UPD: Comfy Registry

0.7.1 Beta3

.github/workflows/publish_action.yml

@@ -7,14 +7,18 @@ on:
     paths:
       - "pyproject.toml"
 
+permissions:
+  issues: write
+
 jobs:
   publish-node:
     name: Publish Custom Node to registry
     runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'Gourieff' }}
     steps:
       - name: Check out code
         uses: actions/checkout@v4
       - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@v1
         with:
           personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} ## Add your own personal access token to your Github secrets and reference it here.

.gitignore

@@ -3,3 +3,4 @@ __pycache__/
 .vscode/
 example
 input
+*.dll

README.md

@@ -2,15 +2,7 @@
 
   <img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/ReActor_logo_NEW_EN.png?raw=true" alt="logo" width="180px"/>
 
-  ![Version](https://img.shields.io/badge/node_version-0.5.2-brightgreen?style=for-the-badge&labelColor=darkgreen)
+  ![Version](https://img.shields.io/badge/node_version-0.7.1_beta3-green?style=for-the-badge&labelColor=darkgreen)
-
-  <!--<sup>
-  <font color=brightred>
-
-  ## !!! [Important Update](#latestupdate) !!!<br>Don't forget to add the Node again in existing workflows
-  
-  </font>
-  </sup>-->
 
   <a href="https://boosty.to/artgourieff" target="_blank">
     <img src="https://lovemet.ru/img/boosty.jpg" width="108" alt="Support Me on Boosty"/>
@@ -20,6 +12,9 @@
     </sup>
   </a>
 
+  <a href="https://t.me/reactor_faceswap" target="_blank"><img src="https://img.shields.io/badge/ReActor-2CA5E0?style=for-the-badge&logo=Telegram&logoColor=white&labelColor=blue"></img></a>
+  <a href="https://t.me/artgourieff" target="_blank"><img src="https://img.shields.io/badge/ArtGourieff-2CA5E0?style=for-the-badge&logo=Telegram&logoColor=white&labelColor=blue"></img></a>
+
   <hr>
   
   [![Commit activity](https://img.shields.io/github/commit-activity/t/Gourieff/ComfyUI-ReActor/main?cacheSeconds=0)](https://github.com/Gourieff/ComfyUI-ReActor/commits/main)
@@ -34,7 +29,7 @@
 
 </div>
 
-### The Fast and Simple Face Swap Extension Nodes for ComfyUI, based on [blocked ReActor](https://github.com/Gourieff/comfyui-reactor-node) - now it has a nudity detector to avoid using this software with 18+ content
+### The Fast and Simple Face Swap Extension Nodes for ComfyUI, based on [blocked ReActor](https://web.archive.org/web/20241230084620/https://github.com/Gourieff/comfyui-reactor-node) - now it has a nudity detector to avoid using this software with 18+ content
 
 > By using this Node you accept and assume [responsibility](#disclaimer)
 
@@ -51,27 +46,88 @@
 
 ## What's new in the latest update
 
-### 0.5.2 <sub><sup>BETA1</sup></sub>
+### 0.7.1 <sub><sup>BETA1</sup></sub>
+
+- **New `DLSS5 Frame Enhancer` node**. NVIDIA's latest DLSS 5 technology for enhancing image quality (both overall frame or faces by mask). <br><u>See [Installation Instructions](https://github.com/Gourieff/ComfyUI-ReActor/blob/main/r_dlssnr/dll/README.md).</u><br><pre>The integration operates in an isolated Host mode, fully eliminating conflicts with ComfyUI's CUDA context.<br>Huge thanks to the author of the [Merserk/dlss5-visual-enhancer](https://github.com/Merserk/dlss5-visual-enhancer) project for the C++ wrapper (neuroframe_engine.dll and neuroframe_caller.dll), which formed the base of the computational bridge for this node.<br>Also thanks Gemini 3.1 Pro (via [Google Gemini](https://gemini.google.com/app)) for the contribution.</pre><u>Requirements:</u><br>
+-- Windows 10/11<br>
+-- NVIDIA display driver >= 616.x<br>
+-- NVIDIA RTX 40/50-series GPU<br>
+(compatibility with older RTX series is unconfirmed)
+
+### 0.7.0
+
+- 💥 **Big Update! ☢ New ReActor Core!**<br>✅ No `Insightface` required!<br>✅ No `C++ Build Tools` required!<br>✅ Instalation process is much easier now!<br>✅ `Numpy 2.x` friendly as well as `1.x`!<br>⚠ <u>A swap result is slightly different now</u>. Hard to say if it’s "better" or "worse" — it’s just a bit different. But if we look at the numbers (cosine similarity of face embeddings), the accuracy is actually a little higher than with Insightface.<br>More info you can find here: https://t.me/reactor_faceswap/55
+- New Node "Face Similarity" to check face likeness after you make a swap
+- HyperSwap CPU Float normalization fix (thanks @Buumcode, issue [#183](https://github.com/Gourieff/ComfyUI-ReActor/issues/183))
+- Fixed: gender detection issue and face index logic (issue [#234](https://github.com/Gourieff/ComfyUI-ReActor/issues/234))
+- Comfy Desktop friendly `install.bat`
+- Other fixes and improvements
+
+<details>
+	<summary><a>Previous versions</a></summary>
+
+### 0.6.2
+
+- Added support of HyperSwap models by FaceFusion Labs (thanks [@Buumcode](https://github.com/Buumcode) for contribution)<br>You can download them [here](https://huggingface.co/facefusion/models-3.3.0/tree/main)<br>(hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1c_256.onnx)<br>and put them into the `ComfyUI\models\hyperswap` directory
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-04-3.jpg?raw=true" alt="0.6.2-whatsnew-04-3" width="100%"/>
+</center>
+
+[Comparison grid](https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2_swapmodels_compare.png) of Inswapper vs Reswapper vs HyperSwap
+
+- Face restoration process affects only swapped faces
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-01.jpg?raw=true" alt="0.6.2-whatsnew-01" width="100%"/>
+</center>
+
+- New Node "Restore Face Advanced" with Face Restore Filter, thanks https://github.com/Buumcode for implementation of "Restore Face Filter"<br>This node helps you apply the restoration process to the face(s) you need
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-02.jpg?raw=true" alt="0.6.2-whatsnew-02" width="100%"/>
+</center>
+
+- Added FACE_MODEL_NAME output for "Load Face Model" node
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-03.jpg?raw=true" alt="0.6.2-whatsnew-03" width="50%"/>
+</center>
+
+- Fixes and improvements
+
+### 0.6.1
+
+- Gender detection better logic for many faces and many indexes
+- MaskHelper node 2x speed up - not perfect yet but 1.5x-2x faster then before
+- ComfyUI native ProgressBar for different steps
+- ORIGINAL_IMAGE output for main nodes
+- Different fixes and improvements (https://github.com/Gourieff/ComfyUI-ReActor/issues/25 fix; no tmp file for NSFW detector; NSFW detector little speed up)
+
+### 0.6.0
+
+- New Node `ReActorSetWeight` - you can now set the strength of face swap for `source_image` or `face_model` from 0% to 100% (in 12.5% step)
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-whatsnew-01.jpg?raw=true" alt="0.6.0-whatsnew-01" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-whatsnew-02.jpg?raw=true" alt="0.6.0-whatsnew-02" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-alpha1-01.gif?raw=true" alt="0.6.0-whatsnew-03" width="540px"/>
+</center>
+
+### 0.5.2
 
 - ReSwapper models support. Although Inswapper still has the best similarity, but ReSwapper is evolving - thanks @somanchiu https://github.com/somanchiu/ReSwapper for the ReSwapper models and the ReSwapper project! This is a good step for the Community in the Inswapper's alternative creation!
 
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-03.jpg?raw=true" alt="0.5.2-whatsnew-03" width="100%"/>
+<center>
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-04.jpg?raw=true" alt="0.5.2-whatsnew-04" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-03.jpg?raw=true" alt="0.5.2-whatsnew-03" width="75%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-04.jpg?raw=true" alt="0.5.2-whatsnew-04" width="75%"/>
+</center>
 
 You can download ReSwapper models here:
 https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models
 Just put them into the "models/reswapper" directory.
 
-### 0.5.2 <sub><sup>ALPHA3</sup></sub>
-
 - NSFW-detector to not violate [GitHub rules](https://docs.github.com/en/site-policy/acceptable-use-policies/github-misinformation-and-disinformation#synthetic--manipulated-media-tools)
-
-### 0.5.2 <sub><sup>ALPHA2</sup></sub>
-
-- Minor fixes
-
-### 0.5.2 <sub><sup>ALPHA1</sup></sub>
-
 - New node "Unload ReActor Models" - is useful for complex WFs when you need to free some VRAM utilized by ReActor
 
 <img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-01.jpg?raw=true" alt="0.5.2-whatsnew-01" width="100%"/>
@@ -79,10 +135,12 @@ Just put them into the "models/reswapper" directory.
 - Support of ORT CoreML and ROCM EPs, just install onnxruntime version you need
 - Install script improvements to install latest versions of ORT-GPU
 
+<center>
 <img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-02.jpg?raw=true" alt="0.5.2-whatsnew-02" width="50%"/>
+</center>
+
+- Fixes and improvements
 
-<details>
-	<summary><a>Previous versions</a></summary>
 
 ### 0.5.1
 
@@ -96,7 +154,7 @@ Just put them into the "models/reswapper" directory.
 - Sorting facemodels alphabetically
 - A lot of fixes and improvements
 
-### [0.5.0 <sub><sup>BETA4</sup></sub>](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.5.0)
+### [0.5.0 <sub><sup>BETA4</sup></sub>](https://web.archive.org/web/20241127121952/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.5.0)
 
 - Spandrel lib support for GFPGAN
 
@@ -144,7 +202,7 @@ Use this Node to gain the best results of the face swapping process:
 
 - Little speed boost when analyzing target images (unfortunately it is still quite slow in compare to swapping and restoring...)
 
-### [0.4.2](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.2)
+### [0.4.2](https://web.archive.org/web/20241127034727/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.2)
 
 - GPEN-BFR-512 and RestoreFormer_Plus_Plus face restoration models support
 
@@ -167,12 +225,12 @@ Result example (the new face was created from 4 faces of different actresses):
 
 Basic workflow [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/workflows/ReActor--Build-Blended-Face-Model--v1.json)
 
-### [0.4.1](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.1)
+### [0.4.1](https://web.archive.org/web/20241127044707/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.1)
 
 - CUDA 12 Support - don't forget to run (Windows) `install.bat` or (Linux/MacOS) `install.py` for ComfyUI's Python enclosure or try to install ORT-GPU for CU12 manually (https://onnxruntime.ai/docs/install/#install-onnx-runtime-gpu-cuda-12x)
-- Issue https://github.com/Gourieff/comfyui-reactor-node/issues/173 fix
+- Issue [comfyui-reactor-node/issues/173](https://web.archive.org/web/20240919043728/https://github.com/Gourieff/comfyui-reactor-node/issues/173) fix
 
-- Separate Node for the Face Restoration postprocessing (FR https://github.com/Gourieff/comfyui-reactor-node/issues/191), can be found inside ReActor's menu (RestoreFace Node)
+- Separate Node for the Face Restoration postprocessing (FR [comfyui-reactor-node/issues/191](https://web.archive.org/web/20241127040848/https://github.com/Gourieff/comfyui-reactor-node/issues/191)), can be found inside ReActor's menu (RestoreFace Node)
 - (Windows) Installation can be done for Python from the System's PATH
 - Different fixes and improvements
 
@@ -180,7 +238,7 @@ Basic workflow [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-react
 
 <img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.4.1-whatsnew-01.jpg?raw=true" alt="0.4.1-whatsnew-01" width="100%"/>
 
-### [0.4.0](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.0)
+### [0.4.0](https://web.archive.org/web/20241119155323/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.0)
 
 - Input "input_image" goes first now, it gives a correct bypass and also it is right to have the main input first;
 - You can now save face models as "safetensors" files (`ComfyUI\models\reactor\faces`) and load them into ReActor implementing different scenarios and keeping super lightweight face models of the faces you use:
@@ -201,47 +259,25 @@ Thanks to everyone who finds bugs, suggests new features and supports this proje
 
 ## Installation
 
-<details>
+### Standalone (Portable) <a href="https://github.com/comfyanonymous/ComfyUI">ComfyUI</a> for Windows
-	<summary>SD WebUI: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/">AUTOMATIC1111</a> or <a href="https://github.com/vladmandic/automatic">SD.Next</a></summary>
 
-1. Close (stop) your SD-WebUI/Comfy Server if it's running
+1. Choose between two options:
-2. (For Windows Users):
-   - Install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) (Community version - you need this step to build Insightface)
-   - OR only [VS C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile"
-   - OR if you don't want to install VS or VS C++ BT - follow [this steps (sec. I)](#insightfacebuild)
-3. Go to the `extensions\sd-webui-comfyui\ComfyUI\custom_nodes`
-4. Open Console or Terminal and run `git clone https://github.com/Gourieff/ComfyUI-ReActor`
-5. Go to the SD WebUI root folder, open Console or Terminal and run (Windows users)`.\venv\Scripts\activate` or (Linux/MacOS)`venv/bin/activate`
-6. `python -m pip install -U pip`
-7. `cd extensions\sd-webui-comfyui\ComfyUI\custom_nodes\ComfyUI-ReActor`
-8. `python install.py`
-9.  Please, wait until the installation process will be finished
-10. (From the version 0.3.0) Download additional facerestorers models from the link below and put them into the `extensions\sd-webui-comfyui\ComfyUI\models\facerestore_models` directory:<br>
-https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models
-11. Run SD WebUI and check console for the message that ReActor Node is running:
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/console_status_running.jpg?raw=true" alt="console_status_running" width="759"/>
-
-12.  Go to the ComfyUI tab and find there ReActor Node inside the menu `ReActor` or by using a search:
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/webui-demo.png?raw=true" alt="webui-demo" width="100%"/>
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/search-demo.png?raw=true" alt="webui-demo" width="1043"/>
-
-</details>
-
-<details>
-	<summary>Standalone (Portable) <a href="https://github.com/comfyanonymous/ComfyUI">ComfyUI</a> for Windows</summary>
-
-1. Do the following:
-   - Install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) (Community version - you need this step to build Insightface)
-   - OR only [VS C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile"
-   - OR if you don't want to install VS or VS C++ BT - follow [this steps (sec. I)](#insightfacebuild)
-2. Choose between two options:
    - (ComfyUI Manager) Open ComfyUI Manager, click "Install Custom Nodes", type "ReActor" in the "Search" field and then click "Install". After ComfyUI will complete the process - please restart the Server.
    - (Manually) Go to `ComfyUI\custom_nodes`, open Console and run `git clone https://github.com/Gourieff/ComfyUI-ReActor`
-3. Go to `ComfyUI\custom_nodes\ComfyUI-ReActor` and run `install.bat`
+2. Go to `ComfyUI\custom_nodes\ComfyUI-ReActor` and run `install.bat`
-4. If you don't have the "face_yolov8m.pt" Ultralytics model - you can download it from the [Assets](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt) and put it into the "ComfyUI\models\ultralytics\bbox" directory<br>As well as one or both of "Sams" models from [here](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/sams) - download (if you don't have them) and put into the "ComfyUI\models\sams" directory
+3. Download required models from the Section below
-5. Run ComfyUI and find there ReActor Nodes inside the menu `ReActor` or by using a search
+4. Run ComfyUI and find there ReActor Nodes inside the menu `ReActor` or by using a search
 
-</details>
+
+## Models
+
+ - buffalo_l: downloaded on first launch into `ComfyUI\models\insightface\models\buffalo_l`, or you can download manually from [here](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - inswapper_128: downloaded during installation into `ComfyUI\models\insightface`, or you can download manually from [here](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - reswapper_128/256: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models put them into `ComfyUI\models\reswapper`
+ - hyperswap_256: https://huggingface.co/facefusion/models-3.3.0/tree/main (hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1c_256.onnx) put them into `ComfyUI\models\hyperswap`
+ - Face restoration models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models put any you like into `ComfyUI\models\facerestore_models`
+ - Ultralytics model: https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt put into `ComfyUI\models\ultralytics\bbox`
+ - SAM models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/sams put into `ComfyUI\models\sams`
 
 ## Usage
 
@@ -249,11 +285,12 @@ You can find ReActor Nodes inside the menu `ReActor` or by using a search (just
 
 List of Nodes:
 - ••• Main Nodes •••
-   - ReActorFaceSwap (Main Node)
+  - ReActorFaceSwap (Main Node)
-   - ReActorFaceSwapOpt (Main Node with the additional Options input)
+  - ReActorFaceSwapOpt (Main Node with the additional Options input)
-   - ReActorOptions (Options for ReActorFaceSwapOpt)
+  - ReActorOptions (Options for ReActorFaceSwapOpt)
-   - ReActorFaceBoost (Face Booster Node)
+  - ReActorFaceBoost (Face Booster Node)
-   - ReActorMaskHelper (Masking Helper)
+  - ReActorMaskHelper (Masking Helper)
+  - ReActorSetWeight (Set Face Swap Weight)
 - ••• Operations with Face Models •••
   - ReActorSaveFaceModel (Save Face Model)
   - ReActorLoadFaceModel (Load Face Model)
@@ -261,8 +298,12 @@ List of Nodes:
   - ReActorMakeFaceModelBatch (Make Face Model Batch)
 - ••• Additional Nodes •••
   - ReActorRestoreFace (Face Restoration)
+  - ReActorRestoreFaceAdvanced (Restore Face Advanced)
+  - ReActorFaceSimilarity (Face Similarity)
   - ReActorImageDublicator (Dublicate one Image to Images List)
   - ImageRGBA2RGB (Convert RGBA to RGB)
+  - ReActorUnload (Unload ReActor models from VRAM)
+  - DLSS5FrameEnhancer (Enhance frame quality with NVIDIA DLSS 5)
 
 Connect all required slots and run the query.
 
@@ -274,6 +315,10 @@ Connect all required slots and run the query.
   - Supported Nodes: "Load Image" or any other nodes providing images as an output;
 - `face_model` - is the input for the "Load Face Model" Node or another ReActor node to provide a face model file (face embedding) you created earlier via the "Save Face Model" Node;
   - Supported Nodes: "Load Face Model", "Build Blended Face Model";
+- `options` - to connect ReActorOptions;
+  - Supported Nodes: "ReActorOptions";
+- `face_boost` - to connect ReActorFaceBoost;
+  - Supported Nodes: "ReActorFaceBoost";
 
 ### Main Node Outputs
 
@@ -281,6 +326,7 @@ Connect all required slots and run the query.
   - Supported Nodes: any nodes which have images as an input;
 - `FACE_MODEL` - is an output providing a source face's model being built during the swapping process;
   - Supported Nodes: "Save Face Model", "ReActor", "Make Face Model Batch";
+- `ORIGINAL_IMAGE` - `input_image` bypass;
 
 ### Face Restoration
 
@@ -309,59 +355,43 @@ Since version 0.4.0 you can save face models as "safetensors" files (stored in `
 To make new models appear in the list of the "Load Face Model" Node - just refresh the page of your ComfyUI web application.<br>
 (I recommend you to use ComfyUI Manager - otherwise you workflow can be lost after you refresh the page if you didn't save it before that).
 
+### Masking Helper
+
+Face Masking feature is available since version 0.5.0, just add the "ReActorMaskHelper" Node to the workflow and connect it as shown below:
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.0-whatsnew-01.jpg?raw=true" alt="0.5.0-whatsnew-01" width="100%"/>
+
+If you don't have the "face_yolov8m.pt" Ultralytics model - you can download it from the [Assets](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt) and put it into the "ComfyUI\models\ultralytics\bbox" directory
+<br>
+As well as ["sam_vit_b_01ec64.pth"](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/sams/sam_vit_b_01ec64.pth) or ["sam_vit_l_0b3195.pth"](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/sams/sam_vit_l_0b3195.pth) (better occlusion) - download (if you don't have it) and put it into the "ComfyUI\models\sams" directory;
+
+Use this Node to gain the best results of the face swapping process:
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.0-whatsnew-02.jpg?raw=true" alt="0.5.0-whatsnew-02" width="100%"/>
+
+### Face Swap Weigth
+
+You can set the strength of face swap for `source_image` or `face_model` from 0% to 100% (in 12.5% step) with `ReActorSetWeight` node
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-whatsnew-01.jpg?raw=true" alt="0.6.0-whatsnew-01" width="100%"/>
+</center>
+
 ## Troubleshooting
 
-<a name="insightfacebuild">
+### **I. "AttributeError: 'NoneType' object has no attribute 'get'"**
-
-### **I. (For Windows users) If you still cannot build Insightface for some reasons or just don't want to install Visual Studio or VS C++ Build Tools - do the following:**
-
-1. (ComfyUI Portable) From the root folder check the version of Python:<br>run CMD and type `python_embeded\python.exe -V`
-2. Download prebuilt Insightface package [for Python 3.10](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp310-cp310-win_amd64.whl) or [for Python 3.11](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp311-cp311-win_amd64.whl) (if in the previous step you see 3.11) or [for Python 3.12](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp312-cp312-win_amd64.whl) (if in the previous step you see 3.12) and put into the stable-diffusion-webui (A1111 or SD.Next) root folder (where you have "webui-user.bat" file) or into ComfyUI root folder if you use ComfyUI Portable
-3. From the root folder run:
-   - (SD WebUI) CMD and `.\venv\Scripts\activate`
-   - (ComfyUI Portable) run CMD
-4. Then update your PIP:
-   - (SD WebUI) `python -m pip install -U pip`
-   - (ComfyUI Portable) `python_embeded\python.exe -m pip install -U pip`
-5. Then install Insightface:
-   - (SD WebUI) `pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` (for 3.10) or `pip install insightface-0.7.3-cp311-cp311-win_amd64.whl` (for 3.11) or `pip install insightface-0.7.3-cp312-cp312-win_amd64.whl` (for 3.12)
-   - (ComfyUI Portable) `python_embeded\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` (for 3.10) or `python_embeded\python.exe -m pip install insightface-0.7.3-cp311-cp311-win_amd64.whl` (for 3.11) or `python_embeded\python.exe -m pip install insightface-0.7.3-cp312-cp312-win_amd64.whl` (for 3.12)
-6. Enjoy!
-
-### **II. "AttributeError: 'NoneType' object has no attribute 'get'"**
 
 This error may occur if there's smth wrong with the model file `inswapper_128.onnx`
 
-Try to download it manually from [here](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx)
+Try to download it manually from [here](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx)
 and put it to the `ComfyUI\models\insightface` replacing existing one
 
-### **III. "reactor.execute() got an unexpected keyword argument 'reference_image'"**
+### **II. "reactor.execute() got an unexpected keyword argument 'reference_image'"**
 
 This means that input points have been changed with the latest update<br>
 Remove the current ReActor Node from your workflow and add it again
 
-### **IV. ControlNet Aux Node IMPORT failed error when using with ReActor Node**
+### **III. "fatal: fetch-pack: invalid index-pack output" when you try to `git clone` the repository"**
-
-1. Close ComfyUI if it runs
-2. Go to the ComfyUI root folder, open CMD there and run:
-   - `python_embeded\python.exe -m pip uninstall -y opencv-python opencv-contrib-python opencv-python-headless`
-   - `python_embeded\python.exe -m pip install opencv-python==4.7.0.72`
-3. That's it!
-
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/reactor-w-controlnet.png?raw=true" alt="reactor+controlnet" />
-
-### **V. "ModuleNotFoundError: No module named 'basicsr'" or "subprocess-exited-with-error" during future-0.18.3 installation**
-
-- Download https://github.com/Gourieff/Assets/raw/main/comfyui-reactor-node/future-0.18.3-py3-none-any.whl<br>
-- Put it to ComfyUI root And run:
-
-      python_embeded\python.exe -m pip install future-0.18.3-py3-none-any.whl
-
-- Then:
-
-      python_embeded\python.exe -m pip install basicsr
-
-### **VI. "fatal: fetch-pack: invalid index-pack output" when you try to `git clone` the repository"**
 
 Try to clone with `--depth=1` (last commit only):
 
@@ -460,8 +490,8 @@ SHA256:4c06341c33c2ca1f86781dab0e829f88ad5b64be9fba56e56bc9ebdefc619e43
 | [codeformer-v0.1.0.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/codeformer-v0.1.0.pth) | [sczhou](https://github.com/sczhou/CodeFormer) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
 | [GFPGANv1.3.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.3.pth) | [TencentARC](https://github.com/TencentARC/GFPGAN) | ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) |
 | [GFPGANv1.4.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.4.pth) | [TencentARC](https://github.com/TencentARC/GFPGAN) | ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) |
-| [inswapper_128.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) | [DeepInsight](https://github.com/deepinsight/insightface) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+| [inswapper_128.onnx](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx) | [DeepInsight](https://github.com/deepinsight/insightface) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
-| [inswapper_128_fp16.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx) | [Hillobar](https://github.com/Hillobar/Rope) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+| [inswapper_128_fp16.onnx](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128_fp16.onnx) | [Hillobar](https://github.com/Hillobar/Rope) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
 
 [BasicSR](https://github.com/XPixelGroup/BasicSR) - [@XPixelGroup](https://github.com/XPixelGroup) <br>
 [facexlib](https://github.com/xinntao/facexlib) - [@xinntao](https://github.com/xinntao) <br>

README_RU.md

@@ -2,15 +2,7 @@
 
   <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_нода-0.5.2-brightgreen?style=for-the-badge&labelColor=darkgreen)
+  ![Version](https://img.shields.io/badge/версия_нода-0.7.1_beta3-green?style=for-the-badge&labelColor=darkgreen)
-  
-  <!--<sup>
-  <font color=brightred>
-
-  ## !!! [Важные изменения](#latestupdate) !!!<br>Не забудьте добавить Нод заново в существующие воркфлоу
-  
-  </font>
-  </sup>-->
 
   <a href="https://boosty.to/artgourieff" target="_blank">
     <img src="https://lovemet.ru/img/boosty.jpg" width="108" alt="Поддержать проект на Boosty"/>
@@ -20,6 +12,9 @@
     </sup>
   </a>
 
+  <a href="https://t.me/reactor_faceswap" target="_blank"><img src="https://img.shields.io/badge/ReActor-2CA5E0?style=for-the-badge&logo=Telegram&logoColor=white&labelColor=blue"></img></a>
+  <a href="https://t.me/artgourieff" target="_blank"><img src="https://img.shields.io/badge/ArtGourieff-2CA5E0?style=for-the-badge&logo=Telegram&logoColor=white&labelColor=blue"></img></a>
+
   <hr>
   
   [![Commit activity](https://img.shields.io/github/commit-activity/t/Gourieff/ComfyUI-ReActor/main?cacheSeconds=0)](https://github.com/Gourieff/ComfyUI-ReActor/commits/main)
@@ -34,7 +29,7 @@
 
 </div>
 
-### Ноды (nodes) для быстрой и простой замены лиц на любых изображениях для работы с ComfyUI, основан на [ранее заблокированном РеАкторе](https://github.com/Gourieff/comfyui-reactor-node) - теперь имеется встроенный NSFW-детектор, исключающий замену лиц на изображениях с контентом 18+
+### Ноды (nodes) для быстрой и простой замены лиц на любых изображениях для работы с ComfyUI, основан на [ранее заблокированном РеАкторе](https://web.archive.org/web/20241126185020/https://github.com/Gourieff/comfyui-reactor-node/blob/main/README_RU.md) - теперь имеется встроенный NSFW-детектор, исключающий замену лиц на изображениях с контентом 18+
 
 > Используя данное ПО, вы понимаете и принимаете [ответственность](#disclaimer)
 
@@ -51,27 +46,88 @@
 
 ## Что нового в последнем обновлении
 
-### 0.5.2 <sub><sup>BETA1</sup></sub>
+### 0.7.1 <sub><sup>BETA1</sup></sub>
+
+- **Новый узел `DLSS5 Frame Enhancer`**. Новейшая технология DLSS 5 от NVIDIA для улучшения качества изображения (кадра целиком или лиц по маске).<br><u>Следуйте [инструкции по установке](https://github.com/Gourieff/ComfyUI-ReActor/blob/main/r_dlssnr/dll/README.md).</u><br><pre>Интеграция работает в изолированном Host-режиме, что полностью исключает конфликты с CUDA-контекстом ComfyUI.<br>Огромная благодарность автору проекта [Merserk/dlss5-visual-enhancer](https://github.com/Merserk/dlss5-visual-enhancer) за C++ обертку (neuroframe_engine.dll и neuroframe_caller.dll), которая легла в основу вычислительного моста для данного узла.<br>Также благодарность Gemini 3.1 Pro (через [Google Gemini](https://gemini.google.com/app)) за ассистирование и помощь.</pre><u>Требования:</u><br>
+-- Windows 10/11<br>
+-- NVIDIA display driver >= 616.x<br>
+-- NVIDIA RTX 40/50-series GPU<br>
+(совместимость с более старыми сериями RTX не подтверждена)
+
+### 0.7.0
+
+- 💥 **Важное обновление! ☢ Новое ядро РеАктора!**<br>✅ Библиотека `Insightface` больше не требуется!<br>✅ `C++ Build Tools` больше не требуются!<br>✅ Более простой процесс установки!<br>✅ Поддержка `Numpy 2.x` и `1.x`!<br>⚠ <u>Результат Свапа теперь слегка отличается</u>. Сложно сказать, хуже или лучше — результат просто чуть-чуть другой. По показателям схожести (оценка косинусного сходства векторов эмбеддингов лиц) результат стал немного лучше (хоть и незначительно), чем с Insightface.<br>Подробнее здесь: https://t.me/reactor_faceswap/55
+- Новый узел "Face Similarity" для проверки схожести лиц после свапа
+- Исправление "HyperSwap CPU Float normalization" (спасибо @Buumcode, Issue [#183](https://github.com/Gourieff/ComfyUI-ReActor/issues/183))
+- Исправлено: проблема определения пола и логика индексов лиц (Issue [#234](https://github.com/Gourieff/ComfyUI-ReActor/issues/234))
+- Улучшен `install.bat` для поддержки запуска установки в Comfy Desktop
+- Прочие улучшение и исправления
+
+<details>
+	<summary><a>Предыдущие версии</a></summary>
+
+### 0.6.2
+
+- Добавлена поддержка моделей HyperSwap от команды FaceFusion Labs (спасибо [@Buumcode](https://github.com/Buumcode) за вариант реализации)<br>Модели можно скачать [отсюда](https://huggingface.co/facefusion/models-3.3.0/tree/main)<br>(hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1c_256.onnx)<br>и положить в папку `ComfyUI/models/hyperswap`
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-04-3.jpg?raw=true" alt="0.6.2-whatsnew-04-3" width="100%"/>
+</center>
+
+[Сравнение](https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2_swapmodels_compare.png) моделей Inswapper, Reswapper, HyperSwap
+
+- Теперь восстановление лиц затрагивает только заменённые лица
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-01-ru.jpg?raw=true" alt="0.6.2-whatsnew-01" width="100%"/>
+</center>
+
+- Новый узел "Restore Face Advanced" с фильтром по лицам, спасибо https://github.com/Buumcode за реализацию "Фильтра восстановления лиц"<br>Этот узел помогает применить восстановление именно к нужному лицу или лицам
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-02.jpg?raw=true" alt="0.6.2-whatsnew-02" width="100%"/>
+</center>
+
+- Добавлен выход FACE_MODEL_NAME для узла "Load Face Model"
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.2-whatsnew-03.jpg?raw=true" alt="0.6.2-whatsnew-03" width="50%"/>
+</center>
+
+- Исправления и улучшения
+
+### 0.6.1
+
+- Улучшенная логика работы с индексами множества лиц при определении пола
+- MaskHelper нод теперь почти вдвое быстрее - пока не идеально, но лучше, чем было ранее
+- Нативный ProgressBar ComfyUI для разных шагов
+- Добавлен выход ORIGINAL_IMAGE для основных нодов
+- Разные исправления и улучшения (https://github.com/Gourieff/ComfyUI-ReActor/issues/25 фикс; временные файлы для NSFW детектора больше не создаются; NSFW детектор стал работать немного быстрее)
+
+### 0.6.0
+
+- Новый нод `ReActorSetWeight` - теперь можно установить силу замены лица для `source_image` или `face_model` от 0% до 100% (с шагом 12.5%)
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-whatsnew-01.jpg?raw=true" alt="0.6.0-whatsnew-01" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-whatsnew-02.jpg?raw=true" alt="0.6.0-whatsnew-02" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-alpha1-01.gif?raw=true" alt="0.6.0-whatsnew-03" width="540px"/>
+</center>
+
+### 0.5.2
 
 - Поддержка моделей ReSwapper. Несмотря на то, что Inswapper по-прежнему даёт лучшее сходство, но ReSwapper развивается - спасибо @somanchiu https://github.com/somanchiu/ReSwapper за эти модели и проект ReSwapper! Это хороший шаг для Сообщества в создании альтернативы Инсваппера!
 
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-03.jpg?raw=true" alt="0.5.2-whatsnew-03" width="100%"/>
+<center>
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-04.jpg?raw=true" alt="0.5.2-whatsnew-04" width="100%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-03.jpg?raw=true" alt="0.5.2-whatsnew-03" width="75%"/>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-04.jpg?raw=true" alt="0.5.2-whatsnew-04" width="75%"/>
+</center>
 
 Скачать модели ReSwapper можно отсюда:
 https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models
 Сохраните их в директорию "models/reswapper".
 
-### 0.5.2 <sub><sup>ALPHA3</sup></sub>
-
 - NSFW-детектор, чтобы не нарушать [правила GitHub](https://docs.github.com/en/site-policy/acceptable-use-policies/github-misinformation-and-disinformation#synthetic--manipulated-media-tools)
-
-### 0.5.2 <sub><sup>ALPHA2</sup></sub>
-
-- Небольшие исправления
-
-### 0.5.2 <sub><sup>ALPHA1</sup></sub>
-
 - Новый нод "Unload ReActor Models" - полезен для сложных воркфлоу, когда вам нужно освободить ОЗУ, занятую РеАктором
 
 <img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-01.jpg?raw=true" alt="0.5.2-whatsnew-01" width="100%"/>
@@ -79,10 +135,11 @@ https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models
 - Поддержка ORT CoreML and ROCM EPs, достаточно установить ту версию onnxruntime, которая соответствует вашему GPU
 - Некоторые улучшения скрипта установки для поддержки последней версии ORT-GPU
 
+<center>
 <img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.2-whatsnew-02.jpg?raw=true" alt="0.5.2-whatsnew-02" width="50%"/>
+</center>
 
-<details>
+- Исправления и улучшения
-	<summary><a>Предыдущие версии</a></summary>
 
 ### 0.5.1
 
@@ -96,7 +153,7 @@ https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models
 - Сортировка моделей лиц по алфавиту
 - Множество исправлений и улучшений
 
-### [0.5.0 <sub><sup>BETA4</sup></sub>](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.5.0)
+### [0.5.0 <sub><sup>BETA4</sup></sub>](https://web.archive.org/web/20241127121952/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.5.0)
 
 - Поддержка библиотеки Spandrel при работе с GFPGAN
 
@@ -144,7 +201,7 @@ Basic workflow [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-react
 
 - Небольшое улучшение скорости анализа целевых изображений (input)
 
-### [0.4.2](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.2)
+### [0.4.2](https://web.archive.org/web/20241127034727/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.2)
 
 - Добавлена поддержка GPEN-BFR-512 и RestoreFormer_Plus_Plus моделей восстановления лиц
 
@@ -167,12 +224,12 @@ Basic workflow [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-react
 
 Базовый воркфлоу [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/workflows/ReActor--Build-Blended-Face-Model--v1.json)
 
-### [0.4.1](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.1)
+### [0.4.1](https://web.archive.org/web/20241127044707/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.1)
 
 - Поддержка CUDA 12 - не забудьте запустить (Windows) `install.bat` или (Linux/MacOS) `install.py` для используемого Python окружения или попробуйте установить ORT-GPU для CU12 вручную (https://onnxruntime.ai/docs/install/#install-onnx-runtime-gpu-cuda-12x)
-- Исправление Issue https://github.com/Gourieff/comfyui-reactor-node/issues/173
+- Исправление Issue [comfyui-reactor-node/issues/173](https://web.archive.org/web/20240919043728/https://github.com/Gourieff/comfyui-reactor-node/issues/173)
 
-- Отдельный Нод для восстаноления лиц (FR https://github.com/Gourieff/comfyui-reactor-node/issues/191), располагается внутри меню ReActor (нод RestoreFace)
+- Отдельный Нод для восстаноления лиц (FR [comfyui-reactor-node/issues/191](https://web.archive.org/web/20241127040848/https://github.com/Gourieff/comfyui-reactor-node/issues/191)), располагается внутри меню ReActor (нод RestoreFace)
 - (Windows) Установка зависимостей теперь может быть выполнена в Python из PATH ОС
 - Разные исправления и улучшения
 
@@ -180,7 +237,7 @@ Basic workflow [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-react
 
 <img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.4.1-whatsnew-01.jpg?raw=true" alt="0.4.1-whatsnew-01" width="100%"/>
 
-### [0.4.0](https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.0)
+### [0.4.0](https://web.archive.org/web/20241119155323/https://github.com/Gourieff/comfyui-reactor-node/releases/tag/v0.4.0)
 
 - Вход "input_image" теперь идёт первым, это даёт возможность корректного байпаса, а также это правильно с точки зрения расположения входов (главный вход - первый);
 - Теперь можно сохранять модели лиц в качестве файлов "safetensors" (`ComfyUI\models\reactor\faces`) и загружать их в ReActor, реализуя разные сценарии использования, а также  храня супер легкие модели лиц, которые вы чаще всего используете:
@@ -203,48 +260,24 @@ Basic workflow [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-react
 
 ## Установка
 
-<details>
+### Портативная версия <a href="https://github.com/comfyanonymous/ComfyUI">ComfyUI</a> для Windows
-	<summary>SD WebUI: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/">AUTOMATIC1111</a> или <a href="https://github.com/vladmandic/automatic">SD.Next</a></summary>
 
-1. Закройте (остановите) SD-WebUI Сервер, если запущен
+1. Выберите из двух вариантов:
-2. (Для пользователей Windows):
-   - Установите [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) (Например, версию Community - этот шаг нужен для правильной компиляции библиотеки Insightface)
-   - ИЛИ только [VS C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/), выберите "Desktop Development with C++" в разделе "Workloads -> Desktop & Mobile"
-   - ИЛИ если же вы не хотите устанавливать что-либо из вышеуказанного - выполните [данные шаги (раздел. I)](#insightfacebuild)
-3. Перейдите в `extensions\sd-webui-comfyui\ComfyUI\custom_nodes`
-4. Откройте Консоль или Терминал и выполните `git clone https://github.com/Gourieff/ComfyUI-ReActor`
-5. Перейдите в корневую директорию SD WebUI, откройте Консоль или Терминал и выполните (для пользователей Windows)`.\venv\Scripts\activate` или (для пользователей Linux/MacOS)`venv/bin/activate`
-6. `python -m pip install -U pip`
-7. `cd extensions\sd-webui-comfyui\ComfyUI\custom_nodes\ComfyUI-ReActor`
-8. `python install.py`
-9.  Пожалуйста, дождитесь полного завершения установки
-10. (Начиная с версии 0.3.0) Скачайте дополнительные модели восстановления лиц (по ссылке ниже) и сохраните их в папку `extensions\sd-webui-comfyui\ComfyUI\models\facerestore_models`:<br>
-https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models
-11. Запустите SD WebUI и проверьте консоль на сообщение, что ReActor Node работает:
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/console_status_running.jpg?raw=true" alt="console_status_running" width="759"/>
-
-12.   Перейдите во вкладку ComfyUI и найдите там ReActor Node внутри меню `ReActor` или через поиск:
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/webui-demo.png?raw=true" alt="webui-demo" width="100%"/>
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/search-demo.png?raw=true" alt="webui-demo" width="1043"/>
-
-</details>
-
-<details>
-	<summary>Портативная версия <a href="https://github.com/comfyanonymous/ComfyUI">ComfyUI</a> для Windows</summary>
-
-1. Сделайте следующее:
-   - Установите [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) (Например, версию Community - этот шаг нужен для правильной компиляции библиотеки Insightface)
-   - ИЛИ только [VS C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/), выберите "Desktop Development with C++" в разделе "Workloads -> Desktop & Mobile"
-   - ИЛИ если же вы не хотите устанавливать что-либо из вышеуказанного - выполните [данные шаги (раздел. I)](#insightfacebuild)
-2. Выберите из двух вариантов:
    - (ComfyUI Manager) Откройте ComfyUI Manager, нажвите "Install Custom Nodes", введите "ReActor" в поле "Search" и далее нажмите "Install". После того, как ComfyUI завершит установку, перезагрузите сервер.
    - (Вручную) Перейдите в `ComfyUI\custom_nodes`, откройте Консоль и выполните `git clone https://github.com/Gourieff/ComfyUI-ReActor`
-3. Перейдите `ComfyUI\custom_nodes\ComfyUI-ReActor` и запустите `install.bat`, дождитесь окончания установки
+2. Перейдите `ComfyUI\custom_nodes\ComfyUI-ReActor` и запустите `install.bat`, дождитесь окончания установки
-4. Если модель "face_yolov8m.pt" у вас отсутствует - можете скачать её [отсюда](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt) и положить в папку "ComfyUI\models\ultralytics\bbox"<br>
+3. Скачайте необходимые модели из Раздела "Модели" ниже
-То же самое и с "Sams" моделями, скачайте одну или обе [отсюда](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/sams) - и положите в папку "ComfyUI\models\sams"
+4. Запустите ComfyUI и найдите ReActor Node внутри меню `ReActor` или через поиск
-5. Запустите ComfyUI и найдите ReActor Node внутри меню `ReActor` или через поиск
 
-</details>
+## Модели
+
+ - buffalo_l: скачиваются при первом запуске в `ComfyUI\models\insightface\models\buffalo_l`, для ручного скачивания доступны [здесь](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - inswapper_128: скачивается при установке в `ComfyUI\models\insightface`, для ручного скачивания доступны [здесь](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - reswapper_128/256: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models скачать в `ComfyUI\models\reswapper`
+ - hyperswap_256: https://huggingface.co/facefusion/models-3.3.0/tree/main (hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1c_256.onnx) скачать в `ComfyUI\models\hyperswap`
+ - Face restoration models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models скачать любые предпочитаемые в `ComfyUI\models\facerestore_models`
+ - Ultralytics model: https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt скачать в `ComfyUI\models\ultralytics\bbox`
+ - SAM models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/sams скачать в `ComfyUI\models\sams`
 
 <a name="usage">
 
@@ -253,25 +286,30 @@ https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_mo
 Вы можете найти ноды ReActor внутри меню `ReActor` или через поиск (достаточно ввести "ReActor" в поисковой строке)
 
 Список нодов:
-- ••• Main Nodes •••
+- ••• Основные •••
-   - ReActorFaceSwap (Основной нод)
+  - ReActorFaceSwap (Основной нод)
-   - ReActorFaceSwapOpt (Основной нод с доп. входом Options)
+  - ReActorFaceSwapOpt (Основной нод с доп. входом Options)
-   - ReActorOptions (Опции для ReActorFaceSwapOpt)
+  - ReActorOptions (Опции для ReActorFaceSwapOpt)
-   - ReActorFaceBoost (Нод Face Booster)
+  - ReActorFaceBoost (Face Booster)
-   - ReActorMaskHelper (Masking Helper)
+  - ReActorMaskHelper (Masking Helper)
-- ••• Operations with Face Models •••
+  - ReActorSetWeight (Задать замены лица)
-  - ReActorSaveFaceModel (Save Face Model)
+- ••• Работа с моделями лиц •••
-  - ReActorLoadFaceModel (Load Face Model)
+  - ReActorSaveFaceModel (Сохранить модель лица)
-  - ReActorBuildFaceModel (Build Blended Face Model)
+  - ReActorLoadFaceModel (Загрузить модель лица)
-  - ReActorMakeFaceModelBatch (Make Face Model Batch)
+  - ReActorBuildFaceModel (Построить смешанную модель лица)
-- ••• Additional Nodes •••
+  - ReActorMakeFaceModelBatch (Создать пачку моделей лиц)
-  - ReActorRestoreFace (Face Restoration)
+- ••• Дополнительные •••
-  - ReActorImageDublicator (Dublicate one Image to Images List)
+  - ReActorRestoreFace (Восстановление лиц)
-  - ImageRGBA2RGB (Convert RGBA to RGB)
+  - ReActorRestoreFaceAdvanced (Восстановление лиц продвинутое)
+  - ReActorFaceSimilarity (Оценка схожести лиц)
+  - ReActorImageDublicator (Сделать из одного изображения несколько дубликатов)
+  - ImageRGBA2RGB (Конвертировать RGBA в RGB)
+  - ReActorUnload (Выгрузить модели РеАктора из VRAM)
+  - DLSS5FrameEnhancer (Улучшение детализации кадра с NVIDIA DLSS 5)
 
 Соедините все необходимые слоты (slots) и запустите очередь (query).
 
-### Входы основного Нода
+### Входы основного Узла
 
 - `input_image` - это изображение, на котором надо поменять лицо или лица (целевое изображение, аналог "target image" в версии для SD WebUI);
   - Поддерживаемые ноды: "Load Image", "Load Video" или любые другие ноды предоставляющие изображение в качестве выхода;
@@ -279,13 +317,18 @@ https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_mo
   - Поддерживаемые ноды: "Load Image" или любые другие ноды с выходом Image(s);
 - `face_model` - это вход для выхода с нода "Load Face Model" или другого нода ReActor для загрузки модели лица (face model или face embedding), которое вы создали ранее через нод "Save Face Model";
   - Поддерживаемые ноды: "Load Face Model", "Build Blended Face Model";
+- `options` - для соединения с ReActorOptions;
+  - Поддерживаемые ноды: "ReActorOptions";
+- `face_boost` - для соединения с ReActorFaceBoost;
+  - Поддерживаемые ноды: "ReActorFaceBoost";
 
-### Выходы основного Нода
+### Выходы основного Узла
 
 - `IMAGE` - выход с готовым изображением (результатом);
   - Поддерживаемые ноды: любые ноды с изображением на входе;
 - `FACE_MODEL` - выход, предоставляющий модель лица, построенную в ходе замены;
   - Поддерживаемые ноды: "Save Face Model", "ReActor", "Make Face Model Batch";
+- `ORIGINAL_IMAGE` - `input_image` байпас;
 
 ### Восстановление лиц
 
@@ -313,61 +356,47 @@ ReActor заменит только то лицо, которое удовлет
 Чтобы новые модели появились в списке моделей нода "Load Face Model" - обновите страницу of с ComfyUI.<br>
 (Рекомендую использовать ComfyUI Manager - иначе ваше воркфлоу может быть потеряно после перезагрузки страницы, если вы не сохранили его).
 
+### Masking Helper
+
+Нод доступен с версии 0.5.0, просто добавьте "ReActorMaskHelper" в рабочий процесс и соедините как показано ниже:
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.0-whatsnew-01.jpg?raw=true" alt="0.5.0-whatsnew-01" width="100%"/>
+
+Если модель "face_yolov8m.pt" отсутствует - скачайте [отсюда](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt) и положите в папку "ComfyUI\models\ultralytics\bbox"
+<br>
+Также и ["sam_vit_b_01ec64.pth"](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/sams/sam_vit_b_01ec64.pth) или ["sam_vit_l_0b3195.pth"](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/sams/sam_vit_l_0b3195.pth) (лучше окклюзия) - скачайте (если не качали ранее) и положите в папку "ComfyUI\models\sams";
+
+Используйте этот нод для улучшенного результата при замене лиц:
+
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.5.0-whatsnew-02.jpg?raw=true" alt="0.5.0-whatsnew-02" width="100%"/>
+
+### Сила замены лица
+
+Для входов `source_image` or `face_model` можно задать силу замены лица от 0% до 100% (с шагом 12.5%) с помощью нода `ReActorSetWeight`
+
+<center>
+<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/0.6.0-whatsnew-01.jpg?raw=true" alt="0.6.0-whatsnew-01" width="100%"/>
+</center>
+
 <a name="troubleshooting">
 
 ## Устранение проблем
 
 <a name="insightfacebuild">
 
-### **I. (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:**
+### **I. "AttributeError: 'NoneType' object has no attribute 'get'"**
-
-1. (ComfyUI Portable) Находясь в корневой директории, проверьте версию Python:<br>запустите CMD и выполните `python_embeded\python.exe -V`<br>Вы должны увидеть версию или 3.10, или 3.11, или 3.12
-2. Скачайте готовый пакет Insightface [для версии 3.10](https://github.com/Gourieff/sd-webui-reactor/raw/main/example/insightface-0.7.3-cp310-cp310-win_amd64.whl) или [для 3.11](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp311-cp311-win_amd64.whl) (если на предыдущем шаге вы увидели 3.11) или [для 3.12](https://github.com/Gourieff/Assets/raw/main/Insightface/insightface-0.7.3-cp312-cp312-win_amd64.whl) (если на предыдущем шаге вы увидели 3.12) и сохраните его в корневую директорию stable-diffusion-webui (A1111 или SD.Next) - туда, где лежит файл "webui-user.bat" -ИЛИ- в корневую директорию ComfyUI, если вы используете ComfyUI Portable
-3. Из корневой директории запустите:
-   - (SD WebUI) CMD и `.\venv\Scripts\activate`
-   - (ComfyUI Portable) CMD
-4. Обновите PIP:
-   - (SD WebUI) `python -m pip install -U pip`
-   - (ComfyUI Portable) `python_embeded\python.exe -m pip install -U pip`
-5. Затем установите Insightface:
-   - (SD WebUI) `pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` (для 3.10) или `pip install insightface-0.7.3-cp311-cp311-win_amd64.whl` (для 3.11) или `pip install insightface-0.7.3-cp312-cp312-win_amd64.whl` (for 3.12)
-   - (ComfyUI Portable) `python_embeded\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl` (для 3.10) или `python_embeded\python.exe -m pip install insightface-0.7.3-cp311-cp311-win_amd64.whl` (для 3.11) или `python_embeded\python.exe -m pip install insightface-0.7.3-cp312-cp312-win_amd64.whl` (for 3.12)
-6. Готово!
-
-### **II. "AttributeError: 'NoneType' object has no attribute 'get'"**
 
 Эта ошибка появляется, если что-то не так с файлом модели `inswapper_128.onnx`
 
-Скачайте вручную по ссылке [отсюда](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx)
+Скачайте вручную по ссылке [отсюда](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx)
 и сохраните в директорию `ComfyUI\models\insightface`, заменив имеющийся файл
 
-### **III. "reactor.execute() got an unexpected keyword argument 'reference_image'"**
+### **II. "reactor.execute() got an unexpected keyword argument 'reference_image'"**
 
 Это означает, что поменялось обозначение входных точек (input points) всвязи с последним обновлением<br>
 Удалите из вашего рабочего пространства имеющийся ReActor Node и добавьте его снова
 
-### **IV. ControlNet Aux Node IMPORT failed - при использовании совместно с нодом ReActor**
+### **III. "fatal: fetch-pack: invalid index-pack output" при исполнении команды `git clone`"**
-
-1. Закройте или остановите ComfyUI сервер, если он запущен
-2. Перейдите в корневую папку ComfyUI, откройте консоль CMD и выполните следующее:
-   - `python_embeded\python.exe -m pip uninstall -y opencv-python opencv-contrib-python opencv-python-headless`
-   - `python_embeded\python.exe -m pip install opencv-python==4.7.0.72`
-3. Готово!
-
-<img src="https://github.com/Gourieff/Assets/blob/main/comfyui-reactor-node/uploads/reactor-w-controlnet.png?raw=true" alt="reactor+controlnet" />
-
-### **V. "ModuleNotFoundError: No module named 'basicsr'" или "subprocess-exited-with-error" при установке пакета future-0.18.3**
-
-- Скачайте https://github.com/Gourieff/Assets/raw/main/comfyui-reactor-node/future-0.18.3-py3-none-any.whl<br>
-- Скопируйте файл в корневую папку ComfyUI и выполните в консоли:
-
-      python_embeded\python.exe -m pip install future-0.18.3-py3-none-any.whl
-
-- Затем:
-
-      python_embeded\python.exe -m pip install basicsr
-
-### **VI. "fatal: fetch-pack: invalid index-pack output" при исполнении команды `git clone`"**
 
 Попробуйте клонировать репозиторий с параметром `--depth=1` (только последний коммит):
 
@@ -470,8 +499,8 @@ SHA256:4c06341c33c2ca1f86781dab0e829f88ad5b64be9fba56e56bc9ebdefc619e43
 | [codeformer-v0.1.0.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/codeformer-v0.1.0.pth) | [sczhou](https://github.com/sczhou/CodeFormer) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
 | [GFPGANv1.3.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.3.pth) | [TencentARC](https://github.com/TencentARC/GFPGAN) | ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) |
 | [GFPGANv1.4.pth](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/facerestore_models/GFPGANv1.4.pth) | [TencentARC](https://github.com/TencentARC/GFPGAN) | ![license](https://img.shields.io/badge/license-Apache_2.0-green.svg) |
-| [inswapper_128.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) | [DeepInsight](https://github.com/deepinsight/insightface) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+| [inswapper_128.onnx](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx) | [DeepInsight](https://github.com/deepinsight/insightface) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
-| [inswapper_128_fp16.onnx](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx) | [Hillobar](https://github.com/Hillobar/Rope) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
+| [inswapper_128_fp16.onnx](https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128_fp16.onnx) | [Hillobar](https://github.com/Hillobar/Rope) | ![license](https://img.shields.io/badge/license-non_commercial-red) |
 
 [BasicSR](https://github.com/XPixelGroup/BasicSR) - [@XPixelGroup](https://github.com/XPixelGroup) <br>
 [facexlib](https://github.com/xinntao/facexlib) - [@xinntao](https://github.com/xinntao) <br>

__init__.py

@@ -1,39 +1,11 @@
 import sys
 import os
 
+# Добавляем путь расширения, чтобы Питон видел наши папки (r_facelib, scripts и т.д.)
 repo_dir = os.path.dirname(os.path.realpath(__file__))
-sys.path.insert(0, repo_dir)
+if repo_dir not in sys.path:
-original_modules = sys.modules.copy()
+    sys.path.insert(0, repo_dir)
 
-# Place aside existing modules if using a1111 web ui
-modules_used = [
-    "modules",
-    "modules.images",
-    "modules.processing",
-    "modules.scripts_postprocessing",
-    "modules.scripts",
-    "modules.shared",
-]
-original_webui_modules = {}
-for module in modules_used:
-    if module in sys.modules:
-        original_webui_modules[module] = sys.modules.pop(module)
-
-# Proceed with node setup
 from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
 
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
-
-# Clean up imports
-# Remove repo directory from path
-sys.path.remove(repo_dir)
-# Remove any new modules
-modules_to_remove = []
-for module in sys.modules:
-    if module not in original_modules and not module.startswith("google.protobuf") and not module.startswith("onnx") and not module.startswith("cv2"):
-        modules_to_remove.append(module)
-for module in modules_to_remove:
-    del sys.modules[module]
-
-# Restore original modules
-sys.modules.update(original_webui_modules)

install.bat

@@ -1,22 +1,25 @@
 @echo off
 setlocal enabledelayedexpansion
 
-:: Try to use embedded python first
+:: Try to use the Desktop version's venv python first
-if exist ..\..\..\python_embeded\python.exe (
+if exist ..\..\.venv\Scripts\python.exe (
-    :: Use the embedded python
+    :: Use the ComfyUI Desktop venv python
+    set PYTHON=..\..\.venv\Scripts\python.exe
+) else if exist ..\..\..\python_embeded\python.exe (
+    :: Use the embedded python (portable version)
     set PYTHON=..\..\..\python_embeded\python.exe
 ) else (
-    :: Embedded python not found, check for python in the PATH
+    :: Neither found, check for python in the PATH
     for /f "tokens=* USEBACKQ" %%F in (`python --version 2^>^&1`) do (
         set PYTHON_VERSION=%%F
     )
     if errorlevel 1 (
-        echo I couldn't find an embedded version of Python, nor one in the Windows PATH. Please install manually.
+        echo I couldn't find a venv python ^(Desktop^), an embedded python ^(Portable^), nor one in the Windows PATH. Please install manually.
         pause
         exit /b 1
     ) else (
         :: Use python from the PATH (if it's the right version and the user agrees)
-        echo I couldn't find an embedded version of Python, but I did find !PYTHON_VERSION! in your Windows PATH.
+        echo I couldn't find a venv or embedded version of Python, but I did find !PYTHON_VERSION! in your Windows PATH.
         echo Would you like to proceed with the install using that version? (Y/N^)
         set /p USE_PYTHON=
         if /i "!USE_PYTHON!"=="Y" (
@@ -30,6 +33,7 @@ if exist ..\..\..\python_embeded\python.exe (
 )
 
 :: Install the package
+echo Using Python: %PYTHON%
 echo Installing...
 %PYTHON% install.py
 echo Done^!

pyproject.toml

@@ -1,13 +1,13 @@
 [project]
 name = "comfyui-reactor"
-description = "(SFW-Friendly) The Fast and Simple Face Swap Extension Node for ComfyUI, based on ReActor SD-WebUI Face Swap Extension"
+description = "(SFW-Friendly) The Fast and Simple Face Swap Extension for ComfyUI"
-version = "0.5.2"
+version = "0.7.1-b3"
 license = { file = "LICENSE" }
-dependencies = ["insightface==0.7.3", "onnx>=1.14.0", "opencv-python>=4.7.0.72", "numpy==1.26.3", "segment_anything", "albumentations>=1.4.16", "ultralytics"]
+dependencies = ["onnx>=1.14.0", "opencv-python>=4.7.0.72", "numpy", "segment_anything", "albumentations>=1.4.16", "ultralytics"]
 
 [project.urls]
 Repository = "https://github.com/Gourieff/ComfyUI-ReActor"
-#  Used by Comfy Registry https://comfyregistry.org
+# Used by Comfy Registry https://comfyregistry.org
 
 [tool.comfy]
 PublisherId = "gourieff"

r_dlssnr/dll/README.md

@@ -0,0 +1,8 @@
+## Third-party DLLs
+
+1. Download [neuroframe_dlls.zip](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/DLSSNR/neuroframe_dlls.zip) and place `neuroframe_caller.dll`\* and `neuroframe_engine.dll`* here (in `r_dlssnr/dll`)
+2. Place your `nvngx_dlssnr.dll`** here (in `r_dlssnr/dll`)
+
+<sub>* Author [Merserk](https://github.com/Merserk), [LICENSE](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/DLSSNR/LICENSE-Merserk.txt)
+<br>
+** Public distribution of this file is prohibited by NVIDIA, [LICENSE](https://huggingface.co/datasets/Gourieff/ReActor/blob/main/DLSSNR/LICENSE-NVIDIA-DLSS.txt)</sub>

r_dlssnr/dlss5_core.py

@@ -0,0 +1,147 @@
+import ctypes
+import threading
+from dataclasses import dataclass
+from typing import Any
+import numpy as np
+
+# --- КОНСТАНТЫ ---
+BRIDGE_ABI_VERSION = 6
+MEMORY_HOST = 0
+MEMORY_CUDA = 1
+MEMORY_NONE = 2
+
+class NeuralBridgeError(Exception):
+    pass
+
+# --- C-СТРУКТУРЫ ДЛЯ ВЗАИМОДЕЙСТВИЯ С DLL ---
+
+class RenderParameters(ctypes.Structure):
+    _fields_ = [
+        ("struct_size", ctypes.c_uint32),
+        ("abi_version", ctypes.c_uint32),
+        ("style", ctypes.c_int32),
+        ("intensity", ctypes.c_float),
+        ("tone", ctypes.c_float),
+        ("structure", ctypes.c_float),
+        ("skin", ctypes.c_float),
+        ("automask", ctypes.c_int32),
+        ("reset", ctypes.c_int32),
+        ("color_strength", ctypes.c_float),
+        ("tone_preservation", ctypes.c_float),
+        ("mask_memory_type", ctypes.c_uint32),
+        ("mask_width", ctypes.c_uint32),
+        ("mask_height", ctypes.c_uint32),
+        ("mask_stride", ctypes.c_uint32),
+        ("mask_plane", ctypes.c_uint64),          # Из-за uint64 здесь будет 4 байта системного отступа
+        ("face_skin_protection", ctypes.c_float),
+        ("grain_preservation", ctypes.c_float),
+        ("nr_passes", ctypes.c_int32),
+        ("shimmer_suppression", ctypes.c_float),
+        ("prefer_nvof", ctypes.c_int32),
+    ]
+
+
+class DLSSStandaloneManager:
+    def __init__(self, dll_dir: str):
+        self._lock = threading.RLock()
+        self._library = None
+        self.dll_dir = dll_dir
+
+    def initialize(self, ordinal: int):
+        with self._lock:
+            if self._library is not None:
+                return True
+                
+            import os
+            
+            if hasattr(os, 'add_dll_directory'):
+                os.add_dll_directory(self.dll_dir)
+                
+            engine_path = os.path.join(self.dll_dir, "neuroframe_engine.dll")
+            if not os.path.exists(engine_path):
+                raise NeuralBridgeError(f"Missing DLL: {engine_path}")
+            
+            loader = getattr(ctypes, "WinDLL", ctypes.CDLL)
+            try:
+                self._library = loader(engine_path)
+            except OSError as exc:
+                raise NeuralBridgeError(f"DLL load failed: {exc}")
+                
+            # Сигнатура инициализации
+            self._library.dlss5nr_init.argtypes = [
+                ctypes.c_int, ctypes.c_wchar_p, ctypes.c_char_p, ctypes.c_int
+            ]
+            self._library.dlss5nr_init.restype = ctypes.c_int
+            
+            # Сигнатура HOST-рендера (process_v6 вместо process_cuda_v6)
+            c_float_p = ctypes.POINTER(ctypes.c_float)
+            self._library.dlss5nr_process_v6.argtypes = [
+                c_float_p, c_float_p, ctypes.c_int, ctypes.c_int,
+                ctypes.POINTER(RenderParameters), ctypes.c_char_p, ctypes.c_int
+            ]
+            self._library.dlss5nr_process_v6.restype = ctypes.c_int
+
+            try:
+                self._library.dlss5nr_frame_abi_version.argtypes = []
+                self._library.dlss5nr_frame_abi_version.restype = ctypes.c_uint32
+                self.actual_abi = self._library.dlss5nr_frame_abi_version()
+            except Exception:
+                self.actual_abi = BRIDGE_ABI_VERSION
+
+            error = ctypes.create_string_buffer(4096)
+            ok = self._library.dlss5nr_init(ordinal, self.dll_dir, error, len(error))
+            
+            if not ok:
+                err_msg = error.value.decode('utf-8', errors='ignore')
+                raise NeuralBridgeError(f"Bridge initialization failed: {err_msg}")
+            
+            return True
+
+    def process_host(self, source: np.ndarray, destination: np.ndarray, settings: dict, reset: bool, mask: np.ndarray = None):
+        with self._lock:
+            error = ctypes.create_string_buffer(4096)
+            
+            params = RenderParameters()
+            params.struct_size = ctypes.sizeof(RenderParameters)
+            params.abi_version = getattr(self, "actual_abi", BRIDGE_ABI_VERSION)
+            
+            params.style = int(settings.get("style"))
+            params.intensity = float(settings.get("intensity"))
+            params.tone = float(settings.get("local_tone"))
+            params.structure = float(settings.get("local_structure"))
+            params.skin = float(settings.get("skin_structure"))
+            params.automask = int(bool(settings.get("auto_mask")))
+            params.reset = int(bool(reset))
+            params.color_strength = float(settings.get("color_strength"))
+            params.tone_preservation = float(settings.get("tone_preservation"))
+            params.face_skin_protection = float(settings.get("face_skin_protection"))
+            params.grain_preservation = float(settings.get("grain_preservation"))
+            params.nr_passes = int(settings.get("nr_passes"))
+            params.shimmer_suppression = float(settings.get("shimmer_suppression", 0.0))
+            params.prefer_nvof = int(bool(settings.get("prefer_nvof", False)))
+            
+            # Обработка маски через HOST память
+            params.mask_memory_type = MEMORY_NONE
+            if mask is not None:
+                params.mask_memory_type = MEMORY_HOST
+                params.mask_width = int(mask.shape[1])
+                params.mask_height = int(mask.shape[0])
+                params.mask_stride = int(mask.strides[0])
+                params.mask_plane = int(mask.ctypes.data) # Передаем указатель RAM
+            
+            c_float_p = ctypes.POINTER(ctypes.c_float)
+            
+            # Вызываем HOST функцию (DLL сама разберется с видеокартой)
+            ok = self._library.dlss5nr_process_v6(
+                source.ctypes.data_as(c_float_p),
+                destination.ctypes.data_as(c_float_p),
+                source.shape[1],
+                source.shape[0],
+                ctypes.byref(params),
+                error,
+                len(error)
+            )
+            
+            if not ok:
+                err_msg = error.value.decode('utf-8', errors='ignore')
+                raise NeuralBridgeError(f"DLSS-5 process failed: {err_msg}")

r_dlssnr/dlss5_node.py

@@ -0,0 +1,113 @@
+import torch
+import numpy as np
+import os
+import comfy.model_management as model_management
+from scripts.reactor_logger import logger
+from .dlss5_core import DLSSStandaloneManager
+from r_modules.shared import state
+from reactor_utils import (
+    batch_tensor_to_pil, 
+    progress_bar,
+    progress_bar_reset
+)
+
+class DLSS5FrameEnhancer:
+    def __init__(self):
+        self.device = model_management.get_torch_device()
+        self.manager = None
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "style": (["Default", "Nature", "Cinematic"],),
+                "intensity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.05, "tooltip": "0..2, def: 1.0"}),
+                "local_tone": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 2.0, "step": 0.05, "tooltip": "0..2, def: 0.0"}),
+                "local_structure": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.05, "tooltip": "0..2, def: 1.0"}),
+                "skin_structure": ("FLOAT", {"default": 0.5, "min": -1.0, "max": 2.0, "step": 0.05, "tooltip": "-1..2, def: 0.5"}),
+                "color_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05, "tooltip": "0..1, def: 0.5"}),
+                "tone_preservation": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05, "tooltip": "0..1, def: 0.5"}),
+                "face_skin_protection": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05, "tooltip": "0..1, def: 0.0"}),
+                "grain_preservation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05, "tooltip": "0..1, def: 0.0"}),
+                "nr_passes": ("INT", {"default": 1, "min": 1, "max": 4, "tooltip": "0..4, def: 1"}),
+                "auto_mask": ("BOOLEAN", {"default": False, "label_off": "OFF", "label_on": "ON", "tooltip": "AI Automatic masking of complex areas to prevent over-detailing"}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("enhanced_image",)
+    FUNCTION = "enhance"
+    CATEGORY = "🌌 ReActor"
+    DESCRIPTION = (
+        "Requirements:\n"
+        "- NVIDIA display driver >= 616.x\n"
+        "- NVIDIA RTX 40/50-series GPU\n"
+        "(compatibility with older RTX series is unconfirmed)\n"
+        "- DLLs: neuroframe_caller.dll, neuroframe_engine.dll, nvngx_dlssnr.dll\nin custom_nodes/ComfyUI-ReActor/r_dlssnr/dll\nor custom_nodes/comfyui-reactor-node/r_dlssnr/dll\n"
+        "(see README.md in dll folder for instructions)"
+    )
+
+    def load_bridge(self):
+        if self.manager is None:
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            dll_dir = os.path.join(current_dir, "dll")
+            self.manager = DLSSStandaloneManager(dll_dir)
+            ordinal = getattr(self.device, 'index', 0) if self.device.index is not None else 0
+            self.manager.initialize(ordinal)
+            logger.status(f"DLSS-5 Bridge initialized on GPU {ordinal}")
+
+    def enhance(self, image, style, intensity, local_tone, local_structure, 
+                skin_structure, color_strength, tone_preservation, 
+                face_skin_protection, grain_preservation, nr_passes, auto_mask, mask=None):
+        
+        self.load_bridge()
+        
+        style_map = {"Default": 0, "Nature": 1, "Cinematic": 2}
+        settings = {
+            "style": style_map[style], "intensity": intensity, "local_tone": local_tone,
+            "local_structure": local_structure, "skin_structure": skin_structure,
+            "color_strength": color_strength, "tone_preservation": tone_preservation,
+            "face_skin_protection": face_skin_protection, "grain_preservation": grain_preservation,
+            "nr_passes": nr_passes, "auto_mask": auto_mask,
+            "shimmer_suppression": 0.0, "prefer_nvof": False
+        }
+
+        enhanced_batch = []
+
+        pil_images = batch_tensor_to_pil(image)
+        pbar = progress_bar(len(pil_images))
+        
+        for i in range(len(image)):
+
+            if state.interrupted or model_management.processing_interrupted():
+                logger.status("Interrupted by User")
+                break
+
+            img_np = np.ascontiguousarray(image[i].cpu().numpy().astype(np.float32))
+            dest_np = np.ascontiguousarray(np.zeros_like(img_np))
+            
+            mask_np = None
+            if mask is not None:
+                mask_np = np.ascontiguousarray(mask[i].cpu().numpy().astype(np.float32))
+            
+            # Вызываем Host-обработчик (без CUDA конфликтов)
+            self.manager.process_host(
+                source=img_np,
+                destination=dest_np,
+                settings=settings,
+                reset=True,
+                mask=mask_np
+            )
+            
+            out_tensor = torch.from_numpy(dest_np).to(self.device) 
+            enhanced_batch.append(out_tensor)
+
+            pbar.update(1)
+
+        progress_bar_reset(pbar)
+        
+        return (torch.stack(enhanced_batch),)

r_facelib/detection/retinaface/retinaface.py

@@ -6,7 +6,7 @@ import torch.nn.functional as F
 from PIL import Image
 from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
 
-from modules import shared
+from r_modules import shared
 
 from r_facelib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
 from r_facelib.detection.retinaface.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head

r_modules/processing.py

@@ -1,13 +1,15 @@
-class StableDiffusionProcessing:
+class Processing:
 
     def __init__(self, init_imgs):
         self.init_images = init_imgs
         self.width = init_imgs[0].width
         self.height = init_imgs[0].height
         self.extra_generation_params = {}
+        self.bbox = []
+        self.swapped_indexes = []
 
 
-class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
+class ProcessingImg2Img(Processing):
 
     def __init__(self, init_img):
         super().__init__(init_img)

reactor_core/analyzer.py

@@ -0,0 +1,103 @@
+import os
+import zipfile
+from reactor_utils import download  # Твоя функция скачивания из utils
+from .face_objects import Face
+from .inswap import SCRFD, ArcFaceONNX, Attribute, Landmark
+from scripts.reactor_logger import logger
+
+class ReActorFaceAnalysis:
+    """
+    Главный класс-оркестратор. 
+    Берет картинку, находит лица, определяет пол/возраст и вычисляет эмбеддинги.
+    """
+    def __init__(self, name="buffalo_l", root="./models/insightface", providers=None):
+        self.name = name
+        self.root = root
+        self.providers = providers or ["CPUExecutionProvider"]
+        self.models = {}
+
+        model_dir = os.path.join(root, "models", name)
+        os.makedirs(model_dir, exist_ok=True)
+
+        det_file = os.path.join(model_dir, "det_10g.onnx")
+        rec_file = os.path.join(model_dir, "w600k_r50.onnx")
+        attr_file = os.path.join(model_dir, "genderage.onnx")
+        lmk2d_file = os.path.join(model_dir, "2d106det.onnx")
+        lmk3d_file = os.path.join(model_dir, "1k3d68.onnx")
+
+        # Если файлов нет - качаем архив
+        if not (os.path.exists(det_file) and os.path.exists(rec_file) and os.path.exists(attr_file)):
+            zip_url = "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/buffalo_l.zip"
+            zip_path = os.path.join(model_dir, f"{name}.zip")
+
+            logger.status(f"Downloading {name} models archive...")
+            download(zip_url, zip_path, f"{name}.zip")
+
+            logger.status(f"Extracting {name} models...")
+            try:
+                with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                    zip_ref.extractall(model_dir)
+                logger.status("Extraction completed!")
+            except zipfile.BadZipFile:
+                logger.error("Downloaded zip file is corrupted. Please try again.")
+            finally:
+                # В любом случае пытаемся удалить архив, чтобы не занимать место
+                if os.path.exists(zip_path):
+                    os.remove(zip_path)
+
+        # Инициализируем только те модели, которые физически есть в папке
+        if os.path.exists(det_file):
+            self.models["detection"] = SCRFD(det_file, providers=self.providers)
+        if os.path.exists(rec_file):
+            self.models["recognition"] = ArcFaceONNX(rec_file, providers=self.providers)
+        if os.path.exists(attr_file):
+            self.models["attribute"] = Attribute(attr_file, providers=self.providers)
+        if os.path.exists(lmk2d_file):
+            self.models["landmark_2d"] = Landmark(lmk2d_file, providers=self.providers)
+        if os.path.exists(lmk3d_file):
+            self.models["landmark_3d"] = Landmark(lmk3d_file, providers=self.providers)
+
+        if "detection" not in self.models:
+            raise FileNotFoundError(
+                f"Detection model (det_10g.onnx) not found at {det_file}. "
+                "Please ensure the buffalo_l models are downloaded and extracted properly."
+            )
+
+    def prepare(self, ctx_id=0, det_size=(640, 640), det_thresh=0.5):
+        self.det_size = det_size
+        self.det_thresh = det_thresh
+
+    def get(self, img, max_num=0):
+        bboxes, kpss = self.models["detection"].detect(
+            img, 
+            det_thresh=self.det_thresh, 
+            input_size=self.det_size, 
+            max_num=max_num
+        )
+        
+        if bboxes.shape[0] == 0:
+            return []
+
+        ret = []
+        for i in range(bboxes.shape[0]):
+            bbox = bboxes[i, 0:4]
+            det_score = bboxes[i, 4]
+            kps = kpss[i] if kpss is not None else None
+            
+            face = Face(bbox=bbox, kps=kps, det_score=det_score)
+
+            if "attribute" in self.models:
+                self.models["attribute"].get(img, face)
+            
+            if "recognition" in self.models:
+                self.models["recognition"].get(img, face)
+            
+            if "landmark_2d" in self.models:
+                self.models["landmark_2d"].get(img, face)
+                
+            if "landmark_3d" in self.models:
+                self.models["landmark_3d"].get(img, face)
+                
+            ret.append(face)
+            
+        return ret

reactor_core/face_objects.py

@@ -0,0 +1,82 @@
+import numpy as np
+import onnxruntime as ort
+import os
+
+class Face(dict):
+    """
+    Класс-хранилище данных о лице. 
+    Наследуется от dict для полной обратной совместимости с кодом, 
+    который ожидает доступ по ключу (например, face['bbox']).
+    """
+    def __init__(self, d=None, **kwargs):
+        if d is None:
+            d = {}
+        if kwargs:
+            d.update(**kwargs)
+        for k, v in d.items():
+            setattr(self, k, v)
+        # Инициализируем родительский словарь
+        super().__init__(d)
+
+    def __setattr__(self, name, value):
+        # Если это массив, делаем копию, чтобы избежать багов с мутацией по ссылке
+        if isinstance(value, (list, tuple)):
+            value = [x for x in value]
+        elif isinstance(value, np.ndarray):
+            value = value.copy()
+        
+        super().__setattr__(name, value)
+        super().__setitem__(name, value)
+
+    def __setitem__(self, key, value):
+        super().__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    @property
+    def sex(self):
+        """Возвращает 'M' или 'F' на основе числового значения gender"""
+        gender = self.get('gender', None)
+        if gender is None:
+            return None
+        return 'M' if gender == 1 else 'F'
+
+    @property
+    def normed_embedding(self):
+        """Автоматически нормализует эмбеддинг для ArcFace / INSwapper"""
+        embedding = self.get('embedding', None)
+        if embedding is None:
+            return None
+        norm = np.linalg.norm(embedding)
+        if norm == 0:
+            return embedding
+        return embedding / norm
+
+
+class BaseONNXModel:
+    """
+    Базовый класс для всех моделей (Детектор, ArcFace, INSwapper).
+    Берет на себя рутину по открытию сессий и чтению входов/выходов.
+    """
+    def __init__(self, model_file, providers=None):
+        self.model_file = model_file
+        self.providers = providers or ["CPUExecutionProvider"]
+        
+        if not os.path.exists(self.model_file):
+            raise FileNotFoundError(f"Model file not found: {self.model_file}")
+
+        self.session = ort.InferenceSession(self.model_file, providers=self.providers)
+        
+        # Получаем параметры входов
+        self.inputs = self.session.get_inputs()
+        self.input_names = [inp.name for inp in self.inputs]
+        
+        # Обычно нас интересует шейп первого входа (например, батч, каналы, высота, ширина)
+        self.input_shape = self.inputs[0].shape
+        
+        # Получаем параметры выходов
+        self.outputs = self.session.get_outputs()
+        self.output_names = [out.name for out in self.outputs]
+
+    def forward(self, *args, **kwargs):
+        """Этот метод будет переопределен в классах-наследниках"""
+        raise NotImplementedError("Forward method must be implemented by subclasses.")

reactor_core/hyperswap.py

@@ -0,0 +1,181 @@
+import cv2
+import numpy as np
+from .face_objects import BaseONNXModel
+
+class HyperSwapper(BaseONNXModel):
+    """Класс для работы с моделями семейства Hyperswap"""
+
+    def __init__(self, model_file, providers=None):
+        super().__init__(model_file, providers)
+
+    # Функция для получения 5 ключевых точек из объекта Face
+    def get_landmarks_5(self, face):
+        if hasattr(face, 'landmark_5') and face.landmark_5 is not None:
+            return face.landmark_5
+        elif hasattr(face, 'kps') and face.kps is not None:
+            return face.kps
+        elif hasattr(face, 'landmark') and face.landmark is not None:
+            if face.landmark.shape[0] >= 68:
+                idxs = [36, 45, 30, 48, 54]
+                return face.landmark[idxs]
+        return None
+
+    # Функция для вычисления аффинного преобразования
+    def get_affine_transform(self, src_pts, dst_pts):
+        M, _ = cv2.estimateAffinePartial2D(src_pts, dst_pts)
+        return M
+    
+    # Создаём градиентную маску овальной формы без обрезки 
+    def create_gradient_mask(self, crop_size=256):
+        # 1. Создаём пустую маску (все пиксели = 0)
+        mask = np.zeros((crop_size, crop_size), dtype=np.float32)
+        
+        # 2. Определяем центр и размеры эллипса
+        center = (crop_size // 2, crop_size // 2)
+        axes = (int(crop_size * 0.35), int(crop_size * 0.4))
+        
+        # 3. Рисуем эллипс (заполняем белым цветом, значение=1.0)
+        cv2.ellipse(
+            mask,          # Массив для рисования
+            center,        # Центр эллипса
+            axes,          # Полуоси (ширина, высота)
+            angle=0,       # Угол поворота
+            startAngle=0,  # Начальный угол дуги
+            endAngle=360,  # Конечный угол дуги (360 = полный эллипс)
+            color=1.0,     # Значение для заполнения (белый = 1.0)
+            thickness=-1   # -1 = заполнить всю область эллипса   
+        )
+        
+        # 4. Применяем размытие для плавных краёв
+        blur_ksize = 15  # Нечётное число, чтобы ядро было симметричным
+        mask = cv2.GaussianBlur(mask, (blur_ksize, blur_ksize), 0)
+        
+        # 5. Ограничим значения в диапазоне [0, 1]
+        mask = np.clip(mask, 0, 1)
+        
+        return mask
+
+    def paste_back(self, target_img, swapped_face, M, crop_size=256):
+        
+        # 1. Создание мягкой маски (Эрозия + Размытие)
+        mask = self.create_gradient_mask(crop_size)
+
+        # Преобразуем в трехканальную маску
+        mask_3c = np.stack([mask] * 3, axis=2)
+
+        # 2. Получаем размеры целевого изображения
+        h, w = target_img.shape[:2]
+
+        # 3. Нормализация swapped_face к float32 [0,1] для warp
+        swapped_face_norm = swapped_face.astype(np.float32) / 255.0
+        mask_norm = mask_3c.astype(np.float32)  # Маска уже [0,1]
+
+        # 4. Обратное преобразование (WARP_INVERSE_MAP) для лица И маски
+        # Используем BORDER_CONSTANT с borderValue=0.5 (серый, чтобы избежать синих/зеленых артефактов)
+        warped_face = cv2.warpAffine(
+            swapped_face_norm,
+            M,
+            (w, h),
+            flags=cv2.INTER_LANCZOS4 | cv2.WARP_INVERSE_MAP,
+            borderMode=cv2.BORDER_CONSTANT,
+            borderValue=0.5
+        )
+        
+        # Для маски (INTER_CUBIC — плавные границы)
+        warped_mask = cv2.warpAffine(
+            mask_norm,
+            M,
+            (w, h),
+            flags=cv2.INTER_CUBIC | cv2.WARP_INVERSE_MAP,
+            borderMode=cv2.BORDER_CONSTANT,
+            borderValue=0.0  # Маска: 0 за пределами
+        )
+        
+        # 5. Обработка после warp: Clip, NaN fix
+        warped_face = np.clip(warped_face, 0, 1)  # Убираем отрицательные
+        warped_face = np.nan_to_num(warped_face, nan=0.5)  # NaN -> серый
+        
+        warped_mask = np.clip(warped_mask, 0, 1)
+        warped_mask = np.nan_to_num(warped_mask, nan=0.0)
+        
+        # 6. Дополнительное размытие для устранения артефактов
+        warped_mask = cv2.GaussianBlur(warped_mask, (3, 3), 0)
+
+        # 7. Плавное наложение в float32
+        target_float = target_img.astype(np.float32) / 255.0
+        result_float = target_float * (1.0 - warped_mask) + warped_face * warped_mask
+        
+        # 8. Обратная нормализация к uint8
+        result = (result_float * 255).clip(0, 255).astype(np.uint8)
+
+        return result
+
+    def visualize_points(self, img, points, color=(0, 255, 0)):
+        img = img.copy()
+        for p in points:
+            cv2.circle(img, tuple(p.astype(int)), 3, color, -1)
+
+    # Итоговая функция run_hyperswap (get) с аффинным преобразованием
+    def get(self, img, target_face, source_face, paste_back=True):
+        # 1. Подготовка эмбеддинга
+        source_embedding = source_face.normed_embedding.reshape(1, -1).astype(np.float32)
+
+        # 2. Получаем 5 точек target
+        target_landmarks_5 = self.get_landmarks_5(target_face)
+        # self.visualize_points(img, target_landmarks_5, (0, 255, 0)) # не для продакшена
+        
+        if target_landmarks_5 is None:
+            return img if paste_back else (None, None)
+
+        # 3. Определение эталонных точек для выравнивания 256x256 (FFHQ Alignment)
+        std_landmarks_256 = np.array([
+            [ 84.87, 105.94],  # Левый глаз
+            [171.13, 105.94],  # Правый глаз
+            [128.00, 146.66],  # Кончик носа
+            [ 96.95, 188.64],  # Левый уголок рта
+            [159.05, 188.64]   # Правый уголок рта
+        ], dtype=np.float32)
+
+        # Вычисляем аффинную матрицу
+        M = self.get_affine_transform(target_landmarks_5.astype(np.float32), std_landmarks_256)
+        
+        # Применяем аффинное преобразование с новой матрицей M
+        crop = cv2.warpAffine(img, M, (256, 256), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REFLECT)
+
+        # 4. Преобразуем crop для модели
+        crop_input = crop[:, :, ::-1].astype(np.float32) / 255.0  # RGB -> [0,1]
+        crop_input = (crop_input - 0.5) / 0.5  # Нормализация
+        crop_input = crop_input.transpose(2, 0, 1)[np.newaxis, ...].astype(np.float32)
+
+        # 5. Инференс
+        try:
+            output = self.session.run(None, {'source': source_embedding, 'target': crop_input})[0][0]
+        except:
+            return img if paste_back else (None, None)
+
+        if isinstance(output, np.ndarray):
+            # устранение NaN и бесконечностей
+            output = np.nan_to_num(output, nan=0.0, posinf=255.0, neginf=0.0)
+
+            # если диапазон похож на [-1,1] → нормализуем в [0,255]
+            if output.min() < 0.0 or output.max() <= 1.5:
+                output = ((output + 1.0) / 2.0 * 255.0)
+            # жёсткое ограничение диапазона и тип для OpenCV
+            output = np.clip(output, 0, 255).astype(np.uint8).copy()
+
+            # защита от повторного использования буфера (inplace CPU bug)
+            try:
+                output.setflags(write=True)
+            except Exception:
+                pass
+        
+        # 6. Обратная нормализация
+        output = output.transpose(1, 2, 0)  # CHW -> HWC
+        output = output[:, :, ::-1]  # BGR -> RGB
+        
+        # 7. Возвращаем результат в зависимости от флага paste_back
+        if not paste_back:
+            return output, M # Возвращаем только кроп лица (256x256) и матрицу M
+        
+        # Если нужна полная вклейка в исходное изображение:
+        return self.paste_back(img, output, M, crop_size=256)

reactor_core/inswap.py

@@ -0,0 +1,440 @@
+import cv2
+import numpy as np
+from .face_objects import BaseONNXModel
+from .meanshape_68 import MEANSHAPE_68
+
+# --- Математика для 3D позы ---
+
+def estimate_affine_matrix_3d23d(X, Y):
+    ''' Вычисляет аффинную матрицу трансформации 3D -> 3D '''
+    X_homo = np.hstack((X, np.ones([X.shape[0], 1])))
+    P = np.linalg.lstsq(X_homo, Y, rcond=None)[0].T
+    return P
+
+def P2sRt(P):
+    ''' Разбивает матрицу проекции '''
+    t1 = np.linalg.norm(P[:,0])
+    t2 = np.linalg.norm(P[:,1])
+    t3 = np.linalg.norm(P[:,2])
+    s = (t1 + t2 + t3) / 3.0
+    P1 = P / s
+    R = P1[:, 0:3]
+    t = P1[:, 3]
+    return s, R, t
+
+def matrix2angle(R):
+    ''' Превращает матрицу поворота в углы Эйлера (pitch, yaw, roll) '''
+    if R[2,0] != 1 and R[2,0] != -1:
+        pitch = -np.arcsin(R[2,0])
+        yaw = np.arctan2(R[2,1]/np.cos(pitch), R[2,2]/np.cos(pitch))
+        roll = np.arctan2(R[1,0]/np.cos(pitch), R[0,0]/np.cos(pitch))
+    else:
+        yaw = 0
+        if R[2,0] == -1:
+            pitch = np.pi/2
+            roll = yaw + np.arctan2(R[0,1], R[0,2])
+        else:
+            pitch = -np.pi/2
+            roll = -yaw + np.arctan2(-R[0,1], -R[0,2])
+    return pitch, yaw, roll
+
+# --- Вспомогательные функции ---
+
+def distance2bbox(points, distance, max_shape=None):
+    x1 = points[:, 0] - distance[:, 0]
+    y1 = points[:, 1] - distance[:, 1]
+    x2 = points[:, 0] + distance[:, 2]
+    y2 = points[:, 1] + distance[:, 3]
+    if max_shape is not None:
+        x1 = np.clip(x1, 0, max_shape[1])
+        y1 = np.clip(y1, 0, max_shape[0])
+        x2 = np.clip(x2, 0, max_shape[1])
+        y2 = np.clip(y2, 0, max_shape[0])
+    return np.stack([x1, y1, x2, y2], axis=-1)
+
+def distance2kps(points, distance, max_shape=None):
+    preds = []
+    for i in range(0, distance.shape[1], 2):
+        px = points[:, i%2] + distance[:, i]
+        py = points[:, i%2+1] + distance[:, i+1]
+        if max_shape is not None:
+            px = np.clip(px, 0, max_shape[1])
+            py = np.clip(py, 0, max_shape[0])
+        preds.append(px)
+        preds.append(py)
+    return np.stack(preds, axis=-1)
+
+# Стандартные точки FFHQ/ArcFace для матрицы трансформации 112x112
+ARCFACE_STD_POINTS = np.array([
+    [38.2946, 51.6963],
+    [73.5318, 51.5014],
+    [56.0252, 71.7366],
+    [41.5493, 92.3655],
+    [70.7299, 92.2041]
+], dtype=np.float32)
+
+def norm_crop(img, landmark, image_size=112):
+    """Выравнивает и обрезает лицо (для ArcFace)"""
+    M, _ = cv2.estimateAffinePartial2D(landmark, ARCFACE_STD_POINTS)
+    warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
+    return warped
+
+# --- Модели ---
+
+class SCRFD(BaseONNXModel):
+    """Детектор лиц (находит bbox и 5 ключевых точек)"""
+    def __init__(self, model_file, providers=None):
+        super().__init__(model_file, providers)
+        self.batched = len(self.outputs[0].shape) == 3
+        self.input_mean = 127.5
+        self.input_std = 128.0
+        self.use_kps = len(self.outputs) in [9, 15]
+        self.fmc = 5 if len(self.outputs) in [10, 15] else 3
+        self._feat_stride_fpn = [8, 16, 32, 64, 128][:self.fmc]
+        self._num_anchors = 2 if self.fmc == 3 else 1
+        self.center_cache = {}
+
+    def forward(self, img, threshold):
+        scores_list, bboxes_list, kpss_list = [], [], []
+        input_size = tuple(img.shape[0:2][::-1])
+        blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, 
+                                     (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        
+        net_outs = self.session.run(self.output_names, {self.input_names[0]: blob})
+        input_height, input_width = blob.shape[2], blob.shape[3]
+
+        for idx, stride in enumerate(self._feat_stride_fpn):
+            if self.batched:
+                scores = net_outs[idx][0]
+                bbox_preds = net_outs[idx + self.fmc][0] * stride
+                if self.use_kps:
+                    kps_preds = net_outs[idx + self.fmc * 2][0] * stride
+            else:
+                scores = net_outs[idx]
+                bbox_preds = net_outs[idx + self.fmc] * stride
+                if self.use_kps:
+                    kps_preds = net_outs[idx + self.fmc * 2] * stride
+
+            height, width = input_height // stride, input_width // stride
+            key = (height, width, stride)
+            
+            if key in self.center_cache:
+                anchor_centers = self.center_cache[key]
+            else:
+                anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
+                anchor_centers = (anchor_centers * stride).reshape((-1, 2))
+                if self._num_anchors > 1:
+                    anchor_centers = np.stack([anchor_centers] * self._num_anchors, axis=1).reshape((-1, 2))
+                if len(self.center_cache) < 100:
+                    self.center_cache[key] = anchor_centers
+
+            pos_inds = np.where(scores >= threshold)[0]
+            bboxes = distance2bbox(anchor_centers, bbox_preds)
+            
+            scores_list.append(scores[pos_inds])
+            bboxes_list.append(bboxes[pos_inds])
+            
+            if self.use_kps:
+                kpss = distance2kps(anchor_centers, kps_preds)
+                kpss = kpss.reshape((kpss.shape[0], -1, 2))
+                kpss_list.append(kpss[pos_inds])
+
+        return scores_list, bboxes_list, kpss_list
+
+    def detect(self, img, det_thresh=0.5, input_size=(640, 640), max_num=0):
+        im_ratio = float(img.shape[0]) / img.shape[1]
+        model_ratio = float(input_size[1]) / input_size[0]
+        
+        if im_ratio > model_ratio:
+            new_height = input_size[1]
+            new_width = int(new_height / im_ratio)
+        else:
+            new_width = input_size[0]
+            new_height = int(new_width * im_ratio)
+            
+        det_scale = float(new_height) / img.shape[0]
+        resized_img = cv2.resize(img, (new_width, new_height))
+        det_img = np.zeros((input_size[1], input_size[0], 3), dtype=np.uint8)
+        det_img[:new_height, :new_width, :] = resized_img
+
+        scores_list, bboxes_list, kpss_list = self.forward(det_img, det_thresh)
+
+        scores = np.vstack(scores_list).ravel()
+        order = scores.argsort()[::-1]
+        bboxes = np.vstack(bboxes_list) / det_scale
+        
+        if self.use_kps:
+            kpss = np.vstack(kpss_list) / det_scale
+
+        pre_det = np.hstack((bboxes, scores[:, None])).astype(np.float32, copy=False)
+        pre_det = pre_det[order, :]
+        keep = self.nms(pre_det)
+        
+        det = pre_det[keep, :]
+        kpss = kpss[order, :, :][keep, :, :] if self.use_kps else None
+
+        if max_num > 0 and det.shape[0] > max_num:
+            area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
+            img_center = img.shape[0] // 2, img.shape[1] // 2
+            offsets = np.vstack([
+                (det[:, 0] + det[:, 2]) / 2 - img_center[1],
+                (det[:, 1] + det[:, 3]) / 2 - img_center[0]
+            ])
+            offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
+            values = area - offset_dist_squared * 2.0
+            bindex = np.argsort(values)[::-1][:max_num]
+            det = det[bindex, :]
+            if kpss is not None:
+                kpss = kpss[bindex, :]
+                
+        return det, kpss
+
+    def nms(self, dets, nms_thresh=0.4):
+        x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        order = scores.argsort()[::-1]
+        keep = []
+        while order.size > 0:
+            i = order[0]
+            keep.append(i)
+            xx1 = np.maximum(x1[i], x1[order[1:]])
+            yy1 = np.maximum(y1[i], y1[order[1:]])
+            xx2 = np.minimum(x2[i], x2[order[1:]])
+            yy2 = np.minimum(y2[i], y2[order[1:]])
+            w = np.maximum(0.0, xx2 - xx1 + 1)
+            h = np.maximum(0.0, yy2 - yy1 + 1)
+            inter = w * h
+            ovr = inter / (areas[i] + areas[order[1:]] - inter)
+            inds = np.where(ovr <= nms_thresh)[0]
+            order = order[inds + 1]
+        return keep
+
+
+class ArcFaceONNX(BaseONNXModel):
+    """Распознаватель лиц (выдает вектор/эмбеддинг)"""
+    def __init__(self, model_file, providers=None):
+        super().__init__(model_file, providers)
+        self.input_mean = 127.5
+        self.input_std = 127.5
+        self.input_size = tuple(self.input_shape[2:4][::-1])
+
+    def get(self, img, face):
+        aimg = norm_crop(img, landmark=face.kps, image_size=self.input_size[0])
+        blob = cv2.dnn.blobFromImage(aimg, 1.0 / self.input_std, self.input_size,
+                                      (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        net_out = self.session.run(self.output_names, {self.input_names[0]: blob})[0]
+        face.embedding = net_out.flatten()
+        return face.embedding
+
+
+class Attribute(BaseONNXModel):
+    """Анализатор атрибутов (выдает пол и возраст)"""
+    def __init__(self, model_file, providers=None):
+        super().__init__(model_file, providers)
+        self.input_mean = 0.0
+        self.input_std = 1.0
+        self.input_size = tuple(self.input_shape[2:4][::-1])
+
+    def get(self, img, face):
+        bbox = face.bbox
+        w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+        center = ((bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2)
+        _scale = self.input_size[0] / (max(w, h) * 1.5)
+        
+        # Простая трансформация для Attribute (не требует 5 точек, только центр и масштаб)
+        M = np.array([
+            [_scale, 0, self.input_size[0] * 0.5 - center[0] * _scale],
+            [0, _scale, self.input_size[1] * 0.5 - center[1] * _scale]
+        ], dtype=np.float32)
+        
+        aimg = cv2.warpAffine(img, M, self.input_size, borderValue=0.0)
+        blob = cv2.dnn.blobFromImage(aimg, 1.0 / self.input_std, self.input_size, 
+                                     (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        
+        pred = self.session.run(self.output_names, {self.input_names[0]: blob})[0][0]
+        
+        # Получаем гендер и возраст
+        gender = int(np.argmax(pred[:2]))
+        age = int(np.round(pred[2] * 100))
+        face.gender = gender
+        face.age = age
+        return gender, age
+
+class INSwapper(BaseONNXModel):
+    """Свопер лиц (модели inswapper_128, reswapper)"""
+    def __init__(self, model_file, providers=None):
+        super().__init__(model_file, providers)
+        self.input_mean = 0.0
+        self.input_std = 255.0
+        self.input_size = tuple(self.input_shape[2:4][::-1])
+
+        # Хак для экономии памяти: импортируем onnx только здесь,
+        # читаем нужную матрицу emap и сразу выгружаем тяжелую модель из RAM.
+        import onnx
+        from onnx import numpy_helper
+        model = onnx.load(self.model_file, load_external_data=False)
+        self.emap = numpy_helper.to_array(model.graph.initializer[-1])
+        del model 
+
+    def get(self, img, target_face, source_face, paste_back=True):
+        # 1. Идеальное позиционирование (1 в 1 как в оригинальном C++ Insightface)
+        # ВАЖНО: Insightface центрирует лицо для INSwapper ТОЛЬКО по оси X! 
+        # По оси Y оно остается прижатым выше, сохраняя оригинальные пропорции.
+        ratio = float(self.input_size[0]) / 128.0
+        diff_x = 8.0 * ratio
+        
+        src_pts = ARCFACE_STD_POINTS.copy() * ratio
+        src_pts[:, 0] += diff_x  # Смещаем ТОЛЬКО координаты X!
+
+        # 2. Вычисляем аффинную матрицу родным методом OpenCV
+        M, _ = cv2.estimateAffinePartial2D(target_face.kps, src_pts)
+        
+        # 3. Кропаем и выравниваем лицо
+        aimg = cv2.warpAffine(img, M, self.input_size, borderValue=0.0)
+        blob = cv2.dnn.blobFromImage(aimg, 1.0 / self.input_std, self.input_size,
+                                      (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+
+        # 4. Подготавливаем эмбеддинг донора
+        latent = source_face.normed_embedding.reshape((1, -1))
+        latent = np.dot(latent, self.emap)
+        latent /= np.linalg.norm(latent)
+
+        # 5. Инференс
+        pred = self.session.run(self.output_names, {
+            self.input_names[0]: blob, 
+            self.input_names[1]: latent.astype(np.float32)
+        })[0]
+
+        img_fake = pred.transpose((0, 2, 3, 1))[0]
+        bgr_fake = np.clip(255 * img_fake, 0, 255).astype(np.uint8)[:, :, ::-1]
+
+        if not paste_back:
+            return bgr_fake, M
+
+        # 6. Обратная вклейка (Paste Back)
+        target_img = img
+        fake_diff = bgr_fake.astype(np.float32) - aimg.astype(np.float32)
+        fake_diff = np.abs(fake_diff).mean(axis=2)
+        
+        # Обрезаем края
+        fake_diff[:2, :] = 0
+        fake_diff[-2:, :] = 0
+        fake_diff[:, :2] = 0
+        fake_diff[:, -2:] = 0
+
+        IM = cv2.invertAffineTransform(M)
+        img_white = np.full((aimg.shape[0], aimg.shape[1]), 255, dtype=np.float32)
+
+        # Возвращаем в исходную перспективу
+        bgr_fake_warped = cv2.warpAffine(bgr_fake, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
+        img_white_warped = cv2.warpAffine(img_white, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
+        fake_diff_warped = cv2.warpAffine(fake_diff, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
+
+        img_white_warped[img_white_warped > 20] = 255
+        fthresh = 10
+        fake_diff_warped[fake_diff_warped < fthresh] = 0
+        fake_diff_warped[fake_diff_warped >= fthresh] = 255
+
+        img_mask = img_white_warped
+        mask_h_inds, mask_w_inds = np.where(img_mask == 255)
+
+        # Защита от пустой маски
+        if len(mask_h_inds) > 0 and len(mask_w_inds) > 0:
+            mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
+            mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
+            mask_size = int(np.sqrt(mask_h * mask_w))
+
+            k = max(mask_size // 10, 10)
+            kernel = np.ones((k, k), np.uint8)
+            img_mask = cv2.erode(img_mask, kernel, iterations=1)
+
+            kernel = np.ones((2, 2), np.uint8)
+            fake_diff_warped = cv2.dilate(fake_diff_warped, kernel, iterations=1)
+
+            k = max(mask_size // 20, 5)
+            blur_size = (k * 2 + 1, k * 2 + 1)
+            img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
+
+            k = 5
+            blur_size = (k * 2 + 1, k * 2 + 1)
+            fake_diff_warped = cv2.GaussianBlur(fake_diff_warped, blur_size, 0)
+
+        img_mask /= 255.0
+        img_mask = np.reshape(img_mask, [img_mask.shape[0], img_mask.shape[1], 1])
+
+        fake_merged = img_mask * bgr_fake_warped + (1.0 - img_mask) * target_img.astype(np.float32)
+        return fake_merged.astype(np.uint8)
+
+class Landmark(BaseONNXModel):
+    """Извлекает 106 (2D) или 68 (3D) точек лица"""
+    def __init__(self, model_file, providers=None):
+        super().__init__(model_file, providers)
+        self.input_mean = 127.5
+        self.input_std = 128.0
+        self.input_size = tuple(self.input_shape[2:4][::-1])
+        
+        output_shape = self.outputs[0].shape
+        # Определяем, какая это модель (3D или 2D) по размеру выхода
+        if output_shape[1] == 3309:
+            self.lmk_dim = 3
+            self.lmk_num = 68
+            self.taskname = 'landmark_3d_68'
+        else:
+            self.lmk_dim = 2
+            self.lmk_num = output_shape[1] // self.lmk_dim
+            self.taskname = f'landmark_2d_{self.lmk_num}'
+
+    def get(self, img, face):
+        bbox = face.bbox
+        w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1])
+        center = ((bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2)
+        _scale = self.input_size[0] / (max(w, h) * 1.5)
+        
+        # Матрица трансформации (выравнивание по центру bbox)
+        M = np.array([
+            [_scale, 0, self.input_size[0] * 0.5 - center[0] * _scale],
+            [0, _scale, self.input_size[1] * 0.5 - center[1] * _scale]
+        ], dtype=np.float32)
+        
+        aimg = cv2.warpAffine(img, M, self.input_size, borderValue=0.0)
+        blob = cv2.dnn.blobFromImage(aimg, 1.0 / self.input_std, self.input_size, 
+                                     (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        
+        pred = self.session.run(self.output_names, {self.input_names[0]: blob})[0][0]
+        
+        if pred.shape[0] >= 3000:
+            pred = pred.reshape((-1, 3))
+        else:
+            pred = pred.reshape((-1, 2))
+            
+        if self.lmk_num < pred.shape[0]:
+            pred = pred[-self.lmk_num:, :]
+            
+        # Денормализация точек в размер модели
+        pred[:, 0:2] += 1
+        pred[:, 0:2] *= (self.input_size[0] // 2)
+        if pred.shape[1] == 3:
+            pred[:, 2] *= (self.input_size[0] // 2)
+
+        # Обратная трансформация точек на оригинальное изображение
+        IM = cv2.invertAffineTransform(M)
+        pred_xy = pred[:, 0:2]
+        pred_xy = np.hstack((pred_xy, np.ones((pred_xy.shape[0], 1)))) # Добавляем гомогенную координату
+        pred_xy = np.dot(IM, pred_xy.T).T
+        
+        if pred.shape[1] == 3:
+            pred = np.hstack((pred_xy, pred[:, 2:3])) # Возвращаем Z
+        else:
+            pred = pred_xy
+
+        # Сохраняем в объект Face под правильным именем
+        setattr(face, self.taskname, pred)
+        
+        # Честный расчет 3D позы
+        if self.taskname == 'landmark_3d_68':
+            P = estimate_affine_matrix_3d23d(MEANSHAPE_68, pred)
+            _, R, _ = P2sRt(P)
+            rx, ry, rz = matrix2angle(R)
+            face.pose = np.array([rx, ry, rz], dtype=np.float32)
+            
+        return pred

reactor_core/meanshape_68.py

@@ -0,0 +1,348 @@
+import numpy as np
+
+# эталонная 3D-модель "усредненного" человеческого лица (68 точек в 3D-пространстве)
+
+matrix = [
+    [
+        -0.6266950368881226,
+        -0.2926996946334839,
+        -0.3140018582344055
+    ],
+    [
+        -0.5996649265289307,
+        -0.12250272184610367,
+        -0.2924409508705139
+    ],
+    [
+        -0.571026086807251,
+        0.05118739977478981,
+        -0.2549465000629425
+    ],
+    [
+        -0.5338566899299622,
+        0.21284838020801544,
+        -0.1866333782672882
+    ],
+    [
+        -0.4797332286834717,
+        0.3506459593772888,
+        -0.047699932008981705
+    ],
+    [
+        -0.3957556486129761,
+        0.4465116560459137,
+        0.07328663021326065
+    ],
+    [
+        -0.29880842566490173,
+        0.5106605887413025,
+        0.17977793514728546
+    ],
+    [
+        -0.18838681280612946,
+        0.554440438747406,
+        0.3166600465774536
+    ],
+    [
+        0.0014708322705700994,
+        0.584439218044281,
+        0.38841578364372253
+    ],
+    [
+        0.19099053740501404,
+        0.5517070889472961,
+        0.31433114409446716
+    ],
+    [
+        0.3269285559654236,
+        0.48957568407058716,
+        0.16839952766895294
+    ],
+    [
+        0.4400261342525482,
+        0.4023579955101013,
+        0.03596242889761925
+    ],
+    [
+        0.5068787336349487,
+        0.3116249442100525,
+        -0.09476063400506973
+    ],
+    [
+        0.540894627571106,
+        0.20452618598937988,
+        -0.20267142355442047
+    ],
+    [
+        0.574118435382843,
+        0.04570329561829567,
+        -0.2841764986515045
+    ],
+    [
+        0.5991416573524475,
+        -0.14585931599140167,
+        -0.296495646238327
+    ],
+    [
+        0.6275436282157898,
+        -0.30774804949760437,
+        -0.30199483036994934
+    ],
+    [
+        -0.47466811537742615,
+        -0.4376046359539032,
+        0.23648710548877716
+    ],
+    [
+        -0.4166599214076996,
+        -0.4717560112476349,
+        0.3159925639629364
+    ],
+    [
+        -0.3475387990474701,
+        -0.4840780198574066,
+        0.366113543510437
+    ],
+    [
+        -0.26063987612724304,
+        -0.4763909876346588,
+        0.3992317318916321
+    ],
+    [
+        -0.16712301969528198,
+        -0.45777544379234314,
+        0.4166091978549957
+    ],
+    [
+        0.1231740415096283,
+        -0.45874011516571045,
+        0.4250619411468506
+    ],
+    [
+        0.2063615769147873,
+        -0.4804193377494812,
+        0.41578391194343567
+    ],
+    [
+        0.28667303919792175,
+        -0.4901494085788727,
+        0.3919537663459778
+    ],
+    [
+        0.3623969256877899,
+        -0.4768601655960083,
+        0.3527863025665283
+    ],
+    [
+        0.425568163394928,
+        -0.4500580430030823,
+        0.2953187823295593
+    ],
+    [
+        -0.007627937477082014,
+        -0.3230886459350586,
+        0.46194377541542053
+    ],
+    [
+        -0.007876846939325333,
+        -0.255738765001297,
+        0.5104694366455078
+    ],
+    [
+        -0.007687545381486416,
+        -0.19917990267276764,
+        0.5525456666946411
+    ],
+    [
+        -0.007345120422542095,
+        -0.14261691272258759,
+        0.5986667275428772
+    ],
+    [
+        -0.144961878657341,
+        0.033126939088106155,
+        0.4196909964084625
+    ],
+    [
+        -0.08434253931045532,
+        0.03127360716462135,
+        0.4731740951538086
+    ],
+    [
+        -0.005499685648828745,
+        0.03975145146250725,
+        0.5146695971488953
+    ],
+    [
+        0.06346292793750763,
+        0.04613516479730606,
+        0.4792240560054779
+    ],
+    [
+        0.13398391008377075,
+        0.02204025723040104,
+        0.41907867789268494
+    ],
+    [
+        -0.38675081729888916,
+        -0.3133975565433502,
+        0.259631872177124
+    ],
+    [
+        -0.3166607916355133,
+        -0.35007160902023315,
+        0.3285270035266876
+    ],
+    [
+        -0.2341379076242447,
+        -0.35491427779197693,
+        0.3334933817386627
+    ],
+    [
+        -0.15516234934329987,
+        -0.31524932384490967,
+        0.3143281042575836
+    ],
+    [
+        -0.23092176020145416,
+        -0.28427040576934814,
+        0.3255828619003296
+    ],
+    [
+        -0.3175090253353119,
+        -0.28516536951065063,
+        0.3098825216293335
+    ],
+    [
+        0.13895708322525024,
+        -0.30982404947280884,
+        0.3182835876941681
+    ],
+    [
+        0.21945592761039734,
+        -0.35319215059280396,
+        0.33802759647369385
+    ],
+    [
+        0.30174651741981506,
+        -0.349665105342865,
+        0.333102285861969
+    ],
+    [
+        0.37665316462516785,
+        -0.31351813673973083,
+        0.26322856545448303
+    ],
+    [
+        0.2966947853565216,
+        -0.2871439754962921,
+        0.3220130503177643
+    ],
+    [
+        0.21462441980838776,
+        -0.2905276417732239,
+        0.33124178647994995
+    ],
+    [
+        -0.20143844187259674,
+        0.23736143112182617,
+        0.37953662872314453
+    ],
+    [
+        -0.13732077181339264,
+        0.18578563630580902,
+        0.46525245904922485
+    ],
+    [
+        -0.07648587226867676,
+        0.15119342505931854,
+        0.5035716891288757
+    ],
+    [
+        -0.002535885199904442,
+        0.16872699558734894,
+        0.5164376497268677
+    ],
+    [
+        0.06442102789878845,
+        0.15088020265102386,
+        0.5045241117477417
+    ],
+    [
+        0.126465305685997,
+        0.17947596311569214,
+        0.4685916602611542
+    ],
+    [
+        0.21824784576892853,
+        0.23899227380752563,
+        0.37567368149757385
+    ],
+    [
+        0.13288259506225586,
+        0.28392839431762695,
+        0.4400508999824524
+    ],
+    [
+        0.06802233308553696,
+        0.2973543107509613,
+        0.4774041771888733
+    ],
+    [
+        -0.0004690653004217893,
+        0.30004069209098816,
+        0.4871094226837158
+    ],
+    [
+        -0.06934267282485962,
+        0.29696860909461975,
+        0.4805404841899872
+    ],
+    [
+        -0.14252015948295593,
+        0.2743033170700073,
+        0.43808019161224365
+    ],
+    [
+        -0.1781347393989563,
+        0.23059049248695374,
+        0.3963589668273926
+    ],
+    [
+        -0.07403063774108887,
+        0.2147187888622284,
+        0.4653262794017792
+    ],
+    [
+        -0.002636224264279008,
+        0.21414154767990112,
+        0.4832296073436737
+    ],
+    [
+        0.05981616675853729,
+        0.21076396107673645,
+        0.47224411368370056
+    ],
+    [
+        0.16690002381801605,
+        0.23127099871635437,
+        0.396789014339447
+    ],
+    [
+        0.059809282422065735,
+        0.22376428544521332,
+        0.46641337871551514
+    ],
+    [
+        -0.0014343614457175136,
+        0.2257590889930725,
+        0.4752439558506012
+    ],
+    [
+        -0.07522077113389969,
+        0.23065608739852905,
+        0.4671475291252136
+    ]
+]
+
+MEANSHAPE_68 = np.array(matrix, dtype=np.float32)

reactor_patcher.py

@@ -1,161 +0,0 @@
-import os.path as osp
-import glob
-import logging
-import insightface
-from insightface.model_zoo.model_zoo import ModelRouter, PickableInferenceSession
-from insightface.model_zoo.retinaface import RetinaFace
-from insightface.model_zoo.landmark import Landmark
-from insightface.model_zoo.attribute import Attribute
-from insightface.model_zoo.inswapper import INSwapper
-from insightface.model_zoo.arcface_onnx import ArcFaceONNX
-from insightface.app import FaceAnalysis
-from insightface.utils import DEFAULT_MP_NAME, ensure_available
-from insightface.model_zoo import model_zoo
-import onnxruntime
-import onnx
-from onnx import numpy_helper
-from scripts.reactor_logger import logger
-
-
-def patched_get_model_log(self, **kwargs):
-    session = PickableInferenceSession(self.onnx_file, **kwargs)
-    print(f'Applied providers: {session._providers}, with options: {session._provider_options}')
-    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 len(inputs) == 2 and input_shape[2] == 256 and input_shape[3] == 256:
-        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_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 len(inputs) == 2 and input_shape[2] == 256 and input_shape[3] == 256:
-        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 pathced_retinaface_prepare(self, ctx_id, **kwargs):
-    if ctx_id<0:
-        self.session.set_providers(['CPUExecutionProvider'])
-    nms_thresh = kwargs.get('nms_thresh', None)
-    if nms_thresh is not None:
-        self.nms_thresh = nms_thresh
-    det_thresh = kwargs.get('det_thresh', None)
-    if det_thresh is not None:
-        self.det_thresh = det_thresh
-    input_size = kwargs.get('input_size', None)
-    if input_size is not None and self.input_size is None:
-        self.input_size = input_size
-
-
-def patch_insightface(get_model, faceanalysis_init, faceanalysis_prepare, inswapper_init, retinaface_prepare):
-    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
-    insightface.model_zoo.retinaface.RetinaFace.prepare = retinaface_prepare
-
-
-# original_functions = [ModelRouter.get_model, FaceAnalysis.__init__, FaceAnalysis.prepare, INSwapper.__init__, RetinaFace.prepare]
-original_functions = [patched_get_model_log, FaceAnalysis.__init__, FaceAnalysis.prepare, INSwapper.__init__, RetinaFace.prepare]
-patched_functions = [patched_get_model, patched_faceanalysis_init, patched_faceanalysis_prepare, patched_inswapper_init, pathced_retinaface_prepare]
-
-
-def apply_patch(console_log_level):
-    if console_log_level == 0:
-        patch_insightface(*patched_functions)
-        logger.setLevel(logging.WARNING)
-    elif console_log_level == 1:
-        patch_insightface(*patched_functions)
-        logger.setLevel(logging.STATUS)
-    elif console_log_level == 2:
-        patch_insightface(*original_functions)
-        logger.setLevel(logging.INFO)

reactor_utils.py

@@ -7,13 +7,14 @@ import cv2
 import math
 import logging
 import hashlib
-from insightface.app.common import Face
+from reactor_core.face_objects import Face
 from safetensors.torch import save_file, safe_open
 from tqdm import tqdm
 import urllib.request
 import onnxruntime
 from typing import Any
 import folder_paths
+from comfy.utils import ProgressBar
 
 ORT_SESSION = None
 
@@ -24,6 +25,34 @@ def tensor_to_pil(img_tensor, batch_index=0):
     img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
     return img
 
+# def tensor_to_pil(img, batch_index=0):
+#     """Безопасное преобразование тензора в PIL.Image с обработкой особых случаев"""
+#     try:
+#         # Обработка пакетных данных
+#         if isinstance(img, torch.Tensor):
+#             if len(img.shape) == 4:
+#                 img = img[batch_index]  # Выбор элемента батча
+#             img = img.detach().cpu().numpy()
+        
+#         # Нормализация и приведение типа
+#         if img.dtype == np.float32:
+#             img = np.clip(255. * img, 0, 255).astype(np.uint8)
+        
+#         # Обработка нестандартных размерностей
+#         if img.shape[-1] > 4:  # Если каналов больше 4
+#             img = img[..., :3]  # Берем первые 3 канала
+        
+#         # Преобразование в 2D/3D массив
+#         if len(img.shape) == 3 and img.shape[0] == 1:  # [C, H, W] → [H, W]
+#             img = img.squeeze(0)
+#         elif len(img.shape) == 3 and img.shape[2] == 1:  # [H, W, C=1]
+#             img = img.squeeze(-1)
+        
+#         return Image.fromarray(img)
+    
+#     except Exception as e:
+#         raise RuntimeError(f"Невозможно преобразовать тензор формы {img.shape} в PIL.Image") from e
+
 
 def batch_tensor_to_pil(img_tensor):
     # Convert tensor of shape [batch_size, channels, height, width] to a list of PIL Images
@@ -208,6 +237,14 @@ def normalize_cropped_face(cropped_face):
 	return cropped_face
 
 
+def progress_bar(total):
+    return ProgressBar(total)
+
+def progress_bar_reset(pbar):
+    pbar.current = 0
+    pbar.update(0)
+
+
 # author: Trung0246 --->
 def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
     # Iterate over the list of full folder paths

requirements.txt

@@ -1,7 +1,6 @@
 albumentations>=1.4.16
-insightface==0.7.3
 onnx>=1.14.0
 opencv-python>=4.7.0.72
-numpy==1.26.3
+numpy
 segment_anything
 ultralytics

scripts/r_masking/core.py

@@ -312,15 +312,24 @@ class SafeToGPU:
         if is_same_device(device, 'cpu'):
             obj.to(device)
         else:
-            if is_same_device(obj.device, 'cpu'):  # cpu to gpu
+            # TRY to check submodule device
+            current_device = None
+            try:
+                current_device = next(obj.parameters()).device
+            except:
+                pass
+
+            if current_device is None or is_same_device(current_device, 'cpu'):
                 model_management.free_memory(self.size * 1.3, device)
                 if model_management.get_free_memory(device) > self.size * 1.3:
                     try:
+                        print("Moving to GPU...", end=" ")
                         obj.to(device)
+                        print("OK")
                     except:
-                        print(f"WARN: The model is not moved to the '{device}' due to insufficient memory. [1]")
+                        print(f"Failed\nWARN: Model not moved to '{device}' [1]")
                 else:
-                    print(f"WARN: The model is not moved to the '{device}' due to insufficient memory. [2]")
+                    print(f"WARN: Model not moved to '{device}' [2]")
 
 def center_of_bbox(bbox):
     w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
@@ -532,9 +541,10 @@ def merge_and_stack_masks(stacked_masks, group_size):
 
 def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
                             threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
-    if sam_model.is_auto_mode:
+    if getattr(sam_model, 'is_auto_mode', False):
         device = model_management.get_torch_device()
-        sam_model.safe_to.to_device(sam_model, device=device)
+        # sam_model.safe_to.to_device(sam_model, device=device)
+        sam_model.to(device)
 
     try:
         predictor = SamPredictor(sam_model)
@@ -588,18 +598,29 @@ def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
         mask = combine_masks2(total_masks)
 
     finally:
-        if sam_model.is_auto_mode:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-            sam_model.cpu()
+        sam_model.to(device)
+        # if sam_model.is_auto_mode:
+        #     sam_model.cpu()
 
         pass
 
-    mask_working_device = torch.device("cpu")
+    # mask_working_device = torch.device("cpu")
+    mask_working_device = model_management.get_torch_device()
 
     if mask is not None:
         mask = mask.float()
-        mask = dilate_mask(mask.cpu().numpy(), dilation)
+        # mask = dilate_mask(mask.cpu().numpy(), dilation)
-        mask = torch.from_numpy(mask)
+        # mask = torch.from_numpy(mask)
-        mask = mask.to(device=mask_working_device)
+        # mask = mask.to(device=mask_working_device)
+        mask = mask.unsqueeze(0).unsqueeze(0)  # [1,1,H,W]
+        if dilation > 0:
+            kernel_size = 1 + dilation * 2
+            mask = torch.nn.functional.max_pool2d(mask, kernel_size=kernel_size, stride=1, padding=dilation)
+        elif dilation < 0:
+            kernel_size = 1 + abs(dilation) * 2
+            mask = -torch.nn.functional.max_pool2d(-mask, kernel_size=kernel_size, stride=1, padding=abs(dilation))
+        mask = mask.squeeze(0).squeeze(0)  # [H,W]
     else:
         # Extracting batch, height and width
         height, width, _ = image.shape
@@ -641,7 +662,8 @@ def tensor2rgba(t: torch.Tensor) -> torch.Tensor:
     elif size[3] == 1:
         return t.repeat(1, 1, 1, 4)
     elif size[3] == 3:
-        alpha_tensor = torch.ones((size[0], size[1], size[2], 1))
+        # alpha_tensor = torch.ones((size[0], size[1], size[2], 1)).to(t.device)
+        alpha_tensor = t.new_ones((size[0], size[1], size[2], 1))
         return torch.cat((t, alpha_tensor), dim=3)
     else:
         return t

scripts/reactor_faceswap.py

@@ -2,14 +2,14 @@ import os, glob
 
 from PIL import Image
 
-import modules.scripts as scripts
+import r_modules.scripts as scripts
 # from modules.upscaler import Upscaler, UpscalerData
-from modules import scripts, scripts_postprocessing
+from r_modules import scripts, scripts_postprocessing
-from modules.processing import (
+from r_modules.processing import (
-    StableDiffusionProcessing,
+    Processing,
-    StableDiffusionProcessingImg2Img,
+    ProcessingImg2Img,
 )
-from modules.shared import state
+from r_modules.shared import state
 from scripts.reactor_logger import logger
 from scripts.reactor_swapper import (
     swap_face,
@@ -26,7 +26,8 @@ import comfy.model_management as model_management
 def get_models():
     swappers = [
         "insightface",
-        "reswapper"
+        "reswapper",
+        "hyperswap"
     ]
     models_list = []
     for folder in swappers:
@@ -42,7 +43,7 @@ class FaceSwapScript(scripts.Script):
 
     def process(
         self,
-        p: StableDiffusionProcessing,
+        p: Processing,
         img,
         enable,
         source_faces_index,
@@ -101,12 +102,12 @@ class FaceSwapScript(scripts.Script):
                 self.gender_target = 2
 
             # if self.source is not None:
-            if isinstance(p, StableDiffusionProcessingImg2Img) and swap_in_source:
+            if isinstance(p, ProcessingImg2Img) and swap_in_source:
                 logger.status(f"Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
 
                 if len(p.init_images) == 1:
 
-                    result = swap_face(
+                    result, bbox, swapped_indexes = swap_face(
                         self.source,
                         p.init_images[0],
                         source_faces_index=self.source_faces_index,
@@ -123,27 +124,11 @@ class FaceSwapScript(scripts.Script):
                         interpolation=self.interpolation,
                     )
                     p.init_images[0] = result
-
+                    p.bbox = bbox
-                    # for i in range(len(p.init_images)):
+                    p.swapped_indexes = swapped_indexes
-                    #     if state.interrupted or model_management.processing_interrupted():
-                    #         logger.status("Interrupted by User")
-                    #         break
-                    #     if len(p.init_images) > 1:
-                    #         logger.status(f"Swap in %s", i)
-                    #     result = swap_face(
-                    #         self.source,
-                    #         p.init_images[i],
-                    #         source_faces_index=self.source_faces_index,
-                    #         faces_index=self.faces_index,
-                    #         model=self.model,
-                    #         gender_source=self.gender_source,
-                    #         gender_target=self.gender_target,
-                    #         face_model=self.face_model,
-                    #     )
-                    #     p.init_images[i] = result
 
                 elif len(p.init_images) > 1:
-                    result = swap_face_many(
+                    result, bbox, swapped_indexes = swap_face_many(
                         self.source,
                         p.init_images,
                         source_faces_index=self.source_faces_index,
@@ -160,34 +145,7 @@ class FaceSwapScript(scripts.Script):
                         interpolation=self.interpolation,
                     )
                     p.init_images = result
+                    p.bbox = bbox
+                    p.swapped_indexes = swapped_indexes
 
                 logger.status("--Done!--")
-            # else:
-            #     logger.error(f"Please provide a source face")
-
-    def postprocess_batch(self, p, *args, **kwargs):
-        if self.enable:
-            images = kwargs["images"]
-
-    def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
-        if self.enable and self.swap_in_generated:
-            if self.source is not None:
-                logger.status(f"Working: source face index %s, target face index %s", self.source_faces_index, self.faces_index)
-                image: Image.Image = script_pp.image
-                result = swap_face(
-                    self.source,
-                    image,
-                    source_faces_index=self.source_faces_index,
-                    faces_index=self.faces_index,
-                    model=self.model,
-                    upscale_options=self.upscale_options,
-                    gender_source=self.gender_source,
-                    gender_target=self.gender_target,
-                )
-                try:
-                    pp = scripts_postprocessing.PostprocessedImage(result)
-                    pp.info = {}
-                    p.extra_generation_params.update(pp.info)
-                    script_pp.image = pp.image
-                except:
-                    logger.error(f"Cannot create a result image")

scripts/reactor_logger.py

@@ -2,7 +2,7 @@ import logging
 import copy
 import sys
 
-from modules import shared
+from r_modules import shared
 from reactor_utils import addLoggingLevel
 
 

scripts/reactor_sfw.py

@@ -1,18 +1,55 @@
 from transformers import pipeline
 from PIL import Image
+import io
 import logging
+import os
+import comfy.model_management as model_management
+from reactor_utils import download
 from scripts.reactor_logger import logger
 
-SCORE = 0.96
+MODEL_EXISTS = False
+
+def ensure_nsfw_model(nsfwdet_model_path):
+    """Download NSFW detection model if it doesn't exist"""
+    global MODEL_EXISTS
+    downloaded = 0
+    nd_urls = [
+        "https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/config.json",
+        "https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/model.safetensors",
+        "https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/preprocessor_config.json",
+    ]
+    for model_url in nd_urls:
+        model_name = os.path.basename(model_url)
+        model_path = os.path.join(nsfwdet_model_path, model_name)
+        if not os.path.exists(model_path):
+            if not os.path.exists(nsfwdet_model_path):
+                os.makedirs(nsfwdet_model_path)
+            download(model_url, model_path, model_name)
+        if os.path.exists(model_path):
+            downloaded += 1
+    MODEL_EXISTS = True if downloaded == 3 else False
+    return MODEL_EXISTS
+
+SCORE = 0.979
 
 logging.getLogger("transformers").setLevel(logging.ERROR)
 
-def nsfw_image(img_path: str, model_path: str):
+def nsfw_image(img_data, model_path: str):
-
+    if not MODEL_EXISTS:
-    with Image.open(img_path) as img:
+        logger.status("Ensuring NSFW detection model exists...")
-        predict = pipeline("image-classification", model=model_path)
+        if not ensure_nsfw_model(model_path):
+            return True
+    device = model_management.get_torch_device()
+    with Image.open(io.BytesIO(img_data)) as img:
+        if "cpu" in str(device):
+            predict = pipeline("image-classification", model=model_path)
+        else:
+            device_id = 0
+            if "cuda" in str(device):
+                device_id = int(str(device).split(":")[1])
+            predict = pipeline("image-classification", model=model_path, device=device_id)
         result = predict(img)
         if result[0]["label"] == "nsfw" and result[0]["score"] > SCORE:
-            logger.status(f"NSFW content detected, skipping...")
+            logger.status(f'NSFW content detected with score={result[0]["score"]}, skipping...')
             return True
         return False

scripts/reactor_swapper.py

@@ -6,22 +6,22 @@ import cv2
 import numpy as np
 from PIL import Image
 
-import insightface
+from reactor_core.analyzer import ReActorFaceAnalysis
-from insightface.app.common import Face
+from reactor_core.face_objects import Face
-# try:
+from reactor_core.inswap import INSwapper
-#     import torch.cuda as cuda
+from reactor_core.hyperswap import HyperSwapper
-# except:
-#     cuda = None
 import torch
 
 import folder_paths
 import comfy.model_management as model_management
-from modules.shared import state
+from r_modules.shared import state
 
 from scripts.reactor_logger import logger
 from reactor_utils import (
     move_path,
     get_image_md5hash,
+    progress_bar,
+    progress_bar_reset
 )
 from scripts.r_faceboost import swapper, restorer
 
@@ -43,13 +43,6 @@ try:
 except Exception as e:
     logger.debug(f"ExecutionProviderError: {e}.\nEP is set to CPU.")
     providers = ["CPUExecutionProvider"]
-# if cuda is not None:
-#     if cuda.is_available():
-#         providers = ["CUDAExecutionProvider"]
-#     else:
-#         providers = ["CPUExecutionProvider"]
-# else:
-#     providers = ["CPUExecutionProvider"]
 
 models_path_old = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
 insightface_path_old = os.path.join(models_path_old, "insightface")
@@ -59,6 +52,7 @@ models_path = folder_paths.models_dir
 insightface_path = os.path.join(models_path, "insightface")
 insightface_models_path = os.path.join(insightface_path, "models")
 reswapper_path = os.path.join(models_path, "reswapper")
+hyperswap_path = os.path.join(models_path, "hyperswap")
 
 if os.path.exists(models_path_old):
     move_path(insightface_models_path_old, insightface_models_path)
@@ -86,11 +80,6 @@ TARGET_IMAGE_LIST_HASH = []
 
 def unload_model(model):
     if model is not None:
-        # check if model has unload method
-        # if "unload" in model:
-        #     model.unload()
-        # if "model_unload" in model:
-        #     model.model_unload()
         del model
     return None
 
@@ -108,7 +97,7 @@ def getAnalysisModel(det_size = (640, 640)):
     global ANALYSIS_MODELS
     ANALYSIS_MODEL = ANALYSIS_MODELS[str(det_size[0])]
     if ANALYSIS_MODEL is None:
-        ANALYSIS_MODEL = insightface.app.FaceAnalysis(
+        ANALYSIS_MODEL = ReActorFaceAnalysis(
             name="buffalo_l", providers=providers, root=insightface_path
         )
     ANALYSIS_MODEL.prepare(ctx_id=0, det_size=det_size)
@@ -120,11 +109,18 @@ def getFaceSwapModel(model_path: str):
     if FS_MODEL is None or CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
         CURRENT_FS_MODEL_PATH = model_path
         FS_MODEL = unload_model(FS_MODEL)
-        FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
+
+        model_filename = os.path.basename(model_path)
+        if "hyperswap" in model_filename.lower(): # Если это Hyperswap
+            model_path = os.path.join(folder_paths.models_dir, "hyperswap", model_filename)
+            FS_MODEL = HyperSwapper(model_path, providers=providers)
+        else: # Если это INSwapper / Reswapper
+            if "reswapper" in model_filename.lower():
+                model_path = os.path.join(folder_paths.models_dir, "reswapper", model_filename)
+            FS_MODEL = INSwapper(model_path, providers=providers)
 
     return FS_MODEL
 
-
 def sort_by_order(face, order: str):
     if order == "left-right":
         return sorted(face, key=lambda x: x.bbox[0])
@@ -136,42 +132,47 @@ def sort_by_order(face, order: str):
         return sorted(face, key=lambda x: x.bbox[1], reverse = True)
     if order == "small-large":
         return sorted(face, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))
-    # if order == "large-small":
-    #     return sorted(face, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse = True)
     # by default "large-small":
     return sorted(face, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse = True)
 
 def get_face_gender(
-        face,
+    face,
-        face_index,
+    face_index,
-        gender_condition,
+    gender_condition,
-        operated: str,
+    operated: str,
-        order: str,
+    order: str,
 ):
-    gender = [
+    # 1. Сортируем ВСЕ найденные лица (без фильтрации!)
-        x.sex
+    faces_sorted = sort_by_order(face, order)
-        for x in face
+
-    ]
+    # 2. Проверяем, существует ли вообще лицо с таким визуальным индексом
-    gender.reverse()
+    if face_index >= len(faces_sorted):
-    # If index is outside of bounds, return None, avoid exception
+        logger.info("Requested face index (%s) is out of bounds (max available index is %s)", face_index, len(faces_sorted) - 1)
-    if face_index >= len(gender):
+        return None, 0, None
-        logger.status("Requested face index (%s) is out of bounds (max available index is %s)", face_index, len(gender))
+
-        return None, 0
+    # 3. Берем конкретное лицо по его позиции на фото (например, второе справа)
-    face_gender = gender[face_index]
+    face_selected = faces_sorted[face_index]
-    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"):
+    # Если фильтр по полу отключен (no) - сразу отдаем лицо в работу
-        logger.status("OK - Detected Gender matches Condition")
+    if gender_condition == 0:
-        try:
+        return face_selected, 0, face_index
-            faces_sorted = sort_by_order(face, order)
+
-            return faces_sorted[face_index], 0
+    # 4. Проверяем пол выбранного лица
-            # return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
+    # face.gender: 0 = female, 1 = male
-        except IndexError:
+    # gender_condition: 1 = female, 2 = male
-            return None, 0
+    expected_gender = 0 if gender_condition == 1 else 1
-    else:
+    actual_gender = getattr(face_selected, 'gender', -1)
-        logger.status("WRONG - Detected Gender doesn't match Condition")
+    
-        faces_sorted = sort_by_order(face, order)
+    sel_gender_str = "Male" if actual_gender == 1 else "Female" if actual_gender == 0 else "Unknown"
-        return faces_sorted[face_index], 1
+    logger.info("%s Face %s: Detected Gender -%s-", operated, face_index, sel_gender_str)
-        # return sorted(face, key=lambda x: x.bbox[0])[face_index], 1
+
+    # Если пол не совпадает с тем, что заказал юзер
+    if actual_gender != expected_gender:
+        logger.info(f"{operated} Face {face_index}: WRONG gender ({sel_gender_str})")
+        return face_selected, 1, face_index  # 1 означает флаг wrong_gender = True (цикл его пропустит)
+
+    # Если всё идеально
+    return face_selected, 0, face_index
 
 def half_det_size(det_size):
     logger.status("Trying to halve 'det_size' parameter")
@@ -179,7 +180,15 @@ def half_det_size(det_size):
 
 def analyze_faces(img_data: np.ndarray, det_size=(640, 640)):
     face_analyser = getAnalysisModel(det_size)
-    faces = face_analyser.get(img_data)
+
+    faces = []
+    try:
+        faces = face_analyser.get(img_data)
+    except Exception as e:
+        # import traceback
+        # traceback.print_exc()
+        # logger.error(f"Error during face analysis: {e}")
+        logger.error("No faces found")
 
     # Try halving det_size if no faces are found
     if len(faces) == 0 and det_size[0] > 320 and det_size[1] > 320:
@@ -212,10 +221,9 @@ def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640
 
     try:
         faces_sorted = sort_by_order(face, order)
-        return faces_sorted[face_index], 0
+        return faces_sorted[face_index], 0, face_index
-        # return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
     except IndexError:
-        return None, 0
+        return None, 0, None
 
 
 def swap_face(
@@ -236,6 +244,8 @@ def swap_face(
 ):
     global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH
     result_image = target_img
+    bbox = []
+    swapped_indexes = []
 
     if model is not None:
 
@@ -312,82 +322,76 @@ def swap_face(
                 logger.status("Using Hashed Target Face(s) Model...")
                 target_faces = TARGET_FACES
 
-            # No use in trying to swap faces if no faces are found, enhancement
             if len(target_faces) == 0:
                 logger.status("Cannot detect any Target, skipping swapping...")
-                return result_image
+                return result_image, bbox, swapped_indexes
 
+            # --- НОВАЯ ИДЕАЛЬНАЯ ЛОГИКА СОРТИРОВКИ ---
+            
+            # 1. Заранее собираем список ТОЛЬКО ВАЛИДНЫХ исходных лиц
+            valid_source_faces = []
             if source_img is not None:
-                # separated management of wrong_gender between source and target, enhancement
+                for idx in source_faces_index:
-                source_face, src_wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
+                    sf, src_wrong_gender, _ = get_face_single(source_img, source_faces, face_index=idx, gender_source=gender_source, order=faces_order[1])
+                    if sf is not None and src_wrong_gender == 0:
+                        valid_source_faces.append(sf)
             else:
-                # source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
+                sf, src_wrong_gender, _ = get_face_single(None, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
-                source_face = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse = True)[source_faces_index[0]]
+                if sf is not None and src_wrong_gender == 0:
-                src_wrong_gender = 0
+                    valid_source_faces.append(sf)
 
-            if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
+            if len(valid_source_faces) == 0:
-                logger.status(f'Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.')
+                logger.status("No valid source face(s) found in the provided Index after gender filter")
-            elif source_face is not None:
+            else:
                 result = target_img
                 if "inswapper" in model:
                     model_path = os.path.join(insightface_path, model)
                 elif "reswapper" in model:
                     model_path = os.path.join(reswapper_path, model)
+                elif "hyperswap" in model:
+                    model_path = os.path.join(hyperswap_path, model)
+                
                 face_swapper = getFaceSwapModel(model_path)
 
                 source_face_idx = 0
 
+                # 2. Идем по целевым лицам
                 for face_num in faces_index:
-                    # No use in trying to swap faces if no further faces are found, enhancement
+                    target_face, wrong_gender, target_face_index = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target, order=faces_order[0])
-                    if face_num >= len(target_faces):
-                        logger.status("Checked all existing target faces, skipping swapping...")
-                        break
                     
-                    if len(source_faces_index) > 1 and source_face_idx > 0:
+                    if target_face is not None and wrong_gender == 0:
-                        source_face, src_wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source, order=faces_order[1])
+                        logger.status(f"Swapping...")
-                    source_face_idx += 1
                         
-                    if source_face is not None and src_wrong_gender == 0:
+                        # 3. Берем валидное лицо (если их меньше, чем целей — идем по кругу)
-                        target_face, wrong_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target, order=faces_order[0])
+                        source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
-                        if target_face is not None and wrong_gender == 0:
+                        
-                            logger.status(f"Swapping...")
+                        if face_boost_enabled and "hyperswap" not in model:
-                            if face_boost_enabled:
+                            logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
-                                logger.status(f"Face Boost is enabled")
+                            bgr_fake, M = face_swapper.get(result, target_face, source_face_to_use, paste_back=False)
-                                bgr_fake, M = face_swapper.get(result, target_face, source_face, paste_back=False)
+                            bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
-                                bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
+                            M *= scale
-                                M *= scale
+                            result = swapper.in_swap(result, bgr_fake, M)
-                                result = swapper.in_swap(target_img, bgr_fake, M)
-                            else:
-                                # logger.status(f"Swapping as-is")
-                                result = face_swapper.get(result, target_face, source_face)
-                        elif wrong_gender == 1:
-                            wrong_gender = 0
-                            # Keep searching for other faces if wrong gender is detected, enhancement
-                            #if source_face_idx == len(source_faces_index):
-                            #    result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
-                            #    return result_image
-                            logger.status("Wrong target gender detected")
-                            continue
                         else:
-                            logger.status(f"No target face found for {face_num}")
+                            result = face_swapper.get(result, target_face, source_face_to_use)
-                    elif src_wrong_gender == 1:
+                            
-                        src_wrong_gender = 0
+                        bbox.append(tuple(map(float, target_face.bbox)))
-                        # Keep searching for other faces if wrong gender is detected, enhancement
+                        swapped_indexes.append(target_face_index)
-                        #if source_face_idx == len(source_faces_index):
+
-                        #    result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
+                        # Продвигаем индекс исходного лица ТОЛЬКО после УСПЕШНОГО применения
-                        #    return result_image
+                        if len(valid_source_faces) > 1:
-                        logger.status("Wrong source gender detected")
+                            source_face_idx += 1
+
+                    elif wrong_gender == 1:
+                        logger.status("Wrong target gender detected")
                         continue
                     else:
-                        logger.status(f"No source face found for face number {source_face_idx}.")
+                        logger.info(f"No target face found for {face_num}")
 
                 result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
 
-            else:
-                logger.status("No source face(s) in the provided Index")
         else:
             logger.status("No source face(s) found")
-    return result_image
+    return result_image, bbox, swapped_indexes
 
 def swap_face_many(
     source_img: Union[Image.Image, None],
@@ -407,28 +411,23 @@ def swap_face_many(
 ):
     global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, TARGET_FACES_LIST, TARGET_IMAGE_LIST_HASH
     result_images = target_imgs
+    bbox = []
+    swapped_indexes = []
 
     if model is not None:
-
+        if isinstance(source_img, str): 
-        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
+            if 'base64,' in source_img:
-                # 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_imgs = [cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR) for target_img in target_imgs]
 
         if source_img is not None:
-
             source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
-
             source_image_md5hash = get_image_md5hash(source_img)
 
             if SOURCE_IMAGE_HASH is None:
@@ -451,18 +450,22 @@ def swap_face_many(
                 source_faces = SOURCE_FACES
 
         elif face_model is not None:
-
             source_faces_index = [0]
             logger.status("Using Loaded Source Face Model...")
             source_face_model = [face_model]
             source_faces = source_face_model
-
         else:
             logger.error("Cannot detect any Source")
 
         if source_faces is not None:
-
             target_faces = []
+            pbar = progress_bar(len(target_imgs))
+
+            if len(TARGET_IMAGE_LIST_HASH) > 0:
+                logger.status(f"Using Hashed Target Face(s) Model...")
+            else:
+                logger.status(f"Analyzing Target Image...")
+            
             for i, target_img in enumerate(target_imgs):
                 if state.interrupted or model_management.processing_interrupted():
                     logger.status("Interrupted by User")
@@ -484,93 +487,95 @@ def swap_face_many(
                 logger.info("(Image %s) Target Image the Same? %s", i, target_image_same)
 
                 if len(TARGET_FACES_LIST) == 0:
-                    logger.status(f"Analyzing Target Image {i}...")
                     target_face = analyze_faces(target_img)
                     TARGET_FACES_LIST = [target_face]
                 elif len(TARGET_FACES_LIST) == i and not target_image_same:
-                    logger.status(f"Analyzing Target Image {i}...")
                     target_face = analyze_faces(target_img)
                     TARGET_FACES_LIST.append(target_face)
                 elif len(TARGET_FACES_LIST) != i and not target_image_same:
-                    logger.status(f"Analyzing Target Image {i}...")
                     target_face = analyze_faces(target_img)
                     TARGET_FACES_LIST[i] = target_face
                 elif target_image_same:
-                    logger.status("(Image %s) Using Hashed Target Face(s) Model...", i)
                     target_face = TARGET_FACES_LIST[i]
                 
-
-                # logger.status(f"Analyzing Target Image {i}...")
-                # target_face = analyze_faces(target_img)
                 if target_face is not None:
                     target_faces.append(target_face)
+                pbar.update(1)
+
+            progress_bar_reset(pbar)
             
-            # No use in trying to swap faces if no faces are found, enhancement
             if len(target_faces) == 0:
                 logger.status("Cannot detect any Target, skipping swapping...")
-                return result_images
+                return result_images, bbox, swapped_indexes
 
+            # --- НОВАЯ ИДЕАЛЬНАЯ ЛОГИКА СОРТИРОВКИ ---
+            
+            valid_source_faces = []
             if source_img is not None:
-                # separated management of wrong_gender between source and target, enhancement
+                for idx in source_faces_index:
-                source_face, src_wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
+                    sf, src_wrong_gender, _ = get_face_single(source_img, source_faces, face_index=idx, gender_source=gender_source, order=faces_order[1])
+                    if sf is not None and src_wrong_gender == 0:
+                        valid_source_faces.append(sf)
             else:
-                # source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
+                sf, src_wrong_gender, _ = get_face_single(None, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
-                source_face = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse = True)[source_faces_index[0]]
+                if sf is not None and src_wrong_gender == 0:
-                src_wrong_gender = 0
+                    valid_source_faces.append(sf)
 
-            if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
+            if len(valid_source_faces) == 0:
-                logger.status(f'Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.')
+                logger.status("No valid source face(s) found in the provided Index after gender filter")
-            elif source_face is not None:
+            else:
                 results = target_imgs
-                model_path = model_path = os.path.join(insightface_path, model)
+                if "inswapper" in model:
+                    model_path = os.path.join(insightface_path, model)
+                elif "reswapper" in model:
+                    model_path = os.path.join(reswapper_path, model)
+                elif "hyperswap" in model:
+                    model_path = os.path.join(hyperswap_path, model)
+
                 face_swapper = getFaceSwapModel(model_path)
 
                 source_face_idx = 0
+                pbar = progress_bar(len(target_imgs))
+                logger.status(f"Swapping...")
 
                 for face_num in faces_index:
-                    # No use in trying to swap faces if no further faces are found, enhancement
+                    target_used_in_any_image = False
-                    if face_num >= len(target_faces):
-                        logger.status("Checked all existing target faces, skipping swapping...")
-                        break
                     
-                    if len(source_faces_index) > 1 and source_face_idx > 0:
+                    for i, (target_img, target_face_list) in enumerate(zip(results, target_faces)):
-                        source_face, src_wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source, order=faces_order[1])
+                        target_face_single, wrong_gender, target_face_index = get_face_single(target_img, target_face_list, face_index=face_num, gender_target=gender_target, order=faces_order[0])
-                    source_face_idx += 1
                         
-                    if source_face is not None and src_wrong_gender == 0:
+                        if target_face_single is not None and wrong_gender == 0:
-                        # Reading results to make current face swap on a previous face result
+                            target_used_in_any_image = True
-                        for i, (target_img, target_face) in enumerate(zip(results, target_faces)):
+                            source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
-                            target_face_single, wrong_gender = get_face_single(target_img, target_face, face_index=face_num, gender_target=gender_target, order=faces_order[0])
+                            
-                            if target_face_single is not None and wrong_gender == 0:
+                            result = target_img
-                                result = target_img
+                            if face_boost_enabled and "hyperswap" not in model:
-                                logger.status(f"Swapping {i}...")
+                                bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face_to_use, paste_back=False)
-                                if face_boost_enabled:
+                                bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
-                                    logger.status(f"Face Boost is enabled")
+                                M *= scale
-                                    bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face, paste_back=False)
+                                result = swapper.in_swap(target_img, bgr_fake, M)
-                                    bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
-                                    M *= scale
-                                    result = swapper.in_swap(target_img, bgr_fake, M)
-                                else:
-                                    # logger.status(f"Swapping as-is")
-                                    result = face_swapper.get(target_img, target_face_single, source_face)
-                                results[i] = result
-                            elif wrong_gender == 1:
-                                wrong_gender = 0
-                                logger.status("Wrong target gender detected")
-                                continue
                             else:
-                                logger.status(f"No target face found for {face_num}")
+                                result = face_swapper.get(target_img, target_face_single, source_face_to_use)
-                    elif src_wrong_gender == 1:
-                        src_wrong_gender = 0
-                        logger.status("Wrong source gender detected")
-                        continue
-                    else:
-                        logger.status(f"No source face found for face number {source_face_idx}.")
                                 
+                            results[i] = result
+                            bbox.append(tuple(map(float, target_face_single.bbox)))
+                            swapped_indexes.append(target_face_index)
+                            pbar.update(1)
+                            
+                        elif wrong_gender == 1:
+                            logger.status("Wrong target gender detected")
+                            pbar.update(1)
+                            continue
+                        else:
+                            logger.info(f"{i}: No target face found for {face_num}")
+                            pbar.update(1)
+                    
+                    if target_used_in_any_image and len(valid_source_faces) > 1:
+                        source_face_idx += 1
+
+                progress_bar_reset(pbar)
                 result_images = [Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB)) for result in results]
 
-            else:
-                logger.status("No source face(s) in the provided Index")
         else:
             logger.status("No source face(s) found")
-    return result_images
+    return result_images, bbox, swapped_indexes

scripts/reactor_version.py

@@ -1,5 +1,5 @@
 app_title = "ReActor Node for ComfyUI"
-version_flag = "v0.5.2"
+version_flag = "v0.7.1-b3"
 
 COLORS = {
     "CYAN": "\033[0;36m",  # CYAN