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.gitignore

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+__pycache__/
+*$py.class
+.vscode/
+example
+input

LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,363 @@
+# reactor_node
+新增人脸检测功能（图片中有人脸就换脸，没人脸就直接返回）
+
+<div align="center">
+
+  <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/node_version-0.4.1_beta12-green?style=for-the-badge&labelColor=darkgreen)
+
+  <sup>
+  <font color=brightred>
+
+  ## !!! [Important Update](#latestupdate) !!!<br>Don't forget to add the Node again in existing workflows
+  
+  </font>
+  </sup>
+  
+  <a href="https://boosty.to/artgourieff" target="_blank">
+    <img src="https://lovemet.ru/www/boosty.jpg" width="108" alt="Support Me on Boosty"/>
+    <br>
+    <sup>
+      Support This Project
+    </sup>
+  </a>
+
+  <hr>
+  
+  [![Commit activity](https://img.shields.io/github/commit-activity/t/Gourieff/comfyui-reactor-node/main?cacheSeconds=0)](https://github.com/Gourieff/comfyui-reactor-node/commits/main)
+  ![Last commit](https://img.shields.io/github/last-commit/Gourieff/comfyui-reactor-node/main?cacheSeconds=0)
+  [![Opened issues](https://img.shields.io/github/issues/Gourieff/comfyui-reactor-node?color=red)](https://github.com/Gourieff/comfyui-reactor-node/issues?cacheSeconds=0)
+  [![Closed issues](https://img.shields.io/github/issues-closed/Gourieff/comfyui-reactor-node?color=green&cacheSeconds=0)](https://github.com/Gourieff/comfyui-reactor-node/issues?q=is%3Aissue+is%3Aclosed)
+  ![License](https://img.shields.io/github/license/Gourieff/comfyui-reactor-node)
+
+  English | [Русский](/README_RU.md)
+
+# ReActor Node for ComfyUI
+
+</div>
+
+### The Fast and Simple Face Swap Extension Node for ComfyUI, based on [ReActor](https://github.com/Gourieff/sd-webui-reactor) SD-WebUI Face Swap Extension
+
+> This Node goes without NSFW filter (uncensored, use it on your own [responsibility](#disclaimer)) 
+
+<div align="center">
+
+---
+[**What's new**](#latestupdate) | [**Installation**](#installation) | [**Usage**](#usage) | [**Troubleshooting**](#troubleshooting) | [**Updating**](#updating) | [**Disclaimer**](#disclaimer) | [**Credits**](#credits) | [**Note!**](#note)
+
+---
+
+</div>
+
+<div align="center">
+  <img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/demo.gif?raw=true" alt="demo" width="100%"/>
+</div>
+
+<a name="latestupdate">
+
+## What's new in the latest update
+
+### 0.4.1 <sub><sup>BETA9</sup></sub>
+
+- CUDA 12 Support - don't forget to run (Windows) `install.bat` or (Linux/MacOS) `install.py` for ComfyUI's Python enclosure or try to install ORT-GPU for CU12 manually (https://onnxruntime.ai/docs/install/#install-onnx-runtime-gpu-cuda-12x)
+- Issue https://github.com/Gourieff/comfyui-reactor-node/issues/173 fix
+
+### 0.4.1 <sub><sup>BETA8</sup></sub>
+
+- Seperate Node for the Face Restoration postprocessing (FR https://github.com/Gourieff/comfyui-reactor-node/issues/191), can be found inside ReActor's menu (RestoreFace Node)
+- (Windows) Installation can be done for Python from the System's PATH
+- Different fixes and improvements
+
+### 0.4.1 <sub><sup>BETA1</sup></sub>
+
+- Face Restore Visibility and CodeFormer Weight (Fidelity) options are now available! Don't forget to reload the Node in your existing workflow
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.4.1-whatsnew-01.jpg?raw=true" alt="0.4.1-whatsnew-01" width="100%"/>
+
+### 0.4.0
+
+- Input "input_image" goes first now, it gives a correct bypass and also it is right to have the main input first;
+- You can now save face models as "safetensors" files (`ComfyUI\models\reactor\faces`) and load them into ReActor implementing different scenarios and keeping super lightweight face models of the faces you use:
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.4.0-whatsnew-01.jpg?raw=true" alt="0.4.0-whatsnew-01" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.4.0-whatsnew-02.jpg?raw=true" alt="0.4.0-whatsnew-02" width="100%"/>
+
+- Ability to build and save face models directly from an image:
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.4.0-whatsnew-03.jpg?raw=true" alt="0.4.0-whatsnew-03" width="50%"/>
+
+- Both the inputs are optional, just connect one of them according to your workflow; if both is connected - `image` has a priority.
+- Different fixes making this extension better.
+
+Thanks to everyone who finds bugs, suggests new features and supports this project!
+
+## Installation
+
+<details>
+	<summary>SD WebUI: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/">AUTOMATIC1111</a> or <a href="https://github.com/vladmandic/automatic">SD.Next</a></summary>
+
+1. Close (stop) your SD-WebUI/Comfy Server if it's running
+2. (For Windows Users):
+   - Install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) (Community version - you need this step to build Insightface)
+   - OR only [VS C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile"
+   - OR if you don't want to install VS or VS C++ BT - follow [this steps (sec. I)](#insightfacebuild)
+3. Go to the `extensions\sd-webui-comfyui\ComfyUI\custom_nodes`
+4. Open Console or Terminal and run `git clone https://github.com/Gourieff/comfyui-reactor-node`
+5. Go to the SD WebUI root folder, open Console or Terminal and run (Windows users)`.\venv\Scripts\activate` or (Linux/MacOS)`venv/bin/activate`
+6. `python -m pip install -U pip`
+7. `cd extensions\sd-webui-comfyui\ComfyUI\custom_nodes\comfyui-reactor-node`
+8. `python install.py`
+9.  Please, wait until the installation process will be finished
+10. (From the version 0.3.0) Download facerestorers models from the links below and put them into the `extensions\sd-webui-comfyui\ComfyUI\custom_nodes\comfyui-reactor-node\models\facerestore_models` directory:
+    - CodeFormer: https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth
+    - GFPGAN: https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
+11. Run SD WebUI and check console for the message that ReActor Node is running:
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/console_status_running.jpg?raw=true" alt="console_status_running" width="759"/>
+
+1.  Go to the ComfyUI tab and find there ReActor Node inside the menu `ReActor` or by using a search:
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/webui-demo.png?raw=true" alt="webui-demo" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/search-demo.png?raw=true" alt="webui-demo" width="1043"/>
+
+</details>
+
+<details>
+	<summary>Standalone (Portable) <a href="https://github.com/comfyanonymous/ComfyUI">ComfyUI</a> for Windows</summary>
+
+1. Do the following:
+   - Install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) (Community version - you need this step to build Insightface)
+   - OR only [VS C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile"
+   - OR if you don't want to install VS or VS C++ BT - follow [this steps (sec. I)](#insightfacebuild)
+2. Go to the `ComfyUI\custom_nodes` directory
+3. Open Console and run `git clone https://github.com/Gourieff/comfyui-reactor-node`
+4. Run `install.bat`
+5. (From the version 0.3.0) Download facerestorers models from the links below and put them into the `ComfyUI\models\facerestore_models` directory:
+   - CodeFormer: https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth
+   - GFPGAN: https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
+6. Run ComfyUI and find there ReActor Node inside the menu `ReActor` or by using a search
+
+</details>
+
+## Usage
+
+You can find ReActor Nodes inside the menu `ReActor` or by using a search (just type "ReActor" in the search field)
+
+List of Nodes:
+- ReActorFaceSwap (Main Node)
+- ReActorLoadFaceModel (Load Face Model)
+- ReActorSaveFaceModel (Save Face Model)
+
+Connect all required slots and run the query.
+
+### Main Node Inputs
+
+- `input_image` - is an image to be processed (target image, analog of "target image" in the SD WebUI extension);
+  - Supported Nodes: "Load Image", "Load Video" or any other nodes providng images as an output;
+- `source_image` - is an image with a face or faces to swap in the `input_image` (source image, analog of "source image" in the SD WebUI extension);
+  - Supported Nodes: "Load Image";
+- `face_model` - is the input for the "Load Face Model" Node or another ReActor node to provide a face model file (face embedding) you created earlier via the "Save Face Model" Node;
+  - Supported Nodes: "Load Face Model";
+
+### Main Node Outputs
+
+- `IMAGE` - is an output with the resulted image;
+  - Supported Nodes: any nodes which have images as an input;
+- `FACE_MODEL` - is an output providng a source face's model being built during the swapping process;
+  - Supported Nodes: "Save Face Model", "ReActor";
+
+### Face Restoration
+
+Since version 0.3.0 ReActor Node has a buil-in face restoration.<br>Just download the models you want (see [Installation](#installation) instruction) and select one of them to restore the resulting face(s) during the faceswap. It will enhance face details and make your result more accurate.
+
+### Face Indexes
+
+ReActor detects faces in images in the following order:<br>left->right, top->bottom
+
+And if you need to specify faces, you can set indexes for source and input images.
+
+Index of the first detected face is 0.
+
+You can set indexes in the order you need.<br>
+E.g.: 0,1,2 (for Source); 1,0,2 (for Input).<br>This means: the second Input face (index = 1) will be swapped by the first Source face (index = 0) and so on.
+
+### Genders
+
+You can specify the gender to detect in images.<br>
+ReActor will swap a face only if it meets the given condition.
+
+### Face Models
+
+Since version 0.4.0 you can save face models as "safetensors" files (stored in `ComfyUI\models\reactor\faces`) and load them into ReActor implementing different scenarios and keeping super lightweight face models of the faces you use.
+
+To make new models appear in the list of the "Load Face Model" Node - just refresh the page of your ComfyUI web application.<br>
+(I recommend you to use ComfyUI Manager - otherwise you workflow can be lost after you refresh the page if you didn't save it before that).
+
+## Troubleshooting
+
+<a name="insightfacebuild">
+
+### **I. (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. (ComfyUI Portable) From the root folder check the version of Python:<br>run CMD and type `python_embeded\python.exe -V`
+2. Download prebuilt Insightface package [for Python 3.10](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp310-cp310-win_amd64.whl) or [for Python 3.11](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp311-cp311-win_amd64.whl) (if in the previous step you see 3.11) or [for Python 3.12](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp312-cp312-win_amd64.whl) (if in the previous step you see 3.12) and put into the stable-diffusion-webui (A1111 or SD.Next) root folder (where you have "webui-user.bat" file) or into ComfyUI root folder if you use ComfyUI Portable
+3. From the root folder run:
+   - (SD WebUI) CMD and `.\venv\Scripts\activate`
+   - (ComfyUI Portable) run CMD
+4. Then update your PIP:
+   - (SD WebUI) `python -m pip install -U pip`
+   - (ComfyUI Portable) `python_embeded\python.exe -m pip install -U pip`
+5. Then install Insightface:
+   - (SD WebUI) `pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` (for 3.10) or `pip install insightface-0.7.3-cp311-cp311-win_amd64.whl` (for 3.11) or `pip install insightface-0.7.3-cp312-cp312-win_amd64.whl` (for 3.12)
+   - (ComfyUI Portable) `python_embeded\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` (for 3.10) or `python_embeded\python.exe -m pip install insightface-0.7.3-cp311-cp311-win_amd64.whl` (for 3.11) or `python_embeded\python.exe -m pip install insightface-0.7.3-cp312-cp312-win_amd64.whl` (for 3.12)
+6. Enjoy!
+
+### **II. "AttributeError: 'NoneType' object has no attribute 'get'"**
+
+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)
+and put it to the `ComfyUI\models\insightface` replacing existing one
+
+### **III. "reactor.execute() got an unexpected keyword argument 'reference_image'"**
+
+This means that input points have been changed with the latest update<br>
+Remove the current ReActor Node from your workflow and add it again
+
+### **IV. ControlNet Aux Node IMPORT failed error when using with ReActor Node**
+
+1. Close ComfyUI if it runs
+2. Go to the ComfyUI root folder, open CMD there and run:
+   - `python_embeded\python.exe -m pip uninstall -y opencv-python opencv-contrib-python opencv-python-headless`
+   - `python_embeded\python.exe -m pip install opencv-python==4.7.0.72`
+3. That's it!
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/reactor-w-controlnet.png?raw=true" alt="reactor+controlnet" />
+
+### **V. "ModuleNotFoundError: No module named 'basicsr'" or "subprocess-exited-with-error" during future-0.18.3 installation**
+
+- Download https://github.com/Gourieff/Assets/raw/main/comfyui-reactor-node/future-0.18.3-py3-none-any.whl<br>
+- Put it to ComfyUI root And run:
+
+      python_embeded\python.exe -m pip install future-0.18.3-py3-none-any.whl
+
+- Then:
+
+      python_embeded\python.exe -m pip install basicsr
+
+### **VI. "fatal: fetch-pack: invalid index-pack output" when you try to `git clone` the repository"**
+
+Try to clone with `--depth=1` (last commit only):
+
+     git clone --depth=1 https://github.com/Gourieff/comfyui-reactor-node
+
+Then retrieve the rest (if you need):
+
+     git fetch --unshallow
+
+## Updating
+
+Just put .bat or .sh script from this [Repo](https://github.com/Gourieff/sd-webui-extensions-updater) to the `ComfyUI\custom_nodes` directory and run it when you need to check for updates
+
+### Disclaimer
+
+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.
+
+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.**
+
+By using this extension you are agree not to create any content that:
+- violates any laws;
+- causes any harm to a person or persons;
+- propogates (spreads) any information (both public or personal) or images (both public or personal) which could be meant for harm;
+- spreads misinformation;
+- targets vulnerable groups of people.
+
+This software utilizes the pre-trained models `buffalo_l` and `inswapper_128.onnx`, which are provided by [InsightFace](https://github.com/deepinsight/insightface/). These models are included under the following conditions:
+
+[From insighface licence](https://github.com/deepinsight/insightface/tree/master/python-package): The InsightFace’s pre-trained models are available for non-commercial research purposes only. This includes both auto-downloading models and manually downloaded models.
+
+Users of this software must strictly adhere to these conditions of use. The developers and maintainers of this software are not responsible for any misuse of InsightFace’s pre-trained models.
+
+Please note that if you intend to use this software for any commercial purposes, you will need to train your own models or find models that can be used commercially.
+
+### Models Hashsum
+
+#### Safe-to-use models have the folowing hash:
+
+inswapper_128.onnx
+```
+MD5:a3a155b90354160350efd66fed6b3d80
+SHA256:e4a3f08c753cb72d04e10aa0f7dbe3deebbf39567d4ead6dce08e98aa49e16af
+```
+
+1k3d68.onnx
+
+```
+MD5:6fb94fcdb0055e3638bf9158e6a108f4
+SHA256:df5c06b8a0c12e422b2ed8947b8869faa4105387f199c477af038aa01f9a45cc
+```
+
+2d106det.onnx
+
+```
+MD5:a3613ef9eb3662b4ef88eb90db1fcf26
+SHA256:f001b856447c413801ef5c42091ed0cd516fcd21f2d6b79635b1e733a7109dbf
+```
+
+det_10g.onnx
+
+```
+MD5:4c10eef5c9e168357a16fdd580fa8371
+SHA256:5838f7fe053675b1c7a08b633df49e7af5495cee0493c7dcf6697200b85b5b91
+```
+
+genderage.onnx
+
+```
+MD5:81c77ba87ab38163b0dec6b26f8e2af2
+SHA256:4fde69b1c810857b88c64a335084f1c3fe8f01246c9a191b48c7bb756d6652fb
+```
+
+w600k_r50.onnx
+
+```
+MD5:80248d427976241cbd1343889ed132b3
+SHA256:4c06341c33c2ca1f86781dab0e829f88ad5b64be9fba56e56bc9ebdefc619e43
+```
+
+**Please check hashsums if you download these models from unverified (or untrusted) sources**
+
+<a name="credits">
+
+## Thanks and Credits
+
+<details>
+	<summary><a>Click to expand</a></summary>
+
+<br>
+
+|file|source|license|
+|----|------|-------|
+|[buffalo_l.zip](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/buffalo_l.zip) | [DeepInsight](https://github.com/deepinsight/insightface) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+| [codeformer-v0.1.0.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/codeformer-v0.1.0.pth) | [sczhou](https://github.com/sczhou/CodeFormer) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+| [GFPGANv1.3.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.3.pth) | [TencentARC](https://github.com/TencentARC/GFPGAN) | ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) |
+| [GFPGANv1.4.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.4.pth) | [TencentARC](https://github.com/TencentARC/GFPGAN) | ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) |
+| [inswapper_128.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) | [DeepInsight](https://github.com/deepinsight/insightface) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+| [inswapper_128_fp16.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx) | [Hillobar](https://github.com/Hillobar/Rope) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+
+[BasicSR](https://github.com/XPixelGroup/BasicSR) - [@XPixelGroup](https://github.com/XPixelGroup) <br>
+[facexlib](https://github.com/xinntao/facexlib) - [@xinntao](https://github.com/xinntao) <br>
+
+[@s0md3v](https://github.com/s0md3v), [@henryruhs](https://github.com/henryruhs) - the original Roop App <br>
+[@ssitu](https://github.com/ssitu) - the first version of [ComfyUI_roop](https://github.com/ssitu/ComfyUI_roop) extension
+
+</details>
+
+<a name="note">
+
+### Note!
+
+**If you encounter any errors when you use ReActor Node - don't rush to open an issue, first try to remove current ReActor node in your workflow and add it again**
+
+**ReActor Node gets updates from time to time, new functions appears and old node can work with errors or not work at all**

__init__.py

@@ -0,0 +1,39 @@
+import sys
+import os
+
+repo_dir = os.path.dirname(os.path.realpath(__file__))
+sys.path.insert(0, repo_dir)
+original_modules = sys.modules.copy()
+
+# Place aside existing modules if using a1111 web ui
+modules_used = [
+    "modules",
+    "modules.images",
+    "modules.processing",
+    "modules.scripts_postprocessing",
+    "modules.scripts",
+    "modules.shared",
+]
+original_webui_modules = {}
+for module in modules_used:
+    if module in sys.modules:
+        original_webui_modules[module] = sys.modules.pop(module)
+
+# Proceed with node setup
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
+
+# Clean up imports
+# Remove repo directory from path
+sys.path.remove(repo_dir)
+# Remove any new modules
+modules_to_remove = []
+for module in sys.modules:
+    if module not in original_modules and not module.startswith("google.protobuf") and not module.startswith("onnx") and not module.startswith("cv2"):
+        modules_to_remove.append(module)
+for module in modules_to_remove:
+    del sys.modules[module]
+
+# Restore original modules
+sys.modules.update(original_webui_modules)

install.bat

@@ -0,0 +1,37 @@
+@echo off
+setlocal enabledelayedexpansion
+
+:: Try to use embedded python first
+if exist ..\..\..\python_embeded\python.exe (
+    :: Use the embedded python
+    set PYTHON=..\..\..\python_embeded\python.exe
+) else (
+    :: Embedded python not found, check for python in the PATH
+    for /f "tokens=* USEBACKQ" %%F in (`python --version 2^>^&1`) do (
+        set PYTHON_VERSION=%%F
+    )
+    if errorlevel 1 (
+        echo I couldn't find an embedded version of Python, nor one in the Windows PATH. Please install manually.
+        pause
+        exit /b 1
+    ) else (
+        :: Use python from the PATH (if it's the right version and the user agrees)
+        echo I couldn't find an embedded version of Python, but I did find !PYTHON_VERSION! in your Windows PATH.
+        echo Would you like to proceed with the install using that version? (Y/N^)
+        set /p USE_PYTHON=
+        if /i "!USE_PYTHON!"=="Y" (
+            set PYTHON=python
+        ) else (
+            echo Okay. Please install manually.
+            pause
+            exit /b 1
+        )
+    )
+)
+
+:: Install the package
+echo Installing...
+%PYTHON% install.py
+echo Done^!
+
+@pause

install.py

@@ -0,0 +1,99 @@
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import subprocess
+import os, sys
+try:
+    from pkg_resources import get_distribution as distributions
+except:
+    from importlib_metadata import distributions
+from tqdm import tqdm
+import urllib.request
+from packaging import version as pv
+try:
+    from folder_paths import models_dir
+except:
+    from pathlib import Path
+    models_dir = os.path.join(Path(__file__).parents[2], "models")
+
+sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+
+req_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "requirements.txt")
+
+model_url = "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx"
+model_name = os.path.basename(model_url)
+models_dir_path = os.path.join(models_dir, "insightface")
+model_path = os.path.join(models_dir_path, model_name)
+
+def run_pip(*args):
+    subprocess.run([sys.executable, "-m", "pip", "install", "--no-warn-script-location", *args])
+
+def is_installed (
+        package: str, version: str = None, strict: bool = True
+):
+    has_package = None
+    try:
+        has_package = distributions(package)
+        if has_package is not None:
+            if version is not None:
+                installed_version = has_package.version
+                if (installed_version != version and strict == True) or (pv.parse(installed_version) < pv.parse(version) and strict == False):
+                    return False
+                else:
+                    return True
+            else:
+                return True
+        else:
+            return False
+    except Exception as e:
+        print(f"Status: {e}")
+        return False
+    
+def download(url, path, name):
+    request = urllib.request.urlopen(url)
+    total = int(request.headers.get('Content-Length', 0))
+    with tqdm(total=total, desc=f'[ReActor] Downloading {name} to {path}', unit='B', unit_scale=True, unit_divisor=1024) as progress:
+        urllib.request.urlretrieve(url, path, reporthook=lambda count, block_size, total_size: progress.update(block_size))
+
+if not os.path.exists(models_dir_path):
+    os.makedirs(models_dir_path)
+
+if not os.path.exists(model_path):
+    download(model_url, model_path, model_name)
+
+with open(req_file) as file:
+    try:
+        ort = "onnxruntime-gpu"
+        import torch
+        cuda_version = None
+        if torch.cuda.is_available():
+            cuda_version = torch.version.cuda
+            print(f"CUDA {cuda_version}")
+        elif torch.backends.mps.is_available() or hasattr(torch,'dml') or hasattr(torch,'privateuseone'):
+            ort = "onnxruntime"
+        if cuda_version is not None and float(cuda_version)>=12: # CU12
+            if not is_installed(ort,"1.17.0",False):
+                run_pip(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):
+            run_pip(ort, "-U")
+    except Exception as e:
+        print(e)
+        print(f"Warning: Failed to install {ort}, ReActor will not work.")
+        raise e
+    strict = True
+    for package in file:
+        package_version = None
+        try:
+            package = package.strip()
+            if "==" in package:
+                package_version = package.split('==')[1]
+            elif ">=" in package:
+                package_version = package.split('>=')[1]
+                strict = False
+            if not is_installed(package,package_version,strict):
+                run_pip(package)
+        except Exception as e:
+            print(e)
+            print(f"Warning: Failed to install {package}, ReActor will not work.")
+            raise e
+print("Ok")

nodes.py

@@ -0,0 +1,440 @@
+import os, glob, sys
+
+import torch
+from torchvision.transforms.functional import normalize
+import numpy as np
+import cv2
+
+from modules.processing import StableDiffusionProcessingImg2Img
+from comfy_extras.chainner_models import model_loading
+import comfy.model_management as model_management
+import comfy.utils
+import folder_paths
+
+import scripts.reactor_version
+from scripts.reactor_faceswap import (
+    FaceSwapScript,
+    get_models,
+    get_current_faces_model,
+    analyze_faces,
+    half_det_size
+)
+from scripts.reactor_logger import logger
+from reactor_utils import (
+    batch_tensor_to_pil,
+    batched_pil_to_tensor,
+    tensor_to_pil,
+    img2tensor,
+    tensor2img,
+    save_face_model,
+    load_face_model,
+    download
+)
+from reactor_log_patch import apply_logging_patch
+from r_facelib.utils.face_restoration_helper import FaceRestoreHelper
+from r_basicsr.utils.registry import ARCH_REGISTRY
+import scripts.r_archs.codeformer_arch
+
+
+models_dir = folder_paths.models_dir
+REACTOR_MODELS_PATH = os.path.join(models_dir, "reactor")
+FACE_MODELS_PATH = os.path.join(REACTOR_MODELS_PATH, "faces")
+
+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)
+
+dir_facerestore_models = os.path.join(models_dir, "facerestore_models")
+os.makedirs(dir_facerestore_models, exist_ok=True)
+folder_paths.folder_names_and_paths["facerestore_models"] = ([dir_facerestore_models], folder_paths.supported_pt_extensions)
+
+
+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_restorers():
+    models_path = os.path.join(models_dir, "facerestore_models/*")
+    models = glob.glob(models_path)
+    models = [x for x in models if x.endswith(".pth")]
+    if len(models) == 0:
+        fr_urls = [
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/GFPGANv1.3.pth",
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/GFPGANv1.4.pth",
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/codeformer-v0.1.0.pth"
+        ]
+        for model_url in fr_urls:
+            model_name = os.path.basename(model_url)
+            model_path = os.path.join(dir_facerestore_models, model_name)
+            download(model_url, model_path, model_name)
+        models = glob.glob(models_path)
+        models = [x for x in models if x.endswith(".pth")]
+    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 model_names():
+    models = get_models()
+    return {os.path.basename(x): x for x in models}
+
+
+class reactor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "enabled": ("BOOLEAN", {"default": True, "label_off": "OFF", "label_on": "ON"}),
+                "input_image": ("IMAGE",),               
+                "swap_model": (list(model_names().keys()),),
+                "facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],),
+                "face_restore_model": (get_model_names(get_restorers),),
+                "face_restore_visibility": ("FLOAT", {"default": 1, "min": 0.1, "max": 1, "step": 0.05}),
+                "codeformer_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.05}),
+                "detect_gender_input": (["no","female","male"], {"default": "no"}),
+                "detect_gender_source": (["no","female","male"], {"default": "no"}),
+                "input_faces_index": ("STRING", {"default": "0"}),
+                "source_faces_index": ("STRING", {"default": "0"}),
+                "console_log_level": ([0, 1, 2], {"default": 1}),
+            },
+            "optional": {
+                "source_image": ("IMAGE",),
+                "face_model": ("FACE_MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE","FACE_MODEL")
+    FUNCTION = "execute"
+    CATEGORY = "ReActor"
+
+    def __init__(self):
+        self.face_helper = None
+
+    def restore_face(
+            self,
+            input_image,
+            face_restore_model,
+            face_restore_visibility,
+            codeformer_weight,
+            facedetection
+        ):
+
+        result = input_image
+
+        if face_restore_model != "none" and not model_management.processing_interrupted():
+
+            logger.status(f"Restoring with {face_restore_model}")
+
+            model_path = folder_paths.get_full_path("facerestore_models", face_restore_model)
+
+            device = model_management.get_torch_device()
+            
+            if "codeformer" in face_restore_model.lower():
+                
+                codeformer_net = ARCH_REGISTRY.get("CodeFormer")(
+                    dim_embd=512,
+                    codebook_size=1024,
+                    n_head=8,
+                    n_layers=9,
+                    connect_list=["32", "64", "128", "256"],
+                ).to(device)
+                checkpoint = torch.load(model_path)["params_ema"]
+                codeformer_net.load_state_dict(checkpoint)
+                facerestore_model = codeformer_net.eval()
+            
+            else:
+
+                sd = comfy.utils.load_torch_file(model_path, safe_load=True)
+                facerestore_model = model_loading.load_state_dict(sd).eval()
+
+            facerestore_model.to(device)
+            
+            if self.face_helper is None:
+                self.face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device)
+
+            image_np = 255. * result.cpu().numpy()
+
+            total_images = image_np.shape[0]
+            out_images = np.ndarray(shape=image_np.shape)
+
+            for i in range(total_images):
+                cur_image_np = image_np[i,:, :, ::-1]
+
+                original_resolution = cur_image_np.shape[0:2]
+
+                if facerestore_model is None or self.face_helper is None:
+                    return result
+
+                self.face_helper.clean_all()
+                self.face_helper.read_image(cur_image_np)
+                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+                self.face_helper.align_warp_face()
+
+                restored_face = None
+                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+                    cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
+
+                    try:
+                        with torch.no_grad():
+                            output = facerestore_model(cropped_face_t, w=codeformer_weight)[0] if "codeformer" in face_restore_model.lower() else facerestore_model(cropped_face_t)[0]
+                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+                        del output
+                        torch.cuda.empty_cache()
+                    except Exception as error:
+                        print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
+                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+                    
+                    if face_restore_visibility < 1:
+                        restored_face = cropped_face * (1 - face_restore_visibility) + restored_face * face_restore_visibility
+                    
+                    restored_face = restored_face.astype('uint8')
+                    self.face_helper.add_restored_face(restored_face)
+
+                self.face_helper.get_inverse_affine(None)
+
+                restored_img = self.face_helper.paste_faces_to_input_image()
+                restored_img = restored_img[:, :, ::-1]
+
+                if original_resolution != restored_img.shape[0:2]:
+                    restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
+
+                self.face_helper.clean_all()
+
+                out_images[i] = restored_img
+
+            restored_img_np = np.array(out_images).astype(np.float32) / 255.0
+            restored_img_tensor = torch.from_numpy(restored_img_np)
+
+            result = restored_img_tensor
+
+        return result
+    
+    def execute(self, enabled, input_image, swap_model, detect_gender_source, detect_gender_input, source_faces_index, input_faces_index, console_log_level, face_restore_model, face_restore_visibility, codeformer_weight, facedetection, source_image=None, face_model=None):
+        apply_logging_patch(console_log_level)
+
+        if not enabled:
+            return (input_image,face_model)
+        elif source_image is None and face_model is None:
+            logger.error("Please provide 'source_image' or `face_model`")
+            return (input_image,face_model)
+
+        if face_model == "none":
+            face_model = None
+        
+        script = FaceSwapScript()
+        pil_images = batch_tensor_to_pil(input_image)
+        if source_image is not None:
+            source = tensor_to_pil(source_image)
+        else:
+            source = None
+        p = StableDiffusionProcessingImg2Img(pil_images)
+        script.process(
+            p=p,
+            img=source,
+            enable=True,
+            source_faces_index=source_faces_index,
+            faces_index=input_faces_index,
+            model=swap_model,
+            swap_in_source=True,
+            swap_in_generated=True,
+            gender_source=detect_gender_source,
+            gender_target=detect_gender_input,
+            face_model=face_model,
+        )
+        result = batched_pil_to_tensor(p.init_images)
+
+        if face_model is None:
+            current_face_model = get_current_faces_model()
+            face_model_to_provide = current_face_model[0] if (current_face_model is not None and len(current_face_model) > 0) else face_model
+        else:
+            face_model_to_provide = face_model
+        
+        result = reactor.restore_face(self,result,face_restore_model,face_restore_visibility,codeformer_weight,facedetection)
+
+        return (result,face_model_to_provide)
+
+
+class LoadFaceModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "face_model": (get_model_names(get_facemodels),),
+            }
+        }
+    
+    RETURN_TYPES = ("FACE_MODEL",)
+    FUNCTION = "load_model"
+    CATEGORY = "ReActor"
+
+    def load_model(self, face_model):
+        self.face_model = face_model
+        self.face_models_path = FACE_MODELS_PATH
+        if self.face_model != "none":
+            face_model_path = os.path.join(self.face_models_path, self.face_model)
+            out = load_face_model(face_model_path)
+        else:
+            out = None
+        return (out, )
+
+
+class SaveFaceModel:
+    def __init__(self):
+        self.output_dir = FACE_MODELS_PATH
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "save_mode": ("BOOLEAN", {"default": True, "label_off": "OFF", "label_on": "ON"}),
+                "face_model_name": ("STRING", {"default": "default"}),
+                "select_face_index": ("INT", {"default": 0, "min": 0}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+                "face_model": ("FACE_MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_model"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "ReActor"
+
+    def save_model(self, save_mode, face_model_name, select_face_index, image=None, face_model=None, det_size=(640, 640)):
+        if save_mode and image is not None:
+            source = tensor_to_pil(image)
+            source = cv2.cvtColor(np.array(source), cv2.COLOR_RGB2BGR)
+            apply_logging_patch(1)
+            logger.status("Building Face Model...")
+            face_model_raw = analyze_faces(source, det_size)
+            if len(face_model_raw) == 0:
+                det_size_half = half_det_size(det_size)
+                face_model_raw = analyze_faces(source, det_size_half)
+            try:
+                face_model = face_model_raw[select_face_index]
+            except:
+                logger.error("No face(s) found")
+                return face_model_name
+            logger.status("--Done!--")
+        if save_mode and (face_model != "none" or face_model is not None):
+            face_model_path = os.path.join(self.output_dir, face_model_name + ".safetensors")
+            save_face_model(face_model,face_model_path)
+        if image is None and face_model is None:
+            logger.error("Please provide `face_model` or `image`")
+        return face_model_name
+
+
+class RestoreFace:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),               
+                "facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],),
+                "model": (get_model_names(get_restorers),),
+                "visibility": ("FLOAT", {"default": 1, "min": 0.0, "max": 1, "step": 0.05}),
+                "codeformer_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.05}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "ReActor"
+
+    def __init__(self):
+        self.face_helper = None
+
+    def execute(self, image, model, visibility, codeformer_weight, facedetection):
+        result = reactor.restore_face(self,image,model,visibility,codeformer_weight,facedetection)
+        return (result,)
+
+import numpy as np
+from ultralytics import YOLO
+from PIL import Image
+
+# Load a pretrained YOLOv8n model
+model = YOLO(task='detect', model='/root/autodl-tmp/ComfyUI/custom_nodes/comfyui-reactor-node/yolov8m_200e.pt')
+
+class Mynode_2:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_image": ("IMAGE",),
+                "source_image": ("IMAGE",),
+            },
+            "optional": {
+
+            }
+        }
+
+    CATEGORY = "ReActor"
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "method"
+
+    def method(self, input_image, source_image):
+        input_image_tmp = input_image.squeeze()
+        # Pytorch张量转PIL对象
+        input_image_pil = Image.fromarray(
+            np.clip(255. * input_image_tmp.cpu().numpy(), 0, 255).astype(np.uint8)).convert('RGBA')
+
+        # Run inference on an image
+        results = model.predict(source=input_image_pil, conf=0.5)
+        # View results
+        tmp = results[0].boxes.shape
+        judge_face = tmp[0]
+        print(judge_face)
+
+        if judge_face == 0: # 等于0就是没检测出脸
+
+            return (input_image,)
+
+        else:
+            enabled = True
+
+            swap_model = "inswapper_128.onnx"
+            facedetection = "retinaface_resnet50"
+            face_restore_model = "GFPGANv1.4.pth"
+            face_restore_visibility = 1
+            codeformer_weight = 0.5
+            detect_gender_input = "no"
+            detect_gender_source = "no"
+            input_faces_index = "0"
+            source_faces_index = "0"
+            console_log_level = 1
+
+            class_reactor = reactor()
+            change_face_img, face_model = class_reactor.execute(enabled, input_image, swap_model, detect_gender_source, detect_gender_input,
+                                                                source_faces_index, input_faces_index, console_log_level, face_restore_model,
+                                                                face_restore_visibility, codeformer_weight, facedetection, source_image=source_image,
+                                                                face_model=None)
+
+            return (change_face_img,)
+
+NODE_CLASS_MAPPINGS = {
+    "ReActorFaceSwap": reactor,
+    "ReActorLoadFaceModel": LoadFaceModel,
+    "ReActorSaveFaceModel": SaveFaceModel,
+    "ReActorRestoreFace": RestoreFace,
+    "face_detect": Mynode_2,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ReActorFaceSwap": "ReActor - Fast Face Swap",
+    "ReActorLoadFaceModel": "Load Face Model",
+    "ReActorSaveFaceModel": "Save Face Model",
+    "ReActorRestoreFace": "Restore Face",
+    "face_detect": "face_detect - Reactor",
+}

reactor_log_patch.py

@@ -0,0 +1,120 @@
+import os.path as osp
+import glob
+import logging
+import insightface
+from insightface.model_zoo.model_zoo import ModelRouter, PickableInferenceSession
+from insightface.model_zoo.retinaface import RetinaFace
+from insightface.model_zoo.landmark import Landmark
+from insightface.model_zoo.attribute import Attribute
+from insightface.model_zoo.inswapper import INSwapper
+from insightface.model_zoo.arcface_onnx import ArcFaceONNX
+from insightface.app import FaceAnalysis
+from insightface.utils import DEFAULT_MP_NAME, ensure_available
+from insightface.model_zoo import model_zoo
+import onnxruntime
+import onnx
+from onnx import numpy_helper
+from scripts.reactor_logger import logger
+
+
+def patched_get_model(self, **kwargs):
+    session = PickableInferenceSession(self.onnx_file, **kwargs)
+    inputs = session.get_inputs()
+    input_cfg = inputs[0]
+    input_shape = input_cfg.shape
+    outputs = session.get_outputs()
+
+    if len(outputs) >= 5:
+        return RetinaFace(model_file=self.onnx_file, session=session)
+    elif input_shape[2] == 192 and input_shape[3] == 192:
+        return Landmark(model_file=self.onnx_file, session=session)
+    elif input_shape[2] == 96 and input_shape[3] == 96:
+        return Attribute(model_file=self.onnx_file, session=session)
+    elif len(inputs) == 2 and input_shape[2] == 128 and input_shape[3] == 128:
+        return INSwapper(model_file=self.onnx_file, session=session)
+    elif input_shape[2] == input_shape[3] and input_shape[2] >= 112 and input_shape[2] % 16 == 0:
+        return ArcFaceONNX(model_file=self.onnx_file, session=session)
+    else:
+        return None
+
+
+def patched_faceanalysis_init(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=None, **kwargs):
+    onnxruntime.set_default_logger_severity(3)
+    self.models = {}
+    self.model_dir = ensure_available('models', name, root=root)
+    onnx_files = glob.glob(osp.join(self.model_dir, '*.onnx'))
+    onnx_files = sorted(onnx_files)
+    for onnx_file in onnx_files:
+        model = model_zoo.get_model(onnx_file, **kwargs)
+        if model is None:
+            print('model not recognized:', onnx_file)
+        elif allowed_modules is not None and model.taskname not in allowed_modules:
+            print('model ignore:', onnx_file, model.taskname)
+            del model
+        elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules):
+            self.models[model.taskname] = model
+        else:
+            print('duplicated model task type, ignore:', onnx_file, model.taskname)
+            del model
+    assert 'detection' in self.models
+    self.det_model = self.models['detection']
+
+
+def patched_faceanalysis_prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)):
+    self.det_thresh = det_thresh
+    assert det_size is not None
+    self.det_size = det_size
+    for taskname, model in self.models.items():
+        if taskname == 'detection':
+            model.prepare(ctx_id, input_size=det_size, det_thresh=det_thresh)
+        else:
+            model.prepare(ctx_id)
+
+
+def patched_inswapper_init(self, model_file=None, session=None):
+    self.model_file = model_file
+    self.session = session
+    model = onnx.load(self.model_file)
+    graph = model.graph
+    self.emap = numpy_helper.to_array(graph.initializer[-1])
+    self.input_mean = 0.0
+    self.input_std = 255.0
+    if self.session is None:
+        self.session = onnxruntime.InferenceSession(self.model_file, None)
+    inputs = self.session.get_inputs()
+    self.input_names = []
+    for inp in inputs:
+        self.input_names.append(inp.name)
+    outputs = self.session.get_outputs()
+    output_names = []
+    for out in outputs:
+        output_names.append(out.name)
+    self.output_names = output_names
+    assert len(self.output_names) == 1
+    input_cfg = inputs[0]
+    input_shape = input_cfg.shape
+    self.input_shape = input_shape
+    self.input_size = tuple(input_shape[2:4][::-1])
+
+
+def patch_insightface(get_model, faceanalysis_init, faceanalysis_prepare, inswapper_init):
+    insightface.model_zoo.model_zoo.ModelRouter.get_model = get_model
+    insightface.app.FaceAnalysis.__init__ = faceanalysis_init
+    insightface.app.FaceAnalysis.prepare = faceanalysis_prepare
+    insightface.model_zoo.inswapper.INSwapper.__init__ = inswapper_init
+
+
+original_functions = [ModelRouter.get_model, FaceAnalysis.__init__, FaceAnalysis.prepare, INSwapper.__init__]
+patched_functions = [patched_get_model, patched_faceanalysis_init, patched_faceanalysis_prepare, patched_inswapper_init]
+
+
+def apply_logging_patch(console_log_level):
+    if console_log_level == 0:
+        patch_insightface(*patched_functions)
+        logger.setLevel(logging.WARNING)
+    elif console_log_level == 1:
+        patch_insightface(*patched_functions)
+        logger.setLevel(logging.STATUS)
+    elif console_log_level == 2:
+        patch_insightface(*original_functions)
+        logger.setLevel(logging.INFO)

reactor_utils.py

@@ -0,0 +1,160 @@
+import os
+from PIL import Image
+import numpy as np
+import torch
+import cv2
+import logging
+import hashlib
+from insightface.app.common import Face
+from safetensors.torch import save_file, safe_open
+from tqdm import tqdm
+import urllib.request
+
+
+def tensor_to_pil(img_tensor, batch_index=0):
+    # Convert tensor of shape [batch_size, channels, height, width] at the batch_index to PIL Image
+    img_tensor = img_tensor[batch_index].unsqueeze(0)
+    i = 255. * img_tensor.cpu().numpy()
+    img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
+    return img
+
+
+def batch_tensor_to_pil(img_tensor):
+    # Convert tensor of shape [batch_size, channels, height, width] to a list of PIL Images
+    return [tensor_to_pil(img_tensor, i) for i in range(img_tensor.shape[0])]
+
+
+def pil_to_tensor(image):
+    # Takes a PIL image and returns a tensor of shape [1, height, width, channels]
+    image = np.array(image).astype(np.float32) / 255.0
+    image = torch.from_numpy(image).unsqueeze(0)
+    if len(image.shape) == 3:  # If the image is grayscale, add a channel dimension
+        image = image.unsqueeze(-1)
+    return image
+
+
+def batched_pil_to_tensor(images):
+    # Takes a list of PIL images and returns a tensor of shape [batch_size, height, width, channels]
+    return torch.cat([pil_to_tensor(image) for image in images], dim=0)
+
+
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+
+    def _totensor(img, bgr2rgb, float32):
+        if img.shape[2] == 3 and bgr2rgb:
+            if img.dtype == 'float64':
+                img = img.astype('float32')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = torch.from_numpy(img.transpose(2, 0, 1))
+        if float32:
+            img = img.float()
+        return img
+
+    if isinstance(imgs, list):
+        return [_totensor(img, bgr2rgb, float32) for img in imgs]
+    else:
+        return _totensor(imgs, bgr2rgb, float32)
+
+
+def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
+
+    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
+        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
+
+    if torch.is_tensor(tensor):
+        tensor = [tensor]
+    result = []
+    for _tensor in tensor:
+        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
+        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
+
+        n_dim = _tensor.dim()
+        if n_dim == 4:
+            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if rgb2bgr:
+                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 3:
+            img_np = _tensor.numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if img_np.shape[2] == 1:  # gray image
+                img_np = np.squeeze(img_np, axis=2)
+            else:
+                if rgb2bgr:
+                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 2:
+            img_np = _tensor.numpy()
+        else:
+            raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
+        if out_type == np.uint8:
+            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
+            img_np = (img_np * 255.0).round()
+        img_np = img_np.astype(out_type)
+        result.append(img_np)
+    if len(result) == 1:
+        result = result[0]
+    return result
+
+def download(url, path, name):
+    request = urllib.request.urlopen(url)
+    total = int(request.headers.get('Content-Length', 0))
+    with tqdm(total=total, desc=f'[ReActor] Downloading {name} to {path}', unit='B', unit_scale=True, unit_divisor=1024) as progress:
+        urllib.request.urlretrieve(url, path, reporthook=lambda count, block_size, total_size: progress.update(block_size))
+
+def move_path(old_path, new_path):
+    if os.path.exists(old_path):
+        try:
+            models = os.listdir(old_path)
+            for model in models:
+                move_old_path = os.path.join(old_path, model)
+                move_new_path = os.path.join(new_path, model)
+                os.rename(move_old_path, move_new_path)
+            os.rmdir(old_path)
+        except Exception as e:
+            print(f"Error: {e}")
+            new_path = old_path
+
+def addLoggingLevel(levelName, levelNum, methodName=None):
+    if not methodName:
+        methodName = levelName.lower()
+
+    def logForLevel(self, message, *args, **kwargs):
+        if self.isEnabledFor(levelNum):
+            self._log(levelNum, message, args, **kwargs)
+
+    def logToRoot(message, *args, **kwargs):
+        logging.log(levelNum, message, *args, **kwargs)
+
+    logging.addLevelName(levelNum, levelName)
+    setattr(logging, levelName, levelNum)
+    setattr(logging.getLoggerClass(), methodName, logForLevel)
+    setattr(logging, methodName, logToRoot)
+
+def get_image_md5hash(image: Image.Image):
+    md5hash = hashlib.md5(image.tobytes())
+    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 load_face_model(filename: str):
+    face = {}
+    with safe_open(filename, framework="pt") as f:
+        for k in f.keys():
+            face[k] = f.get_tensor(k).numpy()
+    return Face(face)

requirements.txt

@@ -0,0 +1,4 @@
+insightface==0.7.3
+onnx>=1.14.0
+opencv-python>=4.7.0.72
+numpy