
logger.status(f"Blending with Compute Method {compute_method_name}...")
blended_embedding = np.mean(embeddings, axis=0) if compute_method == 0 else np.median(embeddings, axis=0) if compute_method == 1 else stats.mode(embeddings, axis=0)[0].astype(np.float32)
blended_face = Face(
bbox=faces[0].bbox,
kps=faces[0].kps,
det_score=faces[0].det_score,
landmark_3d_68=faces[0].landmark_3d_68,
pose=faces[0].pose,
landmark_2d_106=faces[0].landmark_2d_106,
embedding=blended_embedding,
gender=faces[0].gender,
age=faces[0].age
)
if blended_face is not None:
face_model_path = os.path.join(FACE_MODELS_PATH, name + ".safetensors")
save_face_model(blended_face,face_model_path)
logger.status("--Done!--")
done_msg = f"Face model has been saved to '{face_model_path}'"
logger.status(done_msg)
return done_msg
else:
no_face_msg = "Something went wrong, please try another set of images"
logger.error(no_face_msg)
return no_face_msg
return "No faces found"
def operate(
source_img,
target_img,
target_img_orig,
model,
source_faces_index,
faces_index,
source_faces,
target_faces,
gender_source,
gender_target,
source_face,
wrong_gender,
source_age,
source_gender,
output,
swapped,
mask_face,
entire_mask_image,
enhancement_options,
detection_options,
):
result = target_img result = target_img
face_swapper = getFaceSwapModel(model) face_swapper = getFaceSwapModel(model)
@ -445,7 +770,7 @@ def swap_face(
return result_image, [], 0 return result_image, [], 0
if len(source_faces_index) > 1 and source_face_idx > 0: if len(source_faces_index) > 1 and source_face_idx > 0:
logger.status("Detecting Source Face, Index = %s", source_faces_index[source_face_idx]) logger.status("Detecting Source Face, Index = %s", source_faces_index[source_face_idx])
source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source) source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
if source_age != "None" or source_gender != "None": if source_age != "None" or source_gender != "None":
logger.status("Detected: -%s- y.o. %s", source_age, source_gender) logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
@ -456,7 +781,7 @@ def swap_face(
if source_face is not None and wrong_gender == 0: if source_face is not None and wrong_gender == 0:
logger.status("Detecting Target Face, Index = %s", face_num) logger.status("Detecting Target Face, Index = %s", face_num)
target_face, wrong_gender, target_age, target_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target) target_face, wrong_gender, target_age, target_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
if target_age != "None" or target_gender != "None": if target_age != "None" or target_gender != "None":
logger.status("Detected: -%s- y.o. %s", target_age, target_gender) logger.status("Detected: -%s- y.o. %s", target_age, target_gender)
@ -468,7 +793,7 @@ def swap_face(
swapped_image = face_swapper.get(result, target_face, source_face) swapped_image = face_swapper.get(result, target_face, source_face)
if mask_face: if mask_face:
result = apply_face_mask(swapped_image=swapped_image,target_image=result,target_face=target_face,entire_mask_image=entire_mask_image,mask_options=mask_options) result = apply_face_mask(swapped_image=swapped_image,target_image=result,target_face=target_face,entire_mask_image=entire_mask_image)
else: else:
result = swapped_image result = swapped_image
swapped += 1 swapped += 1
@ -505,7 +830,7 @@ def swap_face(
if enhancement_options is not None and swapped > 0: if enhancement_options is not None and swapped > 0:
if mask_face and entire_mask_image is not None: if mask_face and entire_mask_image is not None:
result_image, masked_faces = enhance_image_and_mask(result_image, enhancement_options,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L")) result_image = enhance_image_and_mask(result_image, enhancement_options,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
else: else:
result_image = enhance_image(result_image, enhancement_options) result_image = enhance_image(result_image, enhancement_options)
elif mask_face and entire_mask_image is not None and swapped > 0: elif mask_face and entire_mask_image is not None and swapped > 0:
@ -516,28 +841,29 @@ def swap_face(
else: else:
logger.status("No source face(s) found") logger.status("No source face(s) found")
return result_image, output, swapped,masked_faces return result_image, output, swapped
def apply_face_mask(swapped_image:np.ndarray,target_image:np.ndarray,target_face,entire_mask_image:np.array,mask_options:Union[MaskOptions,None] = None)->np.ndarray: # def apply_face_mask(swapped_image:np.ndarray,target_image:np.ndarray,target_face,entire_mask_image:np.array)->np.ndarray:
logger.status("Masking Face") # logger.status("Masking Face")
mask_generator = BiSeNetMaskGenerator() # mask_generator = BiSeNetMaskGenerator()
face = Face(target_image,Rect.from_ndarray(np.array(target_face.bbox)),1.6,mask_options.face_size,"") # face = Face(target_image,Rect.from_ndarray(np.array(target_face.bbox)),1.6,512,"")
face_image = np.array(face.image) # face_image = np.array(face.image)
face_area_on_image = face.face_area_on_image # process_face_image(face)
# face_area_on_image = face.face_area_on_image
mask = mask_generator.generate_mask(face_image,face_area_on_image=face_area_on_image,affected_areas=mask_options.mask_areas,mask_size=0,use_minimal_area=mask_options.use_minimal_area) # mask = mask_generator.generate_mask(face_image,face_area_on_image=face_area_on_image,affected_areas=["Face"],mask_size=0,use_minimal_area=True)
mask = cv2.blur(mask, (mask_options.mask_blur, mask_options.mask_blur)) # mask = cv2.blur(mask, (12, 12))
larger_mask = cv2.resize(mask, dsize=(face.width, face.height)) # """entire_mask_image = np.zeros_like(target_image)"""
entire_mask_image[ # larger_mask = cv2.resize(mask, dsize=(face.width, face.height))
face.top : face.bottom, # entire_mask_image[
face.left : face.right, # face.top : face.bottom,
] = larger_mask # face.left : face.right,
# ] = larger_mask
result = Image.composite(Image.fromarray(swapped_image),Image.fromarray(target_image), Image.fromarray(entire_mask_image).convert("L")) # result = Image.composite(Image.fromarray(swapped_image),Image.fromarray(target_image), Image.fromarray(entire_mask_image).convert("L"))
return np.array(result) # return np.array(result)
def correct_face_tilt(angle: float) -> bool: def correct_face_tilt(angle: float) -> bool:
@ -577,7 +903,22 @@ def color_generator(colors):
color_iter = color_generator(colors) color_iter = color_generator(colors)
def process_face_image(
face: Face,
**kwargs,
) -> Image:
image = np.array(face.image)
overlay = image.copy()
cv2.rectangle(overlay, (0, 0), (image.shape[1], image.shape[0]), next(color_iter), -1)
l, t, r, b = face.face_area_on_image
cv2.rectangle(overlay, (l, t), (r, b), (0, 0, 0), 10)
if face.landmarks_on_image is not None:
for landmark in face.landmarks_on_image:
cv2.circle(overlay, (int(landmark.x), int(landmark.y)), 6, (0, 0, 0), 10)
alpha = 0.3
output = cv2.addWeighted(image, 1 - alpha, overlay, alpha, 0)
return Image.fromarray(output)
def dilate_erode(img: Image.Image, value: int) -> Image.Image: def dilate_erode(img: Image.Image, value: int) -> Image.Image:
""" """
The dilate_erode function takes an image and a value. The dilate_erode function takes an image and a value.

5
scripts/reactor_version.py
View File

@ -1,10 +1,11 @@
app_title = "ReActor" app_title = "ReActor"
version_flag = "v0.5.0" version_flag = "v0.7.0-b6"
from scripts.reactor_logger import logger, get_Run, set_Run from scripts.reactor_logger import logger, get_Run, set_Run
from scripts.reactor_globals import DEVICE
is_run = get_Run() is_run = get_Run()
if not is_run: if not is_run:
logger.status(f"Running {version_flag}") logger.status(f"Running {version_flag} on Device: {DEVICE}")
set_Run(True) set_Run(True)

86
scripts/reactor_xyz.py Normal file
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@ -0,0 +1,86 @@
'''
Thanks @ledahu for contributing
'''
from modules import scripts
from modules.shared import opts
from scripts.reactor_helpers import (
get_model_names,
get_facemodels
)
# xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
def find_module(module_names):
if isinstance(module_names, str):
module_names = [s.strip() for s in module_names.split(",")]
for data in scripts.scripts_data:
if data.script_class.__module__ in module_names and hasattr(data, "module"):
return data.module
return None
def bool_(string):
string = str(string)
if string in ["None", ""]:
return None
elif string.lower() in ["true", "1"]:
return True
elif string.lower() in ["false", "0"]:
return False
else:
raise ValueError(f"Could not convert string to boolean: {string}")
def choices_bool():
return ["False", "True"]
def choices_face_models():
return get_model_names(get_facemodels)
def float_applier(value_name:str, min_range:float = 0, max_range:float = 1):
"""
Returns a function that applies the given value to the given value_name in opts.data.
"""
def validate(value_name:str, value:str):
value = float(value)
# validate value
if not min_range == 0:
assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}"
if not max_range == 1:
assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}"
def apply_float(p, x, xs):
validate(value_name, x)
opts.data[value_name] = float(x)
return apply_float
def bool_applier(value_name:str):
def apply_bool(p, x, xs):
x_normed = bool_(x)
opts.data[value_name] = x_normed
# print(f'normed = {x_normed}')
return apply_bool
def str_applier(value_name:str):
def apply_str(p, x, xs):
opts.data[value_name] = x
return apply_str
def add_axis_options(xyz_grid):
extra_axis_options = [
xyz_grid.AxisOption("[ReActor] CodeFormer Weight", float, float_applier("codeformer_weight", 0, 1)),
xyz_grid.AxisOption("[ReActor] Restorer Visibility", float, float_applier("restorer_visibility", 0, 1)),
xyz_grid.AxisOption("[ReActor] Face Mask Correction", str, bool_applier("mask_face"), choices=choices_bool),
xyz_grid.AxisOption("[ReActor] Face Models", str, str_applier("face_model"), choices=choices_face_models),
]
set_a = {opt.label for opt in xyz_grid.axis_options}
set_b = {opt.label for opt in extra_axis_options}
if set_a.intersection(set_b):
return
xyz_grid.axis_options.extend(extra_axis_options)
def run():
xyz_grid = find_module("xyz_grid.py, xy_grid.py")
if xyz_grid:
add_axis_options(xyz_grid)