UPD: Comfy Registry

VersionUP
0.7.1 Beta3
2026-09-21 12:09:43 +07:00 · 2026-09-21 12:08:12 +07:00 · 2026-09-21 12:07:06 +07:00 · 2026-09-20 13:48:13 +07:00 · 2026-09-20 13:47:33 +07:00 · 2026-09-20 13:45:32 +07:00
33 changed files with 2812 additions and 953 deletions
@@ -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.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} ## Add your own personal access token to your Github secrets and reference it here.
@@ -3,3 +3,4 @@ __pycache__/
 .vscode/
 example
 input
 *.dll
@@ -2,16 +2,8 @@
  <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"/>
    <br>
@@ -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>
@@ -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>
@@ -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)
@@ -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^!
@@ -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"
@@ -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>
@@ -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}")
@@ -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),)
@@ -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
@@ -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)
@@ -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
@@ -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.")
@@ -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)
@@ -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
@@ -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)
@@ -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)
@@ -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
@@ -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
@@ -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
@@ -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")
@@ -2,7 +2,7 @@ import logging
 import copy
 import sys
-from modules import shared
+from r_modules import shared
 from reactor_utils import addLoggingLevel
@@ -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
@@ -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...")
+                    if target_face is not None and wrong_gender == 0:
-                        break
+                        logger.status(f"Swapping...")
-
+                        
-                    if len(source_faces_index) > 1 and source_face_idx > 0:
+                        # 3. Берем валидное лицо (если их меньше, чем целей — идем по кругу)
-                        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])
+                        source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
-                    source_face_idx += 1
+                        
-
+                        if face_boost_enabled and "hyperswap" not in model:
-                    if source_face is not None and src_wrong_gender == 0:
+                            logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
-                        target_face, wrong_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target, order=faces_order[0])
+                            bgr_fake, M = face_swapper.get(result, target_face, source_face_to_use, paste_back=False)
-                        if target_face is not None and wrong_gender == 0:
+                            bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
-                            logger.status(f"Swapping...")
+                            M *= scale
-                            if face_boost_enabled:
+                            result = swapper.in_swap(result, bgr_fake, M)
                                logger.status(f"Face Boost is enabled")
                                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)
                                M *= scale
                                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)
-            # No use in trying to swap faces if no faces are found, enhancement
+            progress_bar_reset(pbar)
            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...")
+                    for i, (target_img, target_face_list) in enumerate(zip(results, target_faces)):
-                        break
+                        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])
-
+                        
-                    if len(source_faces_index) > 1 and source_face_idx > 0:
+                        if target_face_single 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])
+                            target_used_in_any_image = True
-                    source_face_idx += 1
+                            source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
-
+                            
-                    if source_face is not None and src_wrong_gender == 0:
+                            result = target_img
-                        # Reading results to make current face swap on a previous face result
+                            if face_boost_enabled and "hyperswap" not in model:
-                        for i, (target_img, target_face) in enumerate(zip(results, target_faces)):
+                                bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face_to_use, paste_back=False)
-                            target_face_single, wrong_gender = get_face_single(target_img, target_face, face_index=face_num, gender_target=gender_target, order=faces_order[0])
+                                bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
-                            if target_face_single is not None and wrong_gender == 0:
+                                M *= scale
-                                result = target_img
+                                result = swapper.in_swap(target_img, bgr_fake, M)
                                logger.status(f"Swapping {i}...")
                                if face_boost_enabled:
                                    logger.status(f"Face Boost is enabled")
                                    bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face, paste_back=False)
                                    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
+                            results[i] = result
-                        logger.status("Wrong source gender detected")
+                            bbox.append(tuple(map(float, target_face_single.bbox)))
-                        continue
+                            swapped_indexes.append(target_face_index)
-                    else:
+                            pbar.update(1)
-                        logger.status(f"No source face found for face number {source_face_idx}.")
+                            
                        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
@@ -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
Author	SHA1	Message	Date
Gourieff \| 古仁	a12c5b19dc	UPD: Comfy Registry	2026-09-21 12:09:43 +07:00
Gourieff \| 古仁	ea86b52e0f	VersionUP 0.7.1 Beta3	2026-09-21 12:08:12 +07:00
Gourieff \| 古仁	02a3518aa7	UPD: DLSS5 third-party note	2026-09-21 12:07:06 +07:00
Gourieff \| 古仁	3746860aa4	UPD: Comfy Registry	2026-09-20 13:48:13 +07:00
Gourieff \| 古仁	f92ebeb653	VersionUP 0.7.1 Beta2	2026-09-20 13:47:33 +07:00
Gourieff \| 古仁	f6776fded7	UPD: DLSS5 auto_mask tooltip	2026-09-20 13:45:32 +07:00
Gourieff \| 古仁	7b1233edc0	UPD: Comfy Registry	2026-09-20 13:11:43 +07:00
Gourieff \| 古仁	b3a0ac8cf5	UPD: What's new sec -> DLSS5 requirements	2026-09-19 02:07:46 +07:00
Gourieff \| 古仁	de7e09db0f	UPD: Comfy Registry	2026-09-18 19:59:34 +07:00
Gourieff \| 古仁	60b310dd7f	UPD: Usage sec	2026-09-18 17:49:46 +07:00
Gourieff \| 古仁	4351dc4272	FIX: trailing comma	2026-09-18 17:45:16 +07:00
Gourieff \| 古仁	5f68562b6d	UPD: What's new sec	2026-09-18 15:14:20 +07:00
Gourieff \| 古仁	b9f8683da5	UPD: DLSS5FrameEnhancer Description	2026-09-18 14:25:23 +07:00
Gourieff \| 古仁	78c8844f26	VersionUP 0.7.1 Beta1	2026-09-17 13:30:42 +07:00
Gourieff \| 古仁	b01880c37f	Merge branch 'dlss5-support' into evolve	2026-09-17 13:06:35 +07:00
Gourieff \| 古仁	50fdb4993f	UPD: Comfy Registry	2026-09-17 13:01:47 +07:00
Gourieff \| 古仁	8fe1c0ec6d	VersionUP 0.7.0 (Beta passed)	2026-09-17 13:00:49 +07:00
Gourieff \| 古仁	619eb16a1e	UPD: dlssnr dll README	2026-09-17 12:46:49 +07:00
Gourieff \| 古仁	4dab1f1b0a	ADD: DLSS5 Enhancer Node	2026-09-17 03:04:22 +07:00
Gourieff \| 古仁	a51e88ca24	UPD: Comfy Registry	2026-09-04 19:39:15 +07:00
Gourieff \| 古仁	99e23652a2	VersionUP 0.7.0 Beta1	2026-08-30 15:13:57 +07:00
Gourieff \| 古仁	0aa14fabb4	UPD: Desktop friendly install.bat https://codeberg.org/Gourieff/comfyui-reactor-node/issues/52	2026-08-30 15:11:22 +07:00
Gourieff \| 古仁	ecd2b3c16d	FIX: Typo Issue #245	2026-08-30 15:03:36 +07:00
Gourieff \| 古仁	6ad6b35a4d	UPD: Comfy Registry	2026-05-13 00:24:34 +07:00
Gourieff \| 古仁	2e34bf8356	VersionUP 0.7.0 Alpha2	2026-05-13 00:20:10 +07:00
Gourieff \| 古仁	be42ca37ee	FIX: Gender detection face index logic Issue #234	2026-05-13 00:07:42 +07:00
Gourieff \| 古仁	a7628ccdff	UPD: Comfy Registry	2026-04-25 15:44:33 +07:00
Gourieff \| 古仁	59040c1557	VersionUP 0.7.0 Alpha1	2026-04-25 15:34:34 +07:00
Gourieff \| 古仁	e0cd862c3d	Merge branch 'cleanup_and_refactor' into evolve	2026-04-25 13:37:08 +07:00
Gourieff \| 古仁	fc1691dda0	UPD: Comfy Registry	2026-04-25 13:32:43 +07:00
Gourieff \| 古仁	33d0feff2b	VersionUP 0.6.2 (Beta passed)	2026-04-25 13:32:10 +07:00
Gourieff \| 古仁	5881d3ee2f	FIX: Utils Face object link	2026-04-23 23:38:58 +07:00
Gourieff \| 古仁	febdf3027a	DEL: Monkey-patch Insightface patcher is no need any more	2026-04-23 23:15:05 +07:00
Gourieff \| 古仁	74146c26e0	ADD: New node "Face Similarity"	2026-04-23 17:34:36 +07:00
Gourieff \| 古仁	55787ee169	ADD: Hyperswap Class	2026-04-23 16:07:20 +07:00
Gourieff \| 古仁	ca24587deb	UPD: landmark_3d_68 Math	2026-04-23 13:01:17 +07:00
Gourieff \| 古仁	11db21f666	FIX: SFW Score correction Still safe	2026-04-23 12:54:24 +07:00
Gourieff \| 古仁	1356f18bf7	UPD: New ReActor Core - No Insightface required - Numpy 1.x 2.x friendly	2026-04-23 01:11:47 +07:00
Gourieff \| 古仁	b60036fc85	FIX: HyperSwap CPU Float norm Issue #183 Contributor: @Buumcode	2026-04-22 15:55:38 +07:00
Gourieff \| 古仁	a2a61fd5e7	UPD: Comfy Registry	2025-12-08 15:23:23 +07:00
Gourieff \| 古仁	d93450bd80	UPD: What's new sec	2025-10-07 23:23:45 +07:00
Gourieff \| 古仁	67ce1186f8	FIX: Typo	2025-10-07 22:34:56 +07:00
Gourieff \| 古仁	e008d3c5ae	UPD: Profile url	2025-10-07 22:32:36 +07:00
Gourieff \| 古仁	b26892f342	UPD: What's new sec	2025-10-07 22:28:31 +07:00
Gourieff \| 古仁	b51d34e05a	UPD: NSFW filter Score correction	2025-10-07 17:19:14 +07:00
Gourieff \| 古仁	4ea6a541ec	UPD: What's new sec	2025-10-07 02:42:56 +07:00
Gourieff \| 古仁	18fc8a303f	Merge branch 'hyperswap' into evolve	2025-10-07 02:40:03 +07:00
Art Gourieff	43bafc227e	Merge pull request #177 from pervkajan-rgb/main Fix multiple face index	2025-10-07 02:37:57 +07:00
Gourieff \| 古仁	85763bc0b7	FIX: get_face_gender ValueError	2025-10-07 02:21:27 +07:00
Gourieff \| 古仁	b8d49816be	UPD: What's New, Models links + VersionUP 0.6.2 Beta1	2025-10-06 23:45:35 +07:00
Gourieff \| 古仁	95ed7b248f	UPD: HyperSwap models support FR #143 Thanks @Buumcode for contribution	2025-10-06 22:15:27 +07:00
Gourieff \| 古仁	d8e3b90c82	VersionUP + UPD: Comfy Registry 0.6.2 Alpha4	2025-10-03 01:43:46 +07:00
Gourieff \| 古仁	13d836015b	FIX: MaskHelper - move result back to CPU To avoid "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu" might be thrown by the next node.	2025-10-03 01:41:22 +07:00
pervkajan-rgb	fd5f9e1de2	Fix multiple face index	2025-09-30 16:18:34 +03:00
Gourieff \| 古仁	a2ad854a09	VersionUP + UPD: Comfy Registry 0.6.2 Alpha3	2025-09-23 01:09:25 +07:00
Gourieff \| 古仁	a708f71a2f	HOTFIX: ReActorPlusOpt init missed var	2025-09-23 01:04:52 +07:00
Gourieff \| 古仁	6ee22a8ecb	VersionUP + UPD: Comfy Registry 0.6.2 Alpha2	2025-09-22 19:35:28 +07:00
Gourieff \| 古仁	9d5dba9801	HOTFIX: get_face_gender ValueError	2025-09-22 19:31:32 +07:00
Gourieff \| 古仁	dd44fffd9e	UPD: Comfy Registry	2025-09-21 16:55:13 +07:00
Gourieff \| 古仁	4eb66b3a8f	UPD: Comfy Registry	2025-09-21 15:23:01 +07:00
Gourieff \| 古仁	e7dd3bbbc9	HOTFIX: index error	2025-09-21 14:47:10 +07:00
Gourieff \| 古仁	e9d34b3a91	UPD: Comfy Registry	2025-09-21 13:26:26 +07:00
Gourieff \| 古仁	c98dc50515	UPD: Insightface instructions FR #163 Issue #172	2025-09-20 15:42:34 +07:00
Gourieff \| 古仁	14595e4350	UPD: Face Model Name string FACE_MODEL_NAME output for "Load Face Model" node FR #134	2025-09-20 14:36:44 +07:00
Gourieff \| 古仁	1ee4e21d21	UPD: web.archive URLs to old ReActor repo	2025-09-15 15:52:04 +07:00
Gourieff \| 古仁	0a5f8ebffd	UPD: Restore Face Advanced Node Thanks @Buumcode for implementation of "Restore Face Filter"	2025-09-14 23:54:06 +07:00
Gourieff \| 古仁	d218c52501	VersionUP 0.6.2 Alpha1	2025-09-14 04:20:03 +07:00
Gourieff \| 古仁	ec743a7de4	UPD: Face Restore Filter -Stage2-	2025-09-14 03:09:16 +07:00
Gourieff \| 古仁	8e41e6c900	UPD: Face Restore Filter FR #159	2025-09-05 15:24:21 +07:00
Gourieff \| 古仁	5c7884118b	UPD: Comfy Registry	2025-09-05 12:51:32 +07:00
Gourieff \| 古仁	0e8b1edd09	VersionUP 0.6.1 (Beta passed)	2025-09-05 12:49:39 +07:00
Gourieff \| 古仁	d60458f212	FIX: original_image output	2025-08-04 01:47:36 +07:00
Gourieff \| 古仁	9b17e4cea5	UPD: Comfy Registry	2025-07-09 20:50:55 +07:00
Gourieff \| 古仁	1465ec6a66	FIX: Typo	2025-07-09 20:41:21 +07:00
Gourieff \| 古仁	560211e3ab	FIX: Docs	2025-07-09 20:37:25 +07:00
Gourieff \| 古仁	aa318b5073	UPD: Docs	2025-07-09 20:27:25 +07:00
Gourieff \| 古仁	6244ee6cdc	VersionUP 0.6.1 Beta3	2025-07-01 23:41:50 +07:00
Gourieff \| 古仁	5230d0f09b	UPD: Gender det better logic for many faces	2025-07-01 23:38:15 +07:00
Gourieff \| 古仁	392bbfd0af	UPD: Inswapper dl URLs	2025-07-01 15:09:20 +07:00
Gourieff \| 古仁	48a3ad27f9	UPD: Comfy Registry	2025-05-26 23:30:21 +07:00
Gourieff \| 古仁	da1d284141	VersionUP 0.6.1 Beta2	2025-05-26 23:26:59 +07:00
Gourieff \| 古仁	be2e8d549a	UPD: Less annoying msgs	2025-05-26 23:22:51 +07:00
Gourieff \| 古仁	701259b66e	FIX: Numpy array error if Tensor is on GPU Issue #127	2025-05-26 22:58:49 +07:00
Gourieff \| 古仁	bc439d97a1	HOTFIX: " -> ' Issue #125	2025-05-22 17:09:17 +07:00
Gourieff \| 古仁	3626434689	UPD: Comfy Registry	2025-05-21 14:36:55 +07:00
Gourieff \| 古仁	2041f67349	UPD: What's New section	2025-05-21 14:33:36 +07:00
Gourieff \| 古仁	483d56cc80	VersionUP 0.6.1 Beta1	2025-05-21 14:16:48 +07:00
Gourieff \| 古仁	3d65ba0765	Merge branch 'main' into evolve	2025-05-21 14:14:05 +07:00
Gourieff \| 古仁	6029ffd2cc	VersionUP 0.6.0 (Alpha/Beta passed)	2025-05-21 14:08:53 +07:00
Gourieff \| 古仁	f6b4a0ebce	UPDs and FIXs - #25 Fix - ComfyUI native ProgressBar for different steps - ORIGINAL_IMAGE output for main nodes - no tmp file for nsfw detector - nsfw detector little speed up	2025-05-21 12:47:35 +07:00
Gourieff \| 古仁	0addca8a40	UPD: MaskHelper SpeedUp - Try1	2025-05-20 14:38:18 +07:00
Art Gourieff	6944f5d2c0	Merge pull request #95 from ComfyNodePRs/update-publish-yaml Update Github Action for Publishing to Comfy Registry	2025-05-14 12:06:42 +07:00
Gourieff \| 古仁	d901609a1d	UPD: Comfy Registry	2025-03-10 00:02:29 +07:00
Gourieff \| 古仁	54c7c27714	VersionUP 0.6.0 Alpha1	2025-03-02 22:48:47 +07:00
Gourieff \| 古仁	9d835780d7	FIX: Merge	2025-03-02 01:19:41 +07:00
Gourieff \| 古仁	289001af81	Merge branch 'main' into equalize	2025-03-02 01:19:07 +07:00
Gourieff \| 古仁	afc62294ac	UPD: Face Swap Weight w/facemodel	2025-03-01 22:11:05 +07:00
Gourieff \| 古仁	b7e30eea5e	FIX: Black Image Issue Thanks @mschuettlerTNG for pointing out https://github.com/Gourieff/ComfyUI-ReActor/issues/62#issuecomment-2689122264 Issues #15 #28 #57 #62	2025-03-01 18:51:48 +07:00
Gourieff \| 古仁	3095e627cd	FIX: MD	2025-02-28 00:27:30 +07:00
Gourieff \| 古仁	e573562a11	UPD: Face Swap Weight	2025-02-27 21:39:41 +07:00
Gourieff \| 古仁	42cfb3317f	FIX: Threshold adjustment Due to black screen issues with some SFW images; 0.972 - is still safety content	2025-02-25 14:09:26 +07:00
Gourieff \| 古仁	5944d9a3e1	FIX: PR30 'model_path' variable name error	2025-02-21 18:30:53 +07:00
Евгений Гурьев \| Eugene Gourieff \| 古仁	fc69c2232d	Merge pull request #30 from fofr/main UPD: Only download the nsfw-detector when first used	2025-02-13 19:42:52 +07:00
Paul	4a7587a3fe	Only download the nsfw detector when used	2025-01-27 15:46:52 +00:00
snomiao	7b8f84b9c0	chore(publish): update GitHub Actions workflow for node publishing - Add permissions for writing issues - Update action version to v1 - Add condition to run job only for specific repository owner	2025-01-25 15:56:47 +00:00