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# <div align="center">ReActor Extension API</div>
<div align="center">
[Built-in SD WebUI API](#built-in-sd-webui-api) | [External ReActor API](#external-reactor-api)
---
</div>
Gourieff's **ReActor** SD WebUI Extension allows to operate via API: both built-in and external (POST and GET requests).
## Built-in SD WebUI API
This API is actual if you use Automatic1111 stable-diffusion-webui.
First of all - check the [SD Web API Wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API) for how to use the API.
* Call `requests.get(url=f'{address}/sdapi/v1/script-info')` to find the args that ReActor needs;
* Define ReActor script args and add like this `"alwayson_scripts": {"reactor":{"args":args}}` in the payload;
* Call the API.
You can find the [full usage example](./example/api_example.py) with all the available parameters and discriptions in the "example" folder.
## External ReActor API
ReActor extension supports for external calls via POST or GET requests while your SD WebUI server is working.
> :warning: Source and Target images must be "base64".
Example:
```
curl -X POST \
'http://127.0.0.1:7860/reactor/image' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"source_image": "data:image/png;base64,/9j/4QAYRXhpZgAASUkqAAgAAAAAAAAAAAAAAP/sABFEdWNreQABAAQAAABQAAD/7g...",
"target_image": "data:image/png;base64,/9j/4QAYRXhpZgAASUkqAAgAAAAAAAAAAAAAAP/sABFEdWNreQABAAQAAABCAAD/7g...",
"source_faces_index": [0],
"face_index": [0],
"upscaler": "4x_NMKD-Siax_200k",
"scale": 2,
"upscale_visibility": 1,
"face_restorer": "CodeFormer",
"restorer_visibility": 1,
"restore_first": 1,
"model": "inswapper_128.onnx",
"gender_source": 0,
"gender_target": 0,
"save_to_file": 0,
"result_file_path": "",
"device": "CUDA",
"mask_face": 1,
"select_source": 1,
"face_model": "elena.safetensors",
"source_folder": "C:/faces",
"random_image": 1,
"upscale_force": 1
}'
```
* Set `"upscaler"` to `"None"` and `"scale"` to `1` if you don't need to upscale;
* Set `"save_to_file"` to `1` if you need to save result to a file;
* `"result_file_path"` is set to the `"outputs/api"` folder by default (please, create the folder beforehand to avoid any errors) with a timestamped filename; (output_YYYY-MM-DD_hh-mm-ss), you can set any specific path, e.g. `"C:/stable-diffusion-webui/outputs/api/output.png"`;
* Set `"mask_face"` to `1` if you want ReActor to mask the face or to `0` if want ReActor to create a bbox around the face;
* Set `"select_source"` to: 0 - Image, 1 - Face Model, 2 - Source Folder;
* Set `"face_model"` to the face model file you want to choose if you set `"select_source": 1`;
* Set `"source_folder"` to the path with source images (with faces you need as the results) if you set `"select_source": 2`;
* Set `"random_image"` to `1` if want ReActor to choose a random image from the path of `"source_folder"`;
* Set `"upscale_force"` to `1` if you want ReActor to upscale the image even if no face found.
You can find full usage examples with all the available parameters in the "example" folder: [cURL](./example/api_external.curl), [JSON](./example/api_external.json).
As a result you recieve a "base64" image:
```
{"image":"iVBORw0KGgoAAAANSUhEUgAABlAAAARQCAIAAAAdiYuqAAEAAElEQVR4nOz9+ZMlSXImBn6qau4vIjKzzr5wzwBCDrm/7f+/K7IHV3ZkhUIuyZHlkBhiMGig0Y0..."}
```
A list of available models can be seen by GET:
* http://127.0.0.1:7860/reactor/models
* http://127.0.0.1:7860/reactor/upscalers
* http://127.0.0.1:7860/reactor/facemodels

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English | [Русский](/README_RU.md)
# ReActor for Stable Diffusion
### The Fast and Simple FaceSwap Extension with a lot of improvements and without NSFW filter (uncensored, use it on your own [responsibility](#disclaimer))
---
<b>
<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>
</div>
---
<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
[A1111 WebUI / WebUI-Forge](#a1111) | [SD.Next](#sdnext) | [Google Colab SD WebUI](#colab)
<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):
- Install **Visual Studio 2022** (Community version, for example - you need this step to build some of dependencies):
https://visualstudio.microsoft.com/downloads/
- 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/
- 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, 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 (be patient, don't interrupt the process)
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 "Done!", just reload the UI
5. Enjoy!
<a name="sdnext">If you use [SD.Next](https://github.com/vladmandic/automatic):
1. Close (stop) your SD WebUI Server if it's running
2. (For Windows Users) See the [1st step](#a1111) for Automatic1111 (if you followed [this steps (sec. VIII)](#insightfacebuild) instead - go to the Step 5)
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`
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 (be patient, don't interrupt the process)
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
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
9. Run your SD.Next WebUI and enjoy!
<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, 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 (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"
4. Enjoy!
## Features
- Very fast and accurate **face replacement (face swap)** in images
- **Multiple faces support**
- **Gender detection**
- Ability to **save original images** (made before swapping)
- **Face restoration** of a swapped face
- **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**
- **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
- **CUDA** acceleration support since version 0.5.0
- **[API](/API.md) support**: both SD WebUI built-in and external (via POST/GET requests)
- **ComfyUI [support](https://github.com/Gourieff/comfyui-reactor-node)**
- **Mac M1/M2 [support](https://github.com/Gourieff/sd-webui-reactor/issues/42)**
- Console **log level control**
- **NSFW filter free** (this extension is aimed at highly developed intellectual people, not at perverts; our society must be oriented on its way towards the highest standards, not the lowest - this is the essence of development and evolution; so, my position is - that mature-minded people are clever enough to understand for themselves what is good and what is bad and take full responsibility for personal actions; for others - no "filters" will help until they do understand how Universe works)
## Usage
> Using this software you are agree with [disclaimer](#disclaimer)
1. Under "ReActor" drop-down menu, import an image containing a face;
2. Turn on the "Enable" checkbox;
3. That's it, now the generated result will have the face you selected.
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/example.jpg?raw=true" alt="example" width="808"/>
### 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.
### The result face is blurry
Use the "Restore Face" option. You can also try the "Upscaler" option or for more finer control, use an upscaler from the "Extras" tab.
You can also set the postproduction order (from 0.1.0 version):
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/pp-order.png?raw=true" alt="example"/>
*The old logic was the opposite (Upscale -> then Restore), resulting in worse face quality (and big texture differences) after upscaling.*
### There are multiple faces in result
Select the face numbers you wish to swap using the "Comma separated face number(s)" option for swap-source and result images. You can use different index order.
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/multiple-faces.png?raw=true" alt="example"/>
### ~~The result is totally black~~
~~This means NSFW filter detected that your image is NSFW.~~
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/IamSFW.jpg?raw=true" alt="IamSFW" width="50%"/>
### Img2Img
You can choose to activate the swap on the source image or on the generated image, or on both using the checkboxes. Activating on source image allows you to start from a given base and apply the diffusion process to it.
ReActor works with Inpainting - but only the masked part will be swapped.<br>Please use with the "Only masked" option for "Inpaint area" if you enabled "Upscaler". Otherwise use the upscale option via the Extras tab or via the Script loader (below the screen) with "SD upscale" or "Ultimate SD upscale".
### Extras Tab
From the version 0.5.0 you can use ReActor via the Extras Tab. It gives a superfast perfomance and ability to swap face2image avoiding SD pipeline that can cause smushing of original image's details
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/extras_tab.jpg?raw=true" alt="IamSFW"/>
## API
You can use ReActor with the built-in Webui API or via an external API.
Please follow **[this](/API.md)** page for the detailed instruction.
## Troubleshooting
### **I. "You should at least have one model in models directory"**
Please, check the path where "inswapper_128.onnx" model is stored. It must be inside the folder `stable-diffusion-webui\models\insightface`. Move the model there if it's stored in a different directory.
### **II. Any problems with installing Insightface or other dependencies**
(for Mac M1/M2 users) If you get errors when trying to install Insightface - please read https://github.com/Gourieff/sd-webui-reactor/issues/42
(for Windows Users) If you have VS C++ Build Tools or MS VS 2022 installed but still have a problem, then try the next step:
1. Close (stop) your SD WebUI Server and start it again
(for Any OS Users) If the problem still there, then do the following:
1. Close (stop) your SD WebUI Server if it's running
2. Go to (Windows)`venv\Lib\site-packages` folder or (MacOS/Linux)`venv/lib/python3.10/site-packages`
3. If you see any folders with names start from `~` (e.g. "~rotobuf") - delete them
4. Go to (Windows)`venv\Scripts` or (MacOS/Linux)`venv/bin`
5. Run Terminal or Console (cmd) for that folder and type `activate`
6. Update your pip at first: `pip install -U pip`
7. Then one-by-one:
- `pip install insightface==0.7.3`
- `pip install onnx`
- `pip install "onnxruntime-gpu>=1.16.1"`
- `pip install opencv-python`
- `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.
### **III. "TypeError: UpscaleOptions.init() got an unexpected keyword argument 'do_restore_first'"**
First of all - you need to disable any other Roop-based extensions:
- Go to 'Extensions -> Installed' tab and uncheck any Roop-based extensions except this one
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/roop-off.png?raw=true" alt="uncompatible-with-other-roop"/>
- Click 'Apply and restart UI'
Alternative solutions:
- https://github.com/Gourieff/sd-webui-reactor/issues/3#issuecomment-1615919243
- https://github.com/Gourieff/sd-webui-reactor/issues/39#issuecomment-1666559134 (can be actual, if you use Vladmandic SD.Next)
### **IV. "AttributeError: 'FaceSwapScript' object has no attribute 'enable'"**
Probably, you need to disable the "SD-CN-Animation" extension (or perhaps some another that causes the conflict)
### **V. "INVALID_PROTOBUF : Load model from <...>\models\insightface\inswapper_128.onnx failed:Protobuf parsing failed" OR "AttributeError: 'NoneType' object has no attribute 'get'" OR "AttributeError: 'FaceSwapScript' object has no attribute 'save_original'"**
This error may occur if there's smth wrong with the model file `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
### **VI. "ValueError: This ORT build has ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'] enabled" OR "ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled"**
1. Close (stop) your SD WebUI Server if it's running
2. Go to the (Windows)`venv\Lib\site-packages` or (MacOS/Linux)`venv/lib/python3.10/site-packages` and see if there are any folders with names start from "~" (for example "~rotobuf"), delete them
3. Go to the (Windows)`venv\Scripts` or (MacOS/Linux)`venv/bin` run Terminal or Console (cmd) there and type `activate`
4. Then:
- `python -m pip install -U pip`
- `pip uninstall -y onnxruntime onnxruntime-gpu onnxruntime-silicon onnxruntime-extensions`
- `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!
### **VII. "ImportError: cannot import name 'builder' from 'google.protobuf.internal'"**
1. Close (stop) your SD WebUI Server if it's running
2. Go to the (Windows)`venv\Lib\site-packages` or (MacOS/Linux)`venv/lib/python3.10/site-packages` and see if there are any folders with names start from "~" (for example "~rotobuf"), delete them
3. Go to the "google" folder (inside the "site-packages") and delete any folders there with names start from "~"
4. Go to the (Windows)`venv\Scripts` or (MacOS/Linux)`venv/bin` run Terminal or Console (cmd) there and type `activate`
5. Then:
- `python -m pip install -U pip`
- `pip uninstall protobuf`
- `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
<a name="insightfacebuild">
### **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
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`<br>OR<br>(A1111 Portable) Run CMD
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`<br>OR<br>(A1111 Portable)`system\python\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl`
6. Enjoy!
### **IX. 07-August-23 Update problem**
If after `git pull` you see the message: `Merge made by the 'recursive' strategy` and then when you check `git status` you see `Your branch is ahead of 'origin/main' by`
Please do the next:
Inside the folder `extensions\sd-webui-reactor` run Terminal or Console (cmd) and then:
- `git reset f48bdf1 --hard`
- `git pull`
OR
Just delete the folder `sd-webui-reactor` inside the `extensions` directory and then run Terminal or Console (cmd) and type `git clone https://github.com/Gourieff/sd-webui-reactor`
### **X. StabilityMatrix Issues**
If you encounter any issues with installing this extension in the StabilityMatrix package manager - read here how to solve: https://github.com/Gourieff/sd-webui-reactor/issues/129#issuecomment-1768210875
## Updating
A good and quick way to check for Extensions updates: https://github.com/Gourieff/sd-webui-extensions-updater
## ComfyUI
You can use ReActor with ComfyUI.<br>
For the installation instruction follow the [ReActor Node repo](https://github.com/Gourieff/comfyui-reactor-node)
## 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 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.**
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**

420
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<div align="center">
<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-0.7.1_alpha2-lightgreen?style=for-the-badge&labelColor=darkgreen)
<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>
[![Commit activity](https://img.shields.io/github/commit-activity/t/Gourieff/sd-webui-reactor/main?cacheSeconds=0)](https://github.com/Gourieff/sd-webui-reactor/commits/main)
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![License](https://img.shields.io/github/license/Gourieff/sd-webui-reactor)
[English](/README.md) | Русский
# ReActor для Stable Diffusion
### Расширение для быстрой и простой замены лиц на любых изображениях. Без фильтра цензуры, 18+, используйте под вашу собственную [ответственность](#disclaimer)
---
<b>
<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>
</div>
---
<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">
## Установка
[A1111 WebUI / WebUI-Forge](#a1111) | [SD.Next](#sdnext) | [Google Colab SD WebUI](#colab)
<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):
- Установите **Visual Studio 2022** (Например, версию Community - этот шаг нужен для правильной компиляции библиотеки Insightface):
https://visualstudio.microsoft.com/downloads/
- 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/
- 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", 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 (наберитесь терпения, не прерывайте процесс)
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 "Done!", просто перезагрузите UI, нажав на "Reload UI"
5. Готово!
<a name="sdnext">Если вы используете [SD.Next](https://github.com/vladmandic/automatic):
1. Закройте (остановите) SD WebUI Сервер, если он запущен
2. (Для пользователей Windows) Смотрите [Шаг 1](#a1111) для Automatic1111 (если же вы следовали [данным шагам (пункт VIII)](#insightfacebuild) вместо этого - переходите к Шагу 5)
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`
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 (наберитесь терпения, не прерывайте процесс)
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
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`
9. Готово, можете запустить SD.Next WebUI!
<a name="colab">Если вы используете [Cagliostro Colab UI](https://github.com/Linaqruf/sd-notebook-collection):
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 (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"
4. Готово!
<a name="features">
## Возможности
- Быстрая и точна **замена лиц (faceswap)** на изображении
- **Поддержка нескольких лиц**
- **Определение пола**
- Функция **сохранения оригинального изображения** (сгенерированного до замены лица)
- **Восстановление лица** после замены
- **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**
- **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
- **Поддержка CUDA**, начиная с версии 0.5.0
- **Поддержка [API](/API.md)**: как встроенного в SD WebUI, так и внешнего (через POST/GET запросы)
- **[Поддержка](https://github.com/Gourieff/comfyui-reactor-node) ComfyUI**
- **[Поддержка](https://github.com/Gourieff/sd-webui-reactor/issues/42) компьютеров Mac M1/M2**
- **Регулировка уровня логов** консоли
- **Без NSFW фильтров** (данное расширение адресовано высокоразвитым интеллектуальным людям, а не извращенцам; наше общество должно быть ориентировано на своём пути на высшие стандарты, а не на низшие - в этом состоит суть развития и эволюции человеческого общества; поэтому, моя позиция такова - что зрелые умом люди достаточно разумны, чтобы понимать, что есть хорошо, а что плохо и нести полную ответственность за собственные действия; для прочих - никакие "фильтры" не помогут, пока эти люди сами не поймут, как работает Вселенная)
<a name="usage">
## Использование
> Используя данное программное обеспечение, вы соглашаетесь с [ответственностью](#disclaimer)
1. В раскрывающимся меню "ReActor" импортируйте изображение, содержащее лицо;
2. Установите флажок "Enable";
3. Готово, теперь результат будет иметь то лицо, которое вы выбрали.
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/example.jpg?raw=true" alt="example" width="808"/>
### Индексы Лиц (Face Indexes)
ReActor определяет лица на изображении в следующей последовательности:<br>
слева-направо, сверху-вниз.
Если вам нужно заменить определенное лицо, вы можете указать индекс для исходного (source, с лицом) и входного (input, где будет замена лица) изображений.
Индекс первого обнаруженного лица - 0.
Вы можете задать индексы в том порядке, который вам нужен.<br>
Например: 0,1,2 (для Source); 1,0,2 (для Input).<br>
Это означает, что: второе лицо из Input (индекс = 1) будет заменено первым лицом из Source (индекс = 0) и так далее.
### Определение Пола
Вы можете обозначить, какой пол нужно определять на изображении.<br>
ReActor заменит только то лицо, которое удовлетворяет заданному условию.
### Если лицо получилось нечётким
Используйте опцию "Restore Face". Также можете попробовать опцию "Upscaler". Для более точного контроля параметров используйте Upscaler во вкладке "Extras".
Также вы можете установить порядок постобработки (начиная с версии 0.1.0):
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/pp-order.png?raw=true" alt="example"/>
*Прежняя логика была противоположенной (Upscale -> затем Restore), что приводило к более худшему качеству изображения лица (а также к значительной разнице текстур) после увеличения.*
### Результат имеет несколько лиц
Выберите номера лиц, которые нужно поменять, используя поля "Comma separated face number(s)" для исходного изображения лица и для результата. Можно устанавливать любой, необходимый вам, порядок лиц.
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/multiple-faces.png?raw=true" alt="example"/>
### ~~Результат получился чёрным~~
~~Это значит, что сработал NSFW фильтр.~~
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/IamSFW.jpg?raw=true" alt="IamSFW" width="50%"/>
### Img2Img
Используйте эту вкладку, чтобы заменить лицо на уже готовом изображении (флажок "Swap in source image") или на сгенерированном на основе готового (флажок "Swap in generated image").
Inpainting также работает, но замена лица происходит только в области маски.<br>Пожалуйста, используйте с опцией "Only masked" для "Inpaint area", если вы применяете "Upscaler". Иначе, используйте функцию увеличения (апскейла) через вкладку "Extras" или через опциональный загрузчик "Script" (внизу экрана), применив "SD upscale" или "Ultimate SD upscale".
### Extras
Начиная с версии 0.5.0, вы можете использовать ReActor через вкладку Extras, что даёт очень быструю производительность и возможность замены лиц в обход пайплайна SD, что иногда вызывает размытие или искажение деталей оригинального изображения
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/extras_tab.jpg?raw=true" alt="IamSFW"/>
## API
Вы можете использовать ReActor как со встроенным SD Webui API так и через внешнее API.
Подробная инструкция **[здесь](/API.md)**.
<a name="troubleshooting">
## Устранение проблем
### **I. "You should at least have one model in models directory"**
Проверьте путь, где хранится модель "inswapper_128.onnx". Файл должен находиться в папке `stable-diffusion-webui\models\insightface`. Переместите модель туда, если она находится в какой-то иной директории.
### **II. Какие-либо проблемы с установкой Insightface или прочих пакетов**
(Для пользователей Mac M1/M2) Если вы получаете ошибки в ходе установки Insightface - читайте https://github.com/Gourieff/sd-webui-reactor/issues/42
(Для пользователей Windows) Если VS C++ Build Tools или MS VS 2022 установлены но вы видите ошибки, связанные с отсутствием Insightface, попробуйте следующее:
1. Закройте (остановите) SD WebUI Сервер и запустите его снова (возможно, не прошла инициализация пакета после его установки)
(Для пользователей любых ОС) Попробуйте следующее:
1. Закройте (остановите) SD WebUI Сервер, если он запущен
2. Перейдите в папку (Windows)`venv\Lib\site-packages` или (MacOS/Linux)`venv/lib/python3.10/site-packages`
3. Если вы видите к-л папки с именами, начинающимися с `~` (например, "~rotobuf") - удалите их
4. Перейдите в (Windows)`venv\Scripts` или (MacOS/Linux)`venv/bin`
5. Откройте Терминал или Консоль (cmd) для этой папки и выполните `activate`
6. Для начала обновите pip: `pip install -U pip`
7. Далее:
- `pip install insightface==0.7.3`
- `pip install onnx`
- `pip install "onnxruntime-gpu>=1.16.1"`
- `pip install opencv-python`
- `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".
### **III. "TypeError: UpscaleOptions.init() got an unexpected keyword argument 'do_restore_first'"**
Для начала отключите любые другие Roop-подобные расширения:
- Перейдите в 'Extensions -> Installed' и снимите флажок с ненужных:
<img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/roop-off.png?raw=true" alt="uncompatible-with-other-roop"/>
- Нажмите 'Apply and restart UI'
Альтернативные решения:
- https://github.com/Gourieff/sd-webui-reactor/issues/3#issuecomment-1615919243
- https://github.com/Gourieff/sd-webui-reactor/issues/39#issuecomment-1666559134 (актуально для Vladmandic SD.Next)
### **IV. "AttributeError: 'FaceSwapScript' object has no attribute 'enable'"**
Отключите расширение "SD-CN-Animation" (или какое-либо другое, вызывающее конфликт)
### **V. "INVALID_PROTOBUF : Load model from <...>\models\insightface\inswapper_128.onnx failed:Protobuf parsing failed" ИЛИ "AttributeError: 'NoneType' object has no attribute 'get'" ИЛИ "AttributeError: 'FaceSwapScript' object has no attribute 'save_original'"**
Эта ошибка появляется, если что-то не так с файлом модели `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.
### **VI. "ValueError: This ORT build has ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'] enabled" ИЛИ "ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled"**
1. Закройте (остановите) SD WebUI Сервер, если он запущен
2. Перейдите в (Windows)`venv\Lib\site-packages` или (MacOS/Linux)`venv/lib/python3.10/site-packages` и посмотрите, если там папки с именам, начинающимися на "~" (например, "~rotobuf"), удалите их
3. Перейдите в (Windows)`venv\Scripts` или (MacOS/Linux)`venv/bin`, откройте Терминал или Консоль (cmd) и выполните `activate`
4. Затем:
- `python -m pip install -U pip`
- `pip uninstall -y onnxruntime onnxruntime-gpu onnxruntime-silicon onnxruntime-extensions`
- `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!
### **VII. "ImportError: cannot import name 'builder' from 'google.protobuf.internal'"**
1. Закройте (остановите) SD WebUI Сервер, если он запущен
2. Перейдите в (Windows)`venv\Lib\site-packages` или (MacOS/Linux)`venv/lib/python3.10/site-packages` и посмотрите, если там папки с именам, начинающимися на "~" (например, "~rotobuf"), удалите их
3. Перейдите в папку "google" (внутри "site-packages") и удалите любые папки с именам, начинающимися на "~"
4. Перейдите в (Windows)`venv\Scripts` или (MacOS/Linux)`venv/bin`, откройте Терминал или Консоль (cmd) и выполните `activate`
5. Затем:
- `python -m pip install -U pip`
- `pip uninstall protobuf`
- `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.
<a name="insightfacebuild">
### **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
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`<br>ИЛИ<br>(A1111 Portable) Откройте Консоль (CMD)
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`<br>ИЛИ<br>(A1111 Portable)`system\python\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl`
6. Готово!
### **IX. Ошибка обновления 07-Август-23**
Если после очередного `git pull` вы получили сообщение: `Merge made by the 'recursive' strategy` и затем, когда проверяете статус репозитория через `git status`, вы видите `Your branch is ahead of 'origin/main' by`
Выполните следующее:
Внутри папки `extensions\sd-webui-reactor` запустите Терминал или Консоль (cmd) и затем:
- `git reset f48bdf1 --hard`
- `git pull`
ИЛИ:
Полностью удалите папку `sd-webui-reactor` внутри директории `extensions`, запустите Терминал или Консоль (cmd) и выполните `git clone https://github.com/Gourieff/sd-webui-reactor`
### **X. Ошибки установки в StabilityMatrix**
Если вы столкнулись с ошибками при установки данного расширения в пакетном менеджере StabilityMatrix - изучите информацию по ссылке: https://github.com/Gourieff/sd-webui-reactor/issues/129#issuecomment-1768210875
<a name="updating">
## Обновление
Самый простой и удобный способ обновления SD WebUI и расширений: https://github.com/Gourieff/sd-webui-extensions-updater
## ComfyUI
Вы можете использовать ReActor с ComfyUI<br>
Инструкция здесь: [ReActor Node](https://github.com/Gourieff/comfyui-reactor-node)
<a name="disclaimer">
## Ответственность
Это программное обеспечение призвано стать продуктивным вкладом в быстрорастущую медиаиндустрию на основе генеративных сетей и искусственного интеллекта. Данное ПО поможет художникам в решении таких задач, как анимация собственного персонажа или использование персонажа в качестве модели для одежды и т.д.
Разработчики этого программного обеспечения осведомлены о возможных неэтичных применениях и обязуются принять против этого превентивные меры. Мы продолжим развивать этот проект в позитивном направлении, придерживаясь закона и этики.
Подразумевается, что пользователи этого программного обеспечения будут использовать его ответственно, соблюдая локальное законодательство. Если используется лицо реального человека, пользователь обязан получить согласие заинтересованного лица и четко указать, что это дипфейк при размещении контента в Интернете. **Разработчики и Со-авторы данного программного обеспечения не несут ответственности за действия конечных пользователей.**
Используя данное расширение, вы соглашаетесь не создавать материалы, которые:
- нарушают какие-либо действующие законы тех или иных государств или международных организаций;
- причиняют какой-либо вред человеку или лицам;
- пропагандируют любую информацию (как общедоступную, так и личную) или изображения (как общедоступные, так и личные), которые могут быть направлены на причинение вреда;
- используются для распространения дезинформации;
- нацелены на уязвимые группы людей.
Данное программное обеспечение использует предварительно обученные модели `buffalo_l` и `inswapper_128.onnx`, представленные разработчиками [InsightFace](https://github.com/deepinsight/insightface/). Эти модели распространяются при следующих условиях:
[Перевод из текста лицензии insighface](https://github.com/deepinsight/insightface/tree/master/python-package): Предварительно обученные модели InsightFace доступны только для некоммерческих исследовательских целей. Сюда входят как модели с автоматической загрузкой, так и модели, загруженные вручную.
Пользователи данного программного обеспечения должны строго соблюдать данные условия использования. Разработчики и Со-авторы данного программного продукта не несут ответственности за неправильное использование предварительно обученных моделей InsightFace.
Обратите внимание: если вы собираетесь использовать это программное обеспечение в каких-либо коммерческих целях, вам необходимо будет обучить свои собственные модели или найти модели, которые можно использовать в коммерческих целях.
### Хэш файлов моделей
#### Безопасные для использования модели имеют следующий хэш:
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
```
**Пожалуйста, сравните хэш, если вы скачиваете данные модели из непроверенных источников**

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import base64, io, requests, json
from PIL import Image, PngImagePlugin
from datetime import datetime, date
address = 'http://127.0.0.1:7860'
input_file = "extensions\sd-webui-reactor\example\IamSFW.jpg" # Input file path
time = datetime.now()
today = date.today()
current_date = today.strftime('%Y-%m-%d')
current_time = time.strftime('%H-%M-%S')
output = 'outputs/api/output_'+current_date+'_'+current_time # Output file path + name index
try:
im = Image.open(input_file)
except Exception as e:
print(e)
finally:
print(im)
img_bytes = io.BytesIO()
im.save(img_bytes, format='PNG')
img_base64 = base64.b64encode(img_bytes.getvalue()).decode('utf-8')
# ReActor arguments:
args=[
img_base64, #0
True, #1 Enable ReActor
'0', #2 Comma separated face number(s) from swap-source image
'0', #3 Comma separated face number(s) for target image (result)
'C:\stable-diffusion-webui\models\insightface\inswapper_128.onnx', #4 model path
'CodeFormer', #4 Restore Face: None; CodeFormer; GFPGAN
1, #5 Restore visibility value
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
1.5, #9 Upscaler scale value
1, #10 Upscaler visibility (if scale = 1)
False, #11 Swap in source image
True, #12 Swap in generated image
1, #13 Console Log Level (0 - min, 1 - med or 2 - max)
0, #14 Gender Detection (Source) (0 - No, 1 - Female Only, 2 - Male Only)
0, #15 Gender Detection (Target) (0 - No, 1 - Female Only, 2 - Male Only)
False, #16 Save the original image(s) made before swapping
0.8, #17 CodeFormer Weight (0 = maximum effect, 1 = minimum effect), 0.5 - by default
False, #18 Source Image Hash Check, True - by default
False, #19 Target Image Hash Check, False - 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
# requests.get(url=f'{address}/sdapi/v1/script-info')
prompt = "(8k, best quality, masterpiece, highly detailed:1.1),realistic photo of fantastic happy woman,hairstyle of blonde and red short bob hair,modern clothing,cinematic lightning,film grain,dynamic pose,bokeh,dof"
neg = "ng_deepnegative_v1_75t,(badhandv4:1.2),(worst quality:2),(low quality:2),(normal quality:2),lowres,(bad anatomy),(bad hands),((monochrome)),((grayscale)),(verybadimagenegative_v1.3:0.8),negative_hand-neg,badhandv4,nude,naked,(strabismus),cross-eye,heterochromia,((blurred))"
payload = {
"prompt": prompt,
"negative_prompt": neg,
"seed": -1,
"sampler_name": "DPM++ 2M Karras",
"steps": 15,
"cfg_scale": 7,
"width": 512,
"height": 768,
"restore_faces": False,
"alwayson_scripts": {"reactor":{"args":args}}
}
try:
print('Working... Please wait...')
result = requests.post(url=f'{address}/sdapi/v1/txt2img', json=payload)
except Exception as e:
print(e)
finally:
print('Done! Saving file...')
if result is not None:
r = result.json()
n = 0
for i in r['images']:
image = Image.open(io.BytesIO(base64.b64decode(i.split(",",1)[0])))
png_payload = {
"image": "data:image/png;base64," + i
}
response2 = requests.post(url=f'{address}/sdapi/v1/png-info', json=png_payload)
pnginfo = PngImagePlugin.PngInfo()
pnginfo.add_text("parameters", response2.json().get("info"))
output_file = output+'_'+str(n)+'_.png'
try:
image.save(output_file, pnginfo=pnginfo)
except Exception as e:
print(e)
finally:
print(f'{output_file} is saved\nAll is done!')
n += 1
else:
print('Something went wrong...')

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import subprocess
import os, sys
from typing import Any
import pkg_resources
from tqdm import tqdm
import urllib.request
from packaging import version as pv
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
models_path = os.path.abspath("models")
BASE_PATH = os.path.dirname(os.path.realpath(__file__))
req_file = os.path.join(BASE_PATH, "requirements.txt")
models_dir = os.path.join(models_path, "insightface")
# DEPRECATED:
# models_dir_old = os.path.join(models_path, "roop")
# if os.path.exists(models_dir_old):
# if not os.listdir(models_dir_old) and (not os.listdir(models_dir) or not os.path.exists(models_dir)):
# os.rename(models_dir_old, models_dir)
# else:
# import shutil
# for file in os.listdir(models_dir_old):
# shutil.move(os.path.join(models_dir_old, file), os.path.join(models_dir, file))
# try:
# os.rmdir(models_dir_old)
# except Exception as e:
# print(f"OSError: {e}")
model_url = "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx"
model_name = os.path.basename(model_url)
model_path = os.path.join(models_dir, model_name)
def pip_install(*args):
subprocess.run([sys.executable, "-m", "pip", "install", *args])
def pip_uninstall(*args):
subprocess.run([sys.executable, "-m", "pip", "uninstall", "-y", *args])
def is_installed (
package: str, version: str | None = None, strict: bool = True
):
has_package = None
try:
has_package = pkg_resources.get_distribution(package)
if has_package 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 False
except Exception as e:
print(f"Error: {e}")
return False
def download(url, path):
request = urllib.request.urlopen(url)
total = int(request.headers.get('Content-Length', 0))
with tqdm(total=total, desc='Downloading...', 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):
os.makedirs(models_dir)
if not os.path.exists(model_path):
download(model_url, model_path)
# print("ReActor preheating...", end=' ')
last_device = None
first_run = False
available_devices = ["CPU", "CUDA"]
try:
last_device_log = os.path.join(BASE_PATH, "last_device.txt")
with open(last_device_log) as f:
last_device = f.readline().strip()
if last_device not in available_devices:
last_device = None
except:
last_device = "CPU"
first_run = True
with open(os.path.join(BASE_PATH, "last_device.txt"), "w") as txt:
txt.write(last_device)
with open(req_file) as file:
install_count = 0
ort = "onnxruntime-gpu"
import torch
cuda_version = None
try:
if torch.cuda.is_available():
cuda_version = torch.version.cuda
print(f"CUDA {cuda_version}")
if first_run or last_device is None:
last_device = "CUDA"
elif torch.backends.mps.is_available() or hasattr(torch,'dml') or hasattr(torch,'privateuseone'):
ort = "onnxruntime"
# to prevent errors when ORT-GPU is installed but we want ORT instead:
if first_run:
pip_uninstall("onnxruntime", "onnxruntime-gpu")
# just in case:
if last_device == "CUDA" or last_device is None:
last_device = "CPU"
else:
if last_device == "CUDA" or last_device is None:
last_device = "CPU"
with open(os.path.join(BASE_PATH, "last_device.txt"), "w") as txt:
txt.write(last_device)
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
pip_install(ort, "-U")
except Exception as e:
print(e)
print(f"\nERROR: Failed to install {ort} - ReActor won't start")
raise e
# print(f"Device: {last_device}")
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):
install_count += 1
pip_install(package)
except Exception as e:
print(e)
print(f"\nERROR: Failed to install {package} - ReActor won't start")
raise e
if install_count > 0:
print(f"""
+---------------------------------+
--- PLEASE, RESTART the Server! ---
+---------------------------------+
""")

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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 Executable file
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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 Executable file
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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

229
reactor_ui/reactor_main_ui.py Executable file
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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):
try: # GR3.x
return gr.Dropdown.update(
value=selected, choices=get_model_names(get_facemodels)
)
except: # GR4.x
return gr.Dropdown(
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
try: # GR3.x
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)
}
except: # GR4.x
return {
control_col_1: gr.Column(visible=False),
control_col_2: gr.Column(visible=False),
control_col_3: gr.Column(visible=True),
# save_original: gr.Checkbox.update(value=False,visible=False),
imgs_hash_clear: gr.Button(visible=True)
}
if evt.index == 0:
try: # GR3.x
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)
}
except: # GR4.x
return {
control_col_1: gr.Column(visible=True),
control_col_2: gr.Column(visible=False),
control_col_3: gr.Column(visible=False),
# save_original: gr.Checkbox.update(value=SAVE_ORIGINAL,visible=show_br),
imgs_hash_clear: gr.Button(visible=False)
}
if evt.index == 1:
try: # GR3.x
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)
}
except: # GR4.x
return {
control_col_1: gr.Column(visible=False),
control_col_2: gr.Column(visible=True),
control_col_3: gr.Column(visible=False),
# save_original: gr.Checkbox.update(value=SAVE_ORIGINAL,visible=show_br),
imgs_hash_clear: gr.Button(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:
with gr.Row():
selected_tab = gr.Textbox('tab_single', visible=False)
with gr.Tabs() as tab_single:
with gr.Tab('Single'):
img = gr.Image(
type="pil",
label="Single Source Image",
)
with gr.Tab('Multiple') as tab_multiple:
imgs = gr.Files(
label=f"Multiple Source Images{msgs['extra_multiple_source']}",
file_types=["image"],
)
tab_single.select(fn=lambda: 'tab_single', inputs=[], outputs=[selected_tab])
tab_multiple.select(fn=lambda: 'tab_multiple', inputs=[], outputs=[selected_tab])
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, selected_tab, 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 Executable file
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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 Executable file
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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 similar 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 Executable file
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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

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requirements.txt Executable file
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albumentations==1.4.3
insightface==0.7.3
onnx>=1.14.0
opencv-python>=4.7.0.72

125
scripts/console_log_patch.py Executable file
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import os.path as osp
import glob
import logging
import insightface
from insightface.model_zoo.model_zoo import ModelRouter, PickableInferenceSession, get_default_providers
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 patched_get_default_providers():
return ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']
def patch_insightface(get_default_providers, get_model, faceanalysis_init, faceanalysis_prepare, inswapper_init):
insightface.model_zoo.model_zoo.get_default_providers = get_default_providers
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 = [patched_get_default_providers, ModelRouter.get_model, FaceAnalysis.__init__, FaceAnalysis.prepare, INSwapper.__init__]
patched_functions = [patched_get_default_providers, patched_get_model, patched_faceanalysis_init, patched_faceanalysis_prepare, patched_inswapper_init]
def apply_logging_patch(console_logging_level):
if console_logging_level == 0:
patch_insightface(*patched_functions)
logger.setLevel(logging.WARNING)
elif console_logging_level == 1:
patch_insightface(*patched_functions)
logger.setLevel(logging.STATUS)
elif console_logging_level == 2:
patch_insightface(*original_functions)
logger.setLevel(logging.INFO)

194
scripts/reactor_api.py Executable file
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'''
Thanks SpenserCai for the original version of the roop api script
-----------------------------------
--- ReActor External API v1.0.8a ---
-----------------------------------
'''
import os, glob
from datetime import datetime, date
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
import asyncio
from concurrent.futures import ThreadPoolExecutor
# from concurrent.futures.process import ProcessPoolExecutor
# from contextlib import asynccontextmanager
# import multiprocessing
# from modules.api.models import *
from modules import scripts, shared
from modules.api import api
import gradio as gr
from scripts.reactor_swapper import EnhancementOptions, swap_face, DetectionOptions
from scripts.reactor_logger import logger
from scripts.reactor_helpers import get_facemodels
# @asynccontextmanager
# async def lifespan(app: FastAPI):
# app.state.executor = ProcessPoolExecutor(max_workers=4)
# yield
# app.state.executor.shutdown()
# app = FastAPI(lifespan=lifespan)
# def run_app(a: FastAPI):
# global app
# a = app
# return a
# _executor_tp = ThreadPoolExecutor(max_workers=8)
# def entry_point():
# _executor_pp = ProcessPoolExecutor(max_workers=8)
# pool = multiprocessing.Pool(4)
async def run_event(app, fn, *args):
loop = asyncio.get_event_loop()
return await loop.run_in_executor(app.state.executor, fn, *args)
def default_file_path():
time = datetime.now()
today = date.today()
current_date = today.strftime('%Y-%m-%d')
current_time = time.strftime('%H-%M-%S')
output_file = 'output_'+current_date+'_'+current_time+'.png'
return os.path.join(os.path.abspath("outputs/api"), output_file)
def get_face_restorer(name):
for restorer in shared.face_restorers:
if restorer.name() == name:
return restorer
return None
def get_upscaler(name):
for upscaler in shared.sd_upscalers:
if upscaler.name == name:
return upscaler
return None
def get_models():
models_path = os.path.join(scripts.basedir(), "models/insightface/*")
models = glob.glob(models_path)
models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
return models
def get_full_model(model_name):
models = get_models()
for model in models:
model_path = os.path.split(model)
if model_path[1] == model_name:
return model
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):
app.state.executor = ThreadPoolExecutor(max_workers=8)
@app.post("/reactor/image")
async def reactor_image(
source_image: str = Body("",title="Source Face Image"),
target_image: str = Body("",title="Target 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)"),
upscaler: str = Body("None",title="Upscaler"),
scale: float = Body(1,title="Scale by"),
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"),
restorer_visibility: float = Body(1,title="Restore visibility value"),
codeformer_weight: float = Body(0.5,title="CodeFormer Weight"),
restore_first: int = Body(1,title="Restore face -> Then upscale, 1 - True, 0 - False"),
model: str = Body("inswapper_128.onnx",title="Model"),
gender_source: int = Body(0,title="Gender Detection (Source) (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"),
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)"),
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) if select_source == 0 else None
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
f_index = face_index
gender_s = gender_source
gender_t = gender_target
restore_first_bool = True if restore_first == 1 else False
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)
if use_model is None:
Exception("Model not found")
args = [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,_,_ = pool.map(swap_face, *args)
result,_,_ = await run_event(app,swap_face,*args)
# 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)
if alpha is not None:
result = result.convert("RGBA")
result.putalpha(alpha)
if save_to_file == 1:
if result_file_path == "":
result_file_path = default_file_path()
try:
file_format = os.path.split(result_file_path)[1].split(".")[1]
result.save(result_file_path, format=file_format)
logger.status("Result has been saved to: %s", result_file_path)
except Exception as e:
logger.error("Error while saving result: %s",e)
return {"image": api.encode_pil_to_base64(result)}
@app.get("/reactor/models")
async def reactor_models():
model_names = [os.path.split(model)[1] for model in get_models()]
return {"models": model_names}
@app.get("/reactor/upscalers")
async def reactor_upscalers():
names = [upscaler.name for upscaler in shared.sd_upscalers]
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:
import modules.script_callbacks as script_callbacks
script_callbacks.on_app_started(reactor_api)
except:
pass

147
scripts/reactor_entities/face.py Executable file
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import traceback
import cv2
import numpy as np
from modules import images
from PIL import Image
from scripts.reactor_entities.rect import Point, Rect
class FaceArea:
def __init__(self, entire_image: np.ndarray, face_area: Rect, face_margin: float, face_size: int, upscaler: str):
self.face_area = face_area
self.center = face_area.center
left, top, right, bottom = face_area.to_square()
self.left, self.top, self.right, self.bottom = self.__ensure_margin(
left, top, right, bottom, entire_image, face_margin
)
self.width = self.right - self.left
self.height = self.bottom - self.top
self.image = self.__crop_face_image(entire_image, face_size, upscaler)
self.face_size = face_size
self.scale_factor = face_size / self.width
self.face_area_on_image = self.__get_face_area_on_image()
self.landmarks_on_image = self.__get_landmarks_on_image()
def __get_face_area_on_image(self):
left = int((self.face_area.left - self.left) * self.scale_factor)
top = int((self.face_area.top - self.top) * self.scale_factor)
right = int((self.face_area.right - self.left) * self.scale_factor)
bottom = int((self.face_area.bottom - self.top) * self.scale_factor)
return self.__clip_values(left, top, right, bottom)
def __get_landmarks_on_image(self):
landmarks = []
if self.face_area.landmarks is not None:
for landmark in self.face_area.landmarks:
landmarks.append(
Point(
int((landmark.x - self.left) * self.scale_factor),
int((landmark.y - self.top) * self.scale_factor),
)
)
return landmarks
def __crop_face_image(self, entire_image: np.ndarray, face_size: int, upscaler: str):
cropped = entire_image[self.top : self.bottom, self.left : self.right, :]
if upscaler:
return images.resize_image(0, Image.fromarray(cropped), face_size, face_size, upscaler)
else:
return Image.fromarray(cv2.resize(cropped, dsize=(face_size, face_size)))
def __ensure_margin(self, left: int, top: int, right: int, bottom: int, entire_image: np.ndarray, margin: float):
entire_height, entire_width = entire_image.shape[:2]
side_length = right - left
margin = min(min(entire_height, entire_width) / side_length, margin)
diff = int((side_length * margin - side_length) / 2)
top = top - diff
bottom = bottom + diff
left = left - diff
right = right + diff
if top < 0:
bottom = bottom - top
top = 0
if left < 0:
right = right - left
left = 0
if bottom > entire_height:
top = top - (bottom - entire_height)
bottom = entire_height
if right > entire_width:
left = left - (right - entire_width)
right = entire_width
return left, top, right, bottom
def get_angle(self) -> float:
landmarks = getattr(self.face_area, "landmarks", None)
if landmarks is None:
return 0
eye1 = getattr(landmarks, "eye1", None)
eye2 = getattr(landmarks, "eye2", None)
if eye2 is None or eye1 is None:
return 0
try:
dx = eye2.x - eye1.x
dy = eye2.y - eye1.y
if dx == 0:
dx = 1
angle = np.arctan(dy / dx) * 180 / np.pi
if dx < 0:
angle = (angle + 180) % 360
return angle
except Exception:
print(traceback.format_exc())
return 0
def rotate_face_area_on_image(self, angle: float):
center = [
(self.face_area_on_image[0] + self.face_area_on_image[2]) / 2,
(self.face_area_on_image[1] + self.face_area_on_image[3]) / 2,
]
points = [
[self.face_area_on_image[0], self.face_area_on_image[1]],
[self.face_area_on_image[2], self.face_area_on_image[3]],
]
angle = np.radians(angle)
rot_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])
points = np.array(points) - center
points = np.dot(points, rot_matrix.T)
points += center
left, top, right, bottom = (int(points[0][0]), int(points[0][1]), int(points[1][0]), int(points[1][1]))
left, right = (right, left) if left > right else (left, right)
top, bottom = (bottom, top) if top > bottom else (top, bottom)
width, height = right - left, bottom - top
if width < height:
left, right = left - (height - width) // 2, right + (height - width) // 2
elif height < width:
top, bottom = top - (width - height) // 2, bottom + (width - height) // 2
return self.__clip_values(left, top, right, bottom)
def __clip_values(self, *args):
result = []
for val in args:
if val < 0:
result.append(0)
elif val > self.face_size:
result.append(self.face_size)
else:
result.append(val)
return tuple(result)

78
scripts/reactor_entities/rect.py Executable file
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from typing import Dict, NamedTuple, Tuple
import numpy as np
class Point(NamedTuple):
x: int
y: int
class Landmarks(NamedTuple):
eye1: Point
eye2: Point
nose: Point
mouth1: Point
mouth2: Point
class Rect:
def __init__(
self,
left: int,
top: int,
right: int,
bottom: int,
tag: str = "face",
landmarks: Landmarks = None,
attributes: Dict[str, str] = {},
) -> None:
self.tag = tag
self.left = left
self.top = top
self.right = right
self.bottom = bottom
self.center = int((right + left) / 2)
self.middle = int((top + bottom) / 2)
self.width = right - left
self.height = bottom - top
self.size = self.width * self.height
self.landmarks = landmarks
self.attributes = attributes
@classmethod
def from_ndarray(
cls,
face_box: np.ndarray,
tag: str = "face",
landmarks: Landmarks = None,
attributes: Dict[str, str] = {},
) -> "Rect":
left, top, right, bottom, *_ = list(map(int, face_box))
return cls(left, top, right, bottom, tag, landmarks, attributes)
def to_tuple(self) -> Tuple[int, int, int, int]:
return self.left, self.top, self.right, self.bottom
def to_square(self):
left, top, right, bottom = self.to_tuple()
width = right - left
height = bottom - top
if width % 2 == 1:
right = right + 1
width = width + 1
if height % 2 == 1:
bottom = bottom + 1
height = height + 1
diff = int(abs(width - height) / 2)
if width > height:
top = top - diff
bottom = bottom + diff
else:
left = left - diff
right = right + diff
return left, top, right, bottom

739
scripts/reactor_faceswap.py Executable file
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import os, glob
import gradio as gr
from PIL import Image
from typing import List
import modules.scripts as scripts
from modules.upscaler import Upscaler, UpscalerData
from modules import scripts, shared, images, scripts_postprocessing
from modules.processing import (
Processed,
StableDiffusionProcessing,
StableDiffusionProcessingImg2Img,
)
from modules.face_restoration import FaceRestoration
from modules.images import save_image
from reactor_ui import (
ui_main,
ui_upscale,
ui_tools,
ui_settings,
ui_detection,
)
from scripts.reactor_logger import logger
from scripts.reactor_swapper import (
EnhancementOptions,
DetectionOptions,
swap_face,
check_process_halt,
reset_messaged,
)
from scripts.reactor_version import version_flag, app_title
from scripts.console_log_patch import apply_logging_patch
from scripts.reactor_helpers import (
make_grid,
set_Device,
get_SDNEXT,
)
from scripts.reactor_globals import SWAPPER_MODELS_PATH #, DEVICE, DEVICE_LIST
def IA_cap(cond: bool, label: str=""):
return None
try:
from modules.ui_components import InputAccordion
NO_IA = False
except:
NO_IA = True
InputAccordion = IA_cap
def check_old_webui():
return NO_IA
class FaceSwapScript(scripts.Script):
def title(self):
return f"{app_title}"
def show(self, is_img2img):
return scripts.AlwaysVisible
def ui(self, is_img2img):
with (
gr.Accordion(f"{app_title}", open=False) if check_old_webui() else InputAccordion(False, label=f"{app_title}") as enable
):
# SD.Next or A1111 1.52:
if get_SDNEXT() or check_old_webui():
enable = gr.Checkbox(False, label="Enable")
# enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
gr.Markdown(f"<sup>The Fast and Simple FaceSwap Extension - {version_flag}</sup>")
# TAB MAIN
msgs: dict = {
"extra_multiple_source": "",
}
img, imgs, selected_tab, 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)
# TAB DETECTION
det_thresh, det_maxnum = ui_detection.show()
# TAB UPSCALE
restore_first, upscaler_name, upscaler_scale, upscaler_visibility, upscale_force = ui_upscale.show()
# TAB TOOLS
ui_tools.show()
# TAB SETTINGS
model, device, console_logging_level, source_hash_check, target_hash_check = ui_settings.show()
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>")
return [
img,
enable,
source_faces_index,
faces_index,
model,
face_restorer_name,
face_restorer_visibility,
restore_first,
upscaler_name,
upscaler_scale,
upscaler_visibility,
swap_in_source,
swap_in_generated,
console_logging_level,
gender_source,
gender_target,
save_original,
codeformer_weight,
source_hash_check,
target_hash_check,
device,
mask_face,
select_source,
face_model,
source_folder,
imgs,
random_image,
upscale_force,
det_thresh,
det_maxnum,
selected_tab,
]
@property
def upscaler(self) -> UpscalerData:
for upscaler in shared.sd_upscalers:
if upscaler.name == self.upscaler_name:
return upscaler
return None
@property
def face_restorer(self) -> FaceRestoration:
for face_restorer in shared.face_restorers:
if face_restorer.name() == self.face_restorer_name:
return face_restorer
return None
@property
def enhancement_options(self) -> EnhancementOptions:
return EnhancementOptions(
do_restore_first=self.restore_first,
scale=self.upscaler_scale,
upscaler=self.upscaler,
face_restorer=self.face_restorer,
upscale_visibility=self.upscaler_visibility,
restorer_visibility=self.face_restorer_visibility,
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
)
def process(
self,
p: StableDiffusionProcessing,
img,
enable,
source_faces_index,
faces_index,
model,
face_restorer_name,
face_restorer_visibility,
restore_first,
upscaler_name,
upscaler_scale,
upscaler_visibility,
swap_in_source,
swap_in_generated,
console_logging_level,
gender_source,
gender_target,
save_original,
codeformer_weight,
source_hash_check,
target_hash_check,
device,
mask_face,
select_source,
face_model,
source_folder,
imgs,
random_image,
upscale_force,
det_thresh,
det_maxnum,
selected_tab,
):
self.enable = enable
if self.enable:
logger.debug("*** Start process")
reset_messaged()
if check_process_halt():
return
global SWAPPER_MODELS_PATH
if selected_tab == "tab_single":
self.source = img
else:
self.source = None
self.face_restorer_name = face_restorer_name
self.upscaler_scale = upscaler_scale
self.upscaler_visibility = upscaler_visibility
self.face_restorer_visibility = face_restorer_visibility
self.restore_first = restore_first
self.upscaler_name = upscaler_name
self.swap_in_source = swap_in_source
self.swap_in_generated = swap_in_generated
self.model = os.path.join(SWAPPER_MODELS_PATH,model)
self.console_logging_level = console_logging_level
self.gender_source = gender_source
self.gender_target = gender_target
self.save_original = save_original
self.codeformer_weight = codeformer_weight
self.source_hash_check = source_hash_check
self.target_hash_check = target_hash_check
self.device = device
self.mask_face = mask_face
self.select_source = select_source
self.face_model = face_model
self.source_folder = source_folder
if selected_tab == "tab_single":
self.source_imgs = None
else:
self.source_imgs = imgs
self.random_image = random_image
self.upscale_force = upscale_force
self.det_thresh=det_thresh
self.det_maxnum=det_maxnum
if self.gender_source is None or self.gender_source == "No":
self.gender_source = 0
if self.gender_target is None or self.gender_target == "No":
self.gender_target = 0
self.source_faces_index = [
int(x) for x in source_faces_index.strip(",").split(",") if x.isnumeric()
]
self.faces_index = [
int(x) for x in faces_index.strip(",").split(",") if x.isnumeric()
]
if len(self.source_faces_index) == 0:
self.source_faces_index = [0]
if len(self.faces_index) == 0:
self.faces_index = [0]
if self.save_original is None:
self.save_original = False
if self.source_hash_check is None:
self.source_hash_check = True
if self.target_hash_check is None:
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)
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:
logger.debug("*** Check process")
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)):
if len(p.init_images) > 1:
logger.status("Swap in %s", i)
result, output, swapped = swap_face(
self.source,
p.init_images[i],
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,
select_source=self.select_source,
face_model = self.face_model,
source_folder = None,
source_imgs = None,
random_image = False,
detection_options=self.detection_options,
)
p.init_images[i] = result
# result_path = get_image_path(p.init_images[i], p.outpath_samples, "", p.all_seeds[i], p.all_prompts[i], "txt", p=p, suffix="-swapped")
# if len(output) != 0:
# with open(result_path, 'w', encoding="utf8") as f:
# f.writelines(output)
if shared.state.interrupted or shared.state.skipped:
return
else:
logger.error("Please provide a source face")
return
def postprocess(self, p: StableDiffusionProcessing, processed: Processed, *args):
if self.enable:
logger.debug("*** Check postprocess - before IF")
reset_messaged()
if check_process_halt():
return
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)):
logger.debug("*** Check postprocess - after IF")
postprocess_run: bool = True
orig_images : List[Image.Image] = processed.images[processed.index_of_first_image:]
orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:]
result_images: List = processed.images
# result_info: List = processed.infotexts
if self.swap_in_generated:
logger.status("Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
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)):
if check_process_halt():
postprocess_run = False
break
if len(orig_images) > 1:
logger.status("Swap in %s", i)
result, output, swapped = swap_face(
self.source,
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,
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,
)
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:
result_images.append(result)
suffix = "-swapped"
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)")
elif result is None:
logger.error("Cannot create a result image")
# if len(output) != 0:
# split_fullfn = os.path.splitext(img_path[0])
# fullfn = split_fullfn[0] + ".txt"
# with open(fullfn, 'w', encoding="utf8") as f:
# f.writelines(output)
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
# processed.infotexts = result_info
elif self.select_source == 0 and self.source is not None and self.source_imgs is not None:
logger.debug("*** Check postprocess - after ELIF")
if self.result is not None:
orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:]
processed.images = [self.result]
try:
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:
logger.error("Cannot save a result image - please, check SD WebUI Settings (Saving and Paths)")
else:
logger.error("Cannot create a result image")
def postprocess_batch(self, p, *args, **kwargs):
if self.enable and not self.save_original:
logger.debug("*** Check postprocess_batch")
images = kwargs["images"]
def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
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
job_count = shared.state.job_count
if current_job_number == job_count:
reset_messaged()
if check_process_halt():
return
# 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)
image: Image.Image = script_pp.image
result, output, swapped = swap_face(
self.source,
image,
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,
select_source=self.select_source,
face_model = self.face_model,
source_folder = None,
source_imgs = None,
random_image = False,
detection_options=self.detection_options,
)
self.result = result
try:
pp = scripts_postprocessing.PostprocessedImage(result)
pp.info = {}
p.extra_generation_params.update(pp.info)
script_pp.image = pp.image
# if len(output) != 0:
# result_path = get_image_path(script_pp.image, p.outpath_samples, "", p.all_seeds[0], p.all_prompts[0], "txt", p=p, suffix="-swapped")
# if len(output) != 0:
# with open(result_path, 'w', encoding="utf8") as f:
# f.writelines(output)
except:
logger.error("Cannot create a result image")
class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
name = 'ReActor'
order = 20000
def ui(self):
with (
gr.Accordion(f"{app_title}", open=False) if check_old_webui() else InputAccordion(False, label=f"{app_title}") as enable
):
# with ui_components.InputAccordion(False, label=f"{app_title}") as enable:
# with gr.Accordion(f"{app_title}", open=False):
# SD.Next or A1111 1.52:
if get_SDNEXT() or check_old_webui():
enable = gr.Checkbox(False, label="Enable")
# enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
gr.Markdown(f"<span style='display:block;font-size:0.75em;margin-bottom:-24px;'>The Fast and Simple FaceSwap Extension - {version_flag}</span>")
# TAB MAIN
msgs: dict = {
"extra_multiple_source": "",
}
img, imgs, selected_tab, 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)
# TAB DETECTION
det_thresh, det_maxnum = ui_detection.show()
# TAB UPSCALE
restore_first, upscaler_name, upscaler_scale, upscaler_visibility, upscale_force = ui_upscale.show(show_br=False)
# TAB TOOLS
ui_tools.show()
# TAB SETTINGS
model, device, console_logging_level, source_hash_check, target_hash_check = ui_settings.show(hash_check_block=False)
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>")
args = {
'img': img,
'enable': enable,
'source_faces_index': source_faces_index,
'faces_index': faces_index,
'model': model,
'face_restorer_name': face_restorer_name,
'face_restorer_visibility': face_restorer_visibility,
'restore_first': restore_first,
'upscaler_name': upscaler_name,
'upscaler_scale': upscaler_scale,
'upscaler_visibility': upscaler_visibility,
'console_logging_level': console_logging_level,
'gender_source': gender_source,
'gender_target': gender_target,
'codeformer_weight': codeformer_weight,
'device': device,
'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,
'selected_tab': selected_tab,
}
return args
@property
def upscaler(self) -> UpscalerData:
for upscaler in shared.sd_upscalers:
if upscaler.name == self.upscaler_name:
return upscaler
return None
@property
def face_restorer(self) -> FaceRestoration:
for face_restorer in shared.face_restorers:
if face_restorer.name() == self.face_restorer_name:
return face_restorer
return None
@property
def enhancement_options(self) -> EnhancementOptions:
return EnhancementOptions(
do_restore_first=self.restore_first,
scale=self.upscaler_scale,
upscaler=self.upscaler,
face_restorer=self.face_restorer,
upscale_visibility=self.upscaler_visibility,
restorer_visibility=self.face_restorer_visibility,
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
)
def process(self, pp: scripts_postprocessing.PostprocessedImage, **args):
if args['enable']:
reset_messaged()
if check_process_halt():
return
global SWAPPER_MODELS_PATH
if args['selected_tab'] == "tab_single":
self.source = args['img']
else:
self.source = None
self.face_restorer_name = args['face_restorer_name']
self.upscaler_scale = args['upscaler_scale']
self.upscaler_visibility = args['upscaler_visibility']
self.face_restorer_visibility = args['face_restorer_visibility']
self.restore_first = args['restore_first']
self.upscaler_name = args['upscaler_name']
self.model = os.path.join(SWAPPER_MODELS_PATH, args['model'])
self.console_logging_level = args['console_logging_level']
self.gender_source = args['gender_source']
self.gender_target = args['gender_target']
self.codeformer_weight = args['codeformer_weight']
self.device = args['device']
self.mask_face = args['mask_face']
self.select_source = args['select_source']
self.face_model = args['face_model']
self.source_folder = args['source_folder']
if args['selected_tab'] == "tab_single":
self.source_imgs = None
else:
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']
if self.gender_source is None or self.gender_source == "No":
self.gender_source = 0
if self.gender_target is None or self.gender_target == "No":
self.gender_target = 0
self.source_faces_index = [
int(x) for x in args['source_faces_index'].strip(",").split(",") if x.isnumeric()
]
self.faces_index = [
int(x) for x in args['faces_index'].strip(",").split(",") if x.isnumeric()
]
if len(self.source_faces_index) == 0:
self.source_faces_index = [0]
if len(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
job_count = shared.state.job_count
if current_job_number == job_count:
reset_messaged()
set_Device(self.device)
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)
# if self.select_source != 2:
image: Image.Image = pp.image
# 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,
image,
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=True,
target_hash_check=True,
device=self.device,
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,
)
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:
if hasattr(pp, 'extra_images'):
image = result[0]
pp.extra_images.extend(result[1:])
else:
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:
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
logger.status("---Done!---")
except Exception:
logger.error("Cannot create a result image")
else:
logger.error("Please provide a source face")

42
scripts/reactor_globals.py Executable file
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import os
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
BASE_PATH = os.path.join(Path(__file__).parents[1])
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():
try:
LAST_DEVICE_PATH = os.path.join(BASE_PATH, "last_device.txt")
with open(LAST_DEVICE_PATH) as f:
device = f.readline().strip()
if device not in DEVICE_LIST:
print(f"Error: Device {device} is not in DEVICE_LIST")
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
DEVICE = updateDevice()

235
scripts/reactor_helpers.py Executable file
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import os, glob, random
from collections import Counter
from PIL import Image
from math import isqrt, ceil
from typing import List
import logging
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 import shared, script_callbacks
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):
global DEVICE
DEVICE = value
with open(os.path.join(BASE_PATH, "last_device.txt"), "w") as txt:
txt.write(DEVICE)
def get_Device():
global 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):
# Count the occurrences of each image size in the image_list
size_counter = Counter(image.size for image in image_list)
# Get the most common image size (size with the highest count)
common_size = size_counter.most_common(1)[0][0]
# Filter the image_list to include only images with the common size
image_list = [image for image in image_list if image.size == common_size]
# Get the dimensions (width and height) of the common size
size = common_size
# If there are more than one image in the image_list
if len(image_list) > 1:
num_images = len(image_list)
# Calculate the number of rows and columns for the grid
rows = isqrt(num_images)
cols = ceil(num_images / rows)
# Calculate the size of the square image
square_size = (cols * size[0], rows * size[1])
# Create a new RGB image with the square size
square_image = Image.new("RGB", square_size)
# Paste each image onto the square image at the appropriate position
for i, image in enumerate(image_list):
row = i // cols
col = i % cols
square_image.paste(image, (col * size[0], row * size[1]))
# Return the resulting square image
return square_image
# Return None if there are no images or only one image in the image_list
return None
def get_image_path(image, path, basename, seed=None, prompt=None, extension='png', p=None, suffix=""):
namegen = FilenameGenerator(p, seed, prompt, image)
save_to_dirs = shared.opts.save_to_dirs
if save_to_dirs:
dirname = namegen.apply(shared.opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
path = os.path.join(path, dirname)
os.makedirs(path, exist_ok=True)
if seed is None:
file_decoration = ""
elif shared.opts.save_to_dirs:
file_decoration = shared.opts.samples_filename_pattern or "[seed]"
else:
file_decoration = shared.opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
file_decoration = namegen.apply(file_decoration) + suffix
add_number = shared.opts.save_images_add_number or file_decoration == ''
if file_decoration != "" and add_number:
file_decoration = f"-{file_decoration}"
if add_number:
basecount = get_next_sequence_number(path, basename)
fullfn = None
for i in range(500):
fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
if not os.path.exists(fullfn):
break
else:
fullfn = os.path.join(path, f"{file_decoration}.{extension}")
pnginfo = {}
params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo)
# script_callbacks.before_image_saved_callback(params)
fullfn = params.filename
fullfn_without_extension, extension = os.path.splitext(params.filename)
if hasattr(os, 'statvfs'):
max_name_len = os.statvfs(path).f_namemax
fullfn_without_extension = fullfn_without_extension[:max_name_len - max(4, len(extension))]
params.filename = fullfn_without_extension + extension
fullfn = params.filename
return fullfn
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 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]

86
scripts/reactor_inferencers/bisenet_mask_generator.py Executable file
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from typing import List, Tuple
import cv2
import modules.shared as shared
import numpy as np
import torch
from facexlib.parsing import init_parsing_model
from facexlib.utils.misc import img2tensor
from torchvision.transforms.functional import normalize
from scripts.reactor_inferencers.mask_generator import MaskGenerator
class BiSeNetMaskGenerator(MaskGenerator):
def __init__(self) -> None:
self.mask_model = init_parsing_model(device=shared.device)
def name(self):
return "BiSeNet"
def generate_mask(
self,
face_image: np.ndarray,
face_area_on_image: Tuple[int, int, int, int],
affected_areas: List[str],
mask_size: int,
use_minimal_area: bool,
fallback_ratio: float = 0.25,
**kwargs,
) -> np.ndarray:
# original_face_image = face_image
face_image = face_image.copy()
face_image = face_image[:, :, ::-1]
if use_minimal_area:
face_image = MaskGenerator.mask_non_face_areas(face_image, face_area_on_image)
h, w, _ = face_image.shape
if w != 512 or h != 512:
rw = (int(w * (512 / w)) // 8) * 8
rh = (int(h * (512 / h)) // 8) * 8
face_image = cv2.resize(face_image, dsize=(rw, rh))
face_tensor = img2tensor(face_image.astype("float32") / 255.0, float32=True)
normalize(face_tensor, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
face_tensor = torch.unsqueeze(face_tensor, 0).to(shared.device)
with torch.no_grad():
face = self.mask_model(face_tensor)[0]
face = face.squeeze(0).cpu().numpy().argmax(0)
face = face.copy().astype(np.uint8)
mask = self.__to_mask(face, affected_areas)
if mask_size > 0:
mask = cv2.dilate(mask, np.ones((5, 5), np.uint8), iterations=mask_size)
if w != 512 or h != 512:
mask = cv2.resize(mask, dsize=(w, h))
# """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
def __to_mask(self, face: np.ndarray, affected_areas: List[str]) -> np.ndarray:
keep_face = "Face" in affected_areas
keep_neck = "Neck" in affected_areas
keep_hair = "Hair" in affected_areas
keep_hat = "Hat" in affected_areas
mask = np.zeros((face.shape[0], face.shape[1], 3), dtype=np.uint8)
num_of_class = np.max(face)
for i in range(1, num_of_class + 1):
index = np.where(face == i)
if i < 14 and keep_face:
mask[index[0], index[1], :] = [255, 255, 255]
elif i == 14 and keep_neck:
mask[index[0], index[1], :] = [255, 255, 255]
elif i == 17 and keep_hair:
mask[index[0], index[1], :] = [255, 255, 255]
elif i == 18 and keep_hat:
mask[index[0], index[1], :] = [255, 255, 255]
return mask

36
scripts/reactor_inferencers/mask_generator.py Executable file
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from abc import ABC, abstractmethod
from typing import Tuple
import cv2
import numpy as np
class MaskGenerator(ABC):
@abstractmethod
def name(self) -> str:
pass
@abstractmethod
def generate_mask(
self,
face_image: np.ndarray,
face_area_on_image: Tuple[int, int, int, int],
**kwargs,
) -> np.ndarray:
pass
@staticmethod
def mask_non_face_areas(image: np.ndarray, face_area_on_image: Tuple[int, int, int, int]) -> np.ndarray:
left, top, right, bottom = face_area_on_image
image = image.copy()
image[:top, :] = 0
image[bottom:, :] = 0
image[:, :left] = 0
image[:, right:] = 0
return image
@staticmethod
def calculate_mask_coverage(mask: np.ndarray):
gray_mask = cv2.cvtColor(mask, cv2.COLOR_RGB2GRAY)
non_black_pixels = np.count_nonzero(gray_mask)
total_pixels = gray_mask.size
return non_black_pixels / total_pixels

55
scripts/reactor_logger.py Executable file
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import logging
import copy
import sys
from modules import shared
from scripts.reactor_globals import IS_RUN
from scripts.reactor_helpers import addLoggingLevel
class ColoredFormatter(logging.Formatter):
COLORS = {
"DEBUG": "\033[0;36m", # CYAN
"STATUS": "\033[38;5;173m", # Calm ORANGE
"INFO": "\033[0;32m", # GREEN
"WARNING": "\033[0;33m", # YELLOW
"ERROR": "\033[0;31m", # RED
"CRITICAL": "\033[0;37;41m", # WHITE ON RED
"RESET": "\033[0m", # RESET COLOR
}
def format(self, record):
colored_record = copy.copy(record)
levelname = colored_record.levelname
seq = self.COLORS.get(levelname, self.COLORS["RESET"])
colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
return super().format(colored_record)
# Create a new logger
logger = logging.getLogger("ReActor")
logger.propagate = False
# Add Custom Level
addLoggingLevel("STATUS", logging.INFO + 5)
# Add handler if we don't have one.
if not logger.handlers:
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
ColoredFormatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s","%H:%M:%S")
)
logger.addHandler(handler)
# Configure logger
loglevel_string = getattr(shared.cmd_opts, "reactor_loglevel", "INFO")
loglevel = getattr(logging, loglevel_string.upper(), "info")
logger.setLevel(loglevel)
def set_Run(value):
global IS_RUN
IS_RUN = value
def get_Run():
global IS_RUN
return IS_RUN

816
scripts/reactor_swapper.py Executable file
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import copy
import os
from dataclasses import dataclass
from typing import List, Union
import cv2
import numpy as np
from PIL import Image
from scipy import stats
import insightface
from insightface.app.common import Face
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
try: # A1111
from modules import codeformer_model, gfpgan_model
except: # SD.Next
from modules.postprocess import codeformer_model, gfpgan_model
set_SDNEXT()
from modules.upscaler import UpscalerData
from modules.shared import state
from scripts.reactor_logger import logger
from reactor_modules.reactor_mask import apply_face_mask
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
models_path = os.path.abspath("models")
import warnings
np.warnings = warnings
np.warnings.filterwarnings('ignore')
DEVICE = get_Device()
if DEVICE == "CUDA":
PROVIDERS = ["CUDAExecutionProvider"]
else:
PROVIDERS = ["CPUExecutionProvider"]
@dataclass
class EnhancementOptions:
do_restore_first: bool = True
scale: int = 1
upscaler: UpscalerData = None
upscale_visibility: float = 0.5
face_restorer: FaceRestoration = None
restorer_visibility: float = 0.5
codeformer_weight: float = 0.5
upscale_force: bool = False
@dataclass
class DetectionOptions:
det_thresh: float = 0.5
det_maxnum: int = 0
MESSAGED_STOPPED = False
MESSAGED_SKIPPED = False
def reset_messaged():
global MESSAGED_STOPPED, MESSAGED_SKIPPED
if not state.interrupted:
MESSAGED_STOPPED = False
if not state.skipped:
MESSAGED_SKIPPED = False
def check_process_halt(msgforced: bool = False):
global MESSAGED_STOPPED, MESSAGED_SKIPPED
if state.interrupted:
if not MESSAGED_STOPPED or msgforced:
logger.status("Stopped by User")
MESSAGED_STOPPED = True
return True
if state.skipped:
if not MESSAGED_SKIPPED or msgforced:
logger.status("Skipped by User")
MESSAGED_SKIPPED = True
return True
return False
FS_MODEL = None
ANALYSIS_MODEL = None
MASK_MODEL = None
CURRENT_FS_MODEL_PATH = None
CURRENT_MASK_MODEL_PATH = None
SOURCE_FACES = None
SOURCE_IMAGE_HASH = None
TARGET_FACES = 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():
global ANALYSIS_MODEL
if ANALYSIS_MODEL is None:
ANALYSIS_MODEL = insightface.app.FaceAnalysis(
name="buffalo_l", providers=PROVIDERS, root=os.path.join(models_path, "insightface") # note: allowed_modules=['detection', 'genderage']
)
return ANALYSIS_MODEL
def getFaceSwapModel(model_path: str):
global FS_MODEL
global CURRENT_FS_MODEL_PATH
if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
CURRENT_FS_MODEL_PATH = model_path
FS_MODEL = insightface.model_zoo.get_model(model_path, providers=PROVIDERS)
return FS_MODEL
def restore_face(image: Image, enhancement_options: EnhancementOptions):
result_image = image
if check_process_halt(msgforced=True):
return result_image
if enhancement_options.face_restorer is not None:
original_image = result_image.copy()
numpy_image = np.array(result_image)
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, w=enhancement_options.codeformer_weight
)
else: # GFPGAN:
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)
result_image = Image.blend(
original_image, restored_image, enhancement_options.restorer_visibility
)
return result_image
def upscale_image(image: Image, enhancement_options: EnhancementOptions):
result_image = image
if check_process_halt(msgforced=True):
return result_image
if enhancement_options.upscaler is not None and enhancement_options.upscaler.name != "None":
original_image = result_image.copy()
logger.status(
"Upscaling with %s scale = %s",
enhancement_options.upscaler.name,
enhancement_options.scale,
)
result_image = enhancement_options.upscaler.scaler.upscale(
original_image, enhancement_options.scale, enhancement_options.upscaler.data_path
)
if enhancement_options.scale == 1:
result_image = Image.blend(
original_image, result_image, enhancement_options.upscale_visibility
)
return result_image
def enhance_image(image: Image, enhancement_options: EnhancementOptions):
result_image = image
if check_process_halt(msgforced=True):
return result_image
if enhancement_options.do_restore_first:
result_image = restore_face(result_image, enhancement_options)
result_image = upscale_image(result_image, enhancement_options)
else:
result_image = upscale_image(result_image, enhancement_options)
result_image = restore_face(result_image, enhancement_options)
return result_image
def enhance_image_and_mask(image: Image.Image, enhancement_options: EnhancementOptions,target_img_orig:Image.Image,entire_mask_image:Image.Image)->Image.Image:
result_image = image
if check_process_halt(msgforced=True):
return result_image
if enhancement_options.do_restore_first:
result_image = restore_face(result_image, enhancement_options)
result_image = Image.composite(result_image,target_img_orig,entire_mask_image)
result_image = upscale_image(result_image, enhancement_options)
else:
result_image = upscale_image(result_image, enhancement_options)
entire_mask_image = Image.fromarray(cv2.resize(np.array(entire_mask_image),result_image.size, interpolation=cv2.INTER_AREA)).convert("L")
result_image = Image.composite(result_image,target_img_orig,entire_mask_image)
result_image = restore_face(result_image, enhancement_options)
return result_image
def get_gender(face, face_index):
gender = [
x.sex
for x in face
]
gender.reverse()
try:
face_gender = gender[face_index]
except:
logger.error("Gender Detection: No face with index = %s was found", face_index)
return "None"
return face_gender
def get_face_gender(
face,
face_index,
gender_condition,
operated: str,
gender_detected,
):
face_gender = gender_detected
if face_gender == "None":
return None, 0
logger.status("%s Face %s: Detected Gender -%s-", operated, face_index, face_gender)
if (gender_condition == 1 and face_gender == "F") or (gender_condition == 2 and face_gender == "M"):
logger.status("OK - Detected Gender matches Condition")
try:
return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
except IndexError:
return None, 0
else:
logger.status("WRONG - Detected Gender doesn't match Condition")
return sorted(face, key=lambda x: x.bbox[0])[face_index], 1
def get_face_age(face, face_index):
age = [
x.age
for x in face
]
age.reverse()
try:
face_age = age[face_index]
except:
logger.error("Age Detection: No face with index = %s was found", face_index)
return "None"
return face_age
def half_det_size(det_size):
logger.status("Trying to halve 'det_size' parameter")
return (det_size[0] // 2, det_size[1] // 2)
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])
face_analyser = copy.deepcopy(getAnalysisModel())
face_analyser.prepare(ctx_id=0, det_thresh=det_thresh, det_size=det_size)
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, det_thresh=0.5, det_maxnum=0):
buffalo_path = os.path.join(models_path, "insightface/models/buffalo_l.zip")
if os.path.exists(buffalo_path):
os.remove(buffalo_path)
face_age = "None"
try:
face_age = get_face_age(face, face_index)
except:
logger.error("Cannot detect any Age for Face index = %s", face_index)
face_gender = "None"
try:
face_gender = get_gender(face, face_index)
gender_detected = face_gender
face_gender = "Female" if face_gender == "F" else ("Male" if face_gender == "M" else "None")
except:
logger.error("Cannot detect any Gender for Face index = %s", face_index)
if gender_source != 0:
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size)
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)
return faces, wrong_gender, face_age, face_gender
if gender_target != 0:
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size)
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)
return faces, wrong_gender, face_age, face_gender
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size)
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:
return sorted(face, key=lambda x: x.bbox[0])[face_index], 0, face_age, face_gender
except IndexError:
return None, 0, face_age, face_gender
def swap_face(
source_img: Image.Image,
target_img: Image.Image,
model: Union[str, None] = None,
source_faces_index: List[int] = [0],
faces_index: List[int] = [0],
enhancement_options: Union[EnhancementOptions, None] = None,
gender_source: int = 0,
gender_target: int = 0,
source_hash_check: bool = True,
target_hash_check: bool = False,
device: str = "CPU",
mask_face: bool = False,
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,
):
global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS, SOURCE_FACES_LIST, SOURCE_IMAGE_LIST_HASH
result_image = target_img
PROVIDERS = ["CUDAExecutionProvider"] if device == "CUDA" else ["CPUExecutionProvider"]
if check_process_halt():
return result_image, [], 0
if model is not None:
if isinstance(source_img, str): # source_img is a base64 string
import base64, io
if 'base64,' in source_img: # check if the base64 string has a data URL scheme
# split the base64 string to get the actual base64 encoded image data
base64_data = source_img.split('base64,')[-1]
# decode base64 string to bytes
img_bytes = base64.b64decode(base64_data)
else:
# if no data URL scheme, just decode
img_bytes = base64.b64decode(source_img)
source_img = Image.open(io.BytesIO(img_bytes))
target_img = 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))
output: List = []
output_info: str = ""
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:
source_image_md5hash = get_image_md5hash(source_img)
if SOURCE_IMAGE_HASH is None:
SOURCE_IMAGE_HASH = source_image_md5hash
source_image_same = False
else:
source_image_same = True if SOURCE_IMAGE_HASH == source_image_md5hash else False
if not source_image_same:
SOURCE_IMAGE_HASH = source_image_md5hash
logger.info("Source Image MD5 Hash = %s", SOURCE_IMAGE_HASH)
logger.info("Source Image the Same? %s", source_image_same)
if SOURCE_FACES is None or not source_image_same:
logger.status("Analyzing Source Image...")
source_faces = analyze_faces(source_img, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
SOURCE_FACES = source_faces
elif source_image_same:
logger.status("Using Hashed Source Face(s) Model...")
source_faces = SOURCE_FACES
else:
logger.status("Analyzing Source Image...")
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 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)
logger.status("Detecting Source Face, Index = %s", source_faces_index[0])
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":
logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
output_info = f"SourceFaceIndex={source_faces_index[0]};Age={source_age};Gender={source_gender}\n"
output.append(output_info)
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,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
face_swapper = getFaceSwapModel(model)
source_face_idx = 0
for face_num in faces_index:
if check_process_halt():
return result_image, [], 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])
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":
logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
output_info = f"SourceFaceIndex={source_faces_index[source_face_idx]};Age={source_age};Gender={source_gender}\n"
output.append(output_info)
source_face_idx += 1
if source_face is not None and wrong_gender == 0:
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, det_thresh=detection_options.det_thresh, det_maxnum=detection_options.det_maxnum)
if target_age != "None" or target_gender != "None":
logger.status("Detected: -%s- y.o. %s", target_age, target_gender)
output_info = f"TargetFaceIndex={face_num};Age={target_age};Gender={target_gender}\n"
output.append(output_info)
if target_face is not None and wrong_gender == 0:
logger.status("Swapping Source into Target")
swapped_image = face_swapper.get(result, target_face, source_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)
else:
result = swapped_image
swapped += 1
elif wrong_gender == 1:
wrong_gender = 0
if source_face_idx == len(source_faces_index):
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
if enhancement_options is not None and len(source_faces_index) > 1:
result_image = enhance_image(result_image, enhancement_options)
return result_image, output, swapped
else:
logger.status(f"No target face found for {face_num}")
elif wrong_gender == 1:
wrong_gender = 0
if source_face_idx == len(source_faces_index):
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
if enhancement_options is not None and len(source_faces_index) > 1:
result_image = enhance_image(result_image, enhancement_options)
return result_image, output, swapped
else:
logger.status(f"No source face found for face number {source_face_idx}.")
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
if (enhancement_options is not None and swapped > 0) or enhancement_options.upscale_force:
if mask_face and entire_mask_image is not None:
result_image = enhance_image_and_mask(result_image, enhancement_options,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
else:
result_image = enhance_image(result_image, enhancement_options)
elif mask_face and entire_mask_image is not None and swapped > 0:
result_image = Image.composite(result_image,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
return result_image, output, swapped

11
scripts/reactor_version.py Executable file
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@@ -0,0 +1,11 @@
app_title = "ReActor"
version_flag = "v0.7.1-a2"
from scripts.reactor_logger import logger, get_Run, set_Run
from scripts.reactor_globals import DEVICE
is_run = get_Run()
if not is_run:
logger.status(f"Running {version_flag} on Device: {DEVICE}")
set_Run(True)

94
scripts/reactor_xyz.py Executable file
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@@ -0,0 +1,94 @@
'''
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)
# XYZ init:
try:
import modules.script_callbacks as script_callbacks
script_callbacks.on_before_ui(run)
except:
pass