update on requirements

some update on what is needed to be updated

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,26 @@
+***[Remove this]The issue would be closed without notice and be considered spam if the template is not followed.***
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Error Message**
+
+`<The error message in terminal>`
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. Windows]
+ - Version [e.g. 22]
+ - GPU
+ - CPU
+
+**Additional context**
+Add any other context about the problem here.
+
+**Confirmation (Mandatory)**
+- [ ] I have followed the template
+- [ ] This is not a query about how to increase performance
+- [ ] I have checked the issues page, and this is not a duplicate
+

.github/workflows/ruff.yml

@@ -0,0 +1,16 @@
+name: ruff
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v4.0.0
+        with:
+          version: "0.15.7"
+          args: "check --output-format=github"

.gitignore

@@ -6,17 +6,26 @@ __pycache__/
 .todo
 *.log
 *.backup
-
+tf_env/
 *.png
 *.mp4
 *.mkv
 
 .tmp/
 temp/
+.venv/
 venv/
+env/
 workflow/
 gfpgan/
 models/inswapper_128.onnx
 models/GFPGANv1.4.pth
 *.onnx
 models/DMDNet.pth
+faceswap/
+.vscode/
+switch_states.json
+/models
+install.bat
+/.claude
+*.bat

.python-version

@@ -1 +0,0 @@
-3.10.14

CONTRIBUTING.md

@@ -0,0 +1,38 @@
+# Collaboration Guidelines and Codebase Quality Standards
+
+To ensure smooth collaboration and maintain the high quality of our codebase, please adhere to the following guidelines:
+
+## Branching Strategy
+
+*   **`premain`**:
+    *   Always push your changes to the `premain` branch initially.
+    *   This safeguards the `main` branch from unintentional disruptions.
+    *   All tests will be performed on the `premain` branch.
+    *   Changes will only be merged into `main` after several hours or days of rigorous testing.
+*   **`experimental`**:
+    *   For large or potentially disruptive changes, use the `experimental` branch.
+    *   This allows for thorough discussion and review before considering a merge into `main`.
+
+## Pre-Pull Request Checklist
+
+Before creating a Pull Request (PR), ensure you have completed the following tests:
+
+### Functionality
+
+*   **Realtime Faceswap**:
+    *   Test with face enhancer **enabled** and **disabled**.
+*   **Map Faces**:
+    *   Test with both options (**enabled** and **disabled**).
+*   **Camera Listing**:
+    *   Verify that all cameras are listed accurately.
+
+### Stability
+
+*   **Realtime FPS**:
+    *   Confirm that there is no drop in real-time frames per second (FPS).
+*   **Boot Time**:
+    *   Changes should not negatively impact the boot time of either the application or the real-time faceswap feature.
+*   **GPU Overloading**:
+    *   Test for a minimum of 15 minutes to guarantee no GPU overloading, which could lead to crashes.
+*   **App Performance**:
+    *   The application should remain responsive and not exhibit any lag.

README.md

@@ -1,163 +1,361 @@
-![demo-gif](demo.gif)
+<h1 align="center">Deep-Live-Cam 2.1.6</h1>
 
+<p align="center">
+  Real-time face swap and video deepfake with a single click and only a single image.
+</p>
 
-## Disclaimer
-This software is meant to be a productive contribution to the rapidly growing AI-generated media industry. It will help artists with tasks such as animating a custom character or using the character as a model for clothing etc.
+<p align="center">
+<a href="https://trendshift.io/repositories/11395" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11395" alt="hacksider%2FDeep-Live-Cam | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</p>
 
-The developers of this software are aware of its possible unethical applications and are committed to take preventative measures against them. It has a built-in check which prevents the program from working on inappropriate media including but not limited to nudity, graphic content, sensitive material such as war footage etc. We will continue to develop this project in the positive direction while adhering to law and ethics. This project may be shut down or include watermarks on the output if requested by law.
+<p align="center">
+  <img src="media/demo.gif" alt="Demo GIF" width="800">
+</p>
 
-Users of this software are expected to use this software responsibly while abiding the local law. If face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers of this software will not be responsible for actions of end-users.
+##  Disclaimer
 
-## How do I install it?
+This deepfake software is designed to be a productive tool for the AI-generated media industry. It can assist artists in animating custom characters, creating engaging content, and even using models for clothing design.
 
+We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.
 
-### Basic: It is more likely to work on your computer but it will also be very slow. You can follow instructions for the basic install (This usually runs via **CPU**)
-#### 1.Setup your platform
--   python (3.10 recommended)
+- Ethical Use: Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online.
+
+- Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.
+
+- Legal Compliance: We adhere to all relevant laws and ethical guidelines. If legally required, we may shut down the project or add watermarks to the output.
+
+- User Responsibility: We are not responsible for end-user actions. Users must ensure their use of the software aligns with ethical standards and legal requirements.
+
+By using this software, you agree to these terms and commit to using it in a manner that respects the rights and dignity of others.
+
+Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
+
+## Pre-built Quickstart
+
+<p align="center">
+  <a href="https://deeplivecam.net/index.php/quickstart">
+    <img src="https://github.com/user-attachments/assets/fa2cdf79-c933-4b93-844a-b087192261ed" width="100%" alt="Lite / Ultimate Download Banner">
+  </a>
+</p>
+
+<p align="center">
+  <img src="https://github.com/user-attachments/assets/56b61811-3a1e-4672-9b50-cf7f6e8e6852" width="40" alt="Windows">
+  &nbsp;&nbsp;&nbsp;
+  <img src="https://github.com/user-attachments/assets/6538e3a6-c957-431a-b586-2d6abcf534dc" width="34" alt="Mac Silicon">
+  &nbsp;&nbsp;&nbsp;
+  <img src="https://github.com/user-attachments/assets/ad45142e-426c-4364-a2a9-a512670cc62c" width="40" alt="CPU">
+</p>
+
+<p align="center">
+  <strong>Windows • Mac Silicon • CPU • NVIDIA • AMD</strong>
+</p>
+
+<p align="center">
+  Builds optimized for your hardware.
+</p>
+
+<p align="center">
+  <a href="https://deeplivecam.net/index.php/quickstart">
+    <img src="media/Download.png" width="280" alt="Download">
+  </a>
+</p>
+
+> **Ultimate** includes **30+ exclusive features**, performance optimizations, and **priority support**.
+
+Perfect if you want the fastest setup with **zero manual installation**, pre-configured dependencies, and optimized builds for every supported platform.
+
+## TLDR; Live Deepfake in just 3 Clicks
+![easysteps](https://github.com/user-attachments/assets/af825228-852c-411b-b787-ffd9aac72fc6)
+1. Select a face
+2. Select which camera to use
+3. Press live!
+
+## Features & Uses - Everything is in real-time
+
+### Mouth Mask
+
+**Retain your original mouth for accurate movement using Mouth Mask**
+
+<p align="center">
+  <img src="media/ludwig.gif" alt="resizable-gif">
+</p>
+
+### Face Mapping
+
+**Use different faces on multiple subjects simultaneously**
+
+<p align="center">
+  <img src="media/streamers.gif" alt="face_mapping_source">
+</p>
+
+### Your Movie, Your Face
+
+**Watch movies with any face in real-time**
+
+<p align="center">
+  <img src="media/movie.gif" alt="movie">
+</p>
+
+### Live Show
+
+**Run Live shows and performances**
+
+<p align="center">
+  <img src="media/live_show.gif" alt="show">
+</p>
+
+### Memes
+
+**Create Your Most Viral Meme Yet**
+
+<p align="center">
+  <img src="media/meme.gif" alt="show" width="450"> 
+  <br>
+  <sub>Created using Many Faces feature in Deep-Live-Cam</sub>
+</p>
+
+### Omegle
+
+**Surprise people on Omegle**
+
+<p align="center">
+  <video src="https://github.com/user-attachments/assets/2e9b9b82-fa04-4b70-9f56-b1f68e7672d0" width="450" controls></video>
+</p>
+
+## Installation (Manual)
+
+**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.**
+
+<details>
+<summary>Click to see the process</summary>
+
+### Installation
+
+This is more likely to work on your computer but will be slower as it utilizes the CPU.
+
+**1. Set up Your Platform**
+
+-   Python (3.14 recommended; 3.11-3.14 supported)
 -   pip
 -   git
--   [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) 
--   [visual studio 2022 runtimes (windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
-#### 2. Clone Repository
-    https://github.com/hacksider/Deep-Live-Cam.git
+-   [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
+-   [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
 
-#### 3. Download Models
+**2. Clone the Repository**
 
- 1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
- 2. [inswapper_128_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128.onnx)
-
-Then put those 2 files on the "**models**" folder
-
-#### 4. Install dependency
-We highly recommend to work with a  `venv`  to avoid issues.
+```bash
+git clone --depth 1 https://github.com/hacksider/Deep-Live-Cam.git
+cd Deep-Live-Cam
 ```
+
+**3. Download the Models**
+
+1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx)
+2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
+
+Place these files in the "**models**" folder.
+
+**4. Install Dependencies**
+
+We highly recommend using a `venv` to avoid issues.
+
+
+For Windows:
+```bash
+python -m venv venv
+venv\Scripts\activate
 pip install -r requirements.txt
 ```
-##### DONE!!! If you dont have any GPU, You should be able to run roop using `python run.py` command. Keep in mind that while running the program for first time, it will download some models which can take time depending on your network connection.
-
-### *Proceed if you want to use GPU Acceleration
-### CUDA Execution Provider (Nvidia)*
-
-1.  Install  [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive)
-    
-2.  Install dependencies:
-    
-
+For Linux:
+```bash
+# Ensure you use the installed Python 3.14
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
 ```
+
+**For macOS:**
+
+Apple Silicon (M1 through M5) requires specific setup:
+
+```bash
+# Install Python 3.14
+brew install python@3.14
+
+# Install tkinter package (required for the GUI)
+brew install python-tk@3.14
+
+# Create and activate virtual environment with Python 3.14
+python3.14 -m venv venv
+source venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+** In case something goes wrong and you need to reinstall the virtual environment **
+
+```bash
+# Deactivate the virtual environment
+rm -rf venv
+
+# Reinstall the virtual environment
+python -m venv venv
+source venv/bin/activate
+
+# install the dependencies again
+pip install -r requirements.txt
+
+# gfpgan and basicsrs issue fix
+pip install git+https://github.com/xinntao/BasicSR.git@master
+pip uninstall gfpgan -y
+pip install git+https://github.com/TencentARC/GFPGAN.git@master
+```
+
+**Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).
+
+### GPU Acceleration
+
+**CUDA Execution Provider (Nvidia)**
+
+1. Install [CUDA Toolkit 12.8.0](https://developer.nvidia.com/cuda-12-8-0-download-archive)
+2. Install [cuDNN v8.9.7 for CUDA 12.x](https://developer.nvidia.com/rdp/cudnn-archive) (required for onnxruntime-gpu):
+   - Download cuDNN v8.9.7 for CUDA 12.x
+   - Make sure the cuDNN bin directory is in your system PATH
+3. Install dependencies:
+
+```bash
+pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
 pip uninstall onnxruntime onnxruntime-gpu
-pip install onnxruntime-gpu==1.16.3
-
+pip install onnxruntime-gpu==1.21.0
 ```
 
-3.  Usage in case the provider is available:
+3. Usage:
 
-```
+```bash
 python run.py --execution-provider cuda
-
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#coreml-execution-provider-apple-silicon)CoreML Execution Provider (Apple Silicon)
+**CoreML Execution Provider (Apple Silicon)**
 
-1.  Install dependencies:
+Apple Silicon (M1 through M5) specific installation:
 
-```
-pip uninstall onnxruntime onnxruntime-silicon
-pip install onnxruntime-silicon==1.13.1
+1. Make sure you've completed the macOS setup above using Python 3.14.
+2. No extra install step is needed — `requirements.txt` pulls the official
+   `onnxruntime` build, whose macOS wheels ship the CoreML execution provider.
+   If you previously installed the unmaintained `onnxruntime-silicon` fork,
+   remove it first, as it shadows the real package:
 
+```bash
+pip uninstall onnxruntime-silicon
+pip install -r requirements.txt
 ```
 
-2.  Usage in case the provider is available:
-
-```
-python run.py --execution-provider coreml
+3. Usage:
 
+```bash
+python3.14 run.py --execution-provider coreml
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#coreml-execution-provider-apple-legacy)CoreML Execution Provider (Apple Legacy)
+**Important Notes for macOS:**
+- Python 3.11 is the minimum (onnxruntime dropped 3.10); 3.14 is recommended
+- Always run with `python3.14` command not just `python` if you have multiple Python versions installed
+- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.14`
+- If you get model loading errors, check that your models are in the correct folder
+- If you encounter conflicts with other Python versions, consider uninstalling them:
+  ```bash
+  # List all installed Python versions
+  brew list | grep python
 
-1.  Install dependencies:
+  # Uninstall conflicting versions if needed
+  brew uninstall --ignore-dependencies python@3.11
 
-```
+  # Keep only Python 3.14
+  brew cleanup
+  ```
+
+**CoreML Execution Provider (Apple Legacy)**
+
+1. Install dependencies:
+
+```bash
 pip uninstall onnxruntime onnxruntime-coreml
-pip install onnxruntime-coreml==1.13.1
-
+pip install onnxruntime-coreml==1.21.0
 ```
 
-2.  Usage in case the provider is available:
+2. Usage:
 
-```
+```bash
 python run.py --execution-provider coreml
-
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#directml-execution-provider-windows)DirectML Execution Provider (Windows)
+**DirectML Execution Provider (Windows)**
 
-1.  Install dependencies:
+1. Install dependencies:
 
-```
+```bash
 pip uninstall onnxruntime onnxruntime-directml
-pip install onnxruntime-directml==1.15.1
-
+pip install onnxruntime-directml==1.21.0
 ```
 
-2.  Usage in case the provider is available:
+2. Usage:
 
-```
+```bash
 python run.py --execution-provider directml
-
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#openvino-execution-provider-intel)OpenVINO™ Execution Provider (Intel)
+**OpenVINO™ Execution Provider (Intel)**
 
-1.  Install dependencies:
+1. Install dependencies:
 
-```
+```bash
 pip uninstall onnxruntime onnxruntime-openvino
-pip install onnxruntime-openvino==1.15.0
-
+pip install onnxruntime-openvino==1.21.0
 ```
 
-2.  Usage in case the provider is available:
+2. Usage:
 
-```
+```bash
 python run.py --execution-provider openvino
 ```
+</details>
 
-## How do I use it?
-> Note: When you run this program for the first time, it will download some models ~300MB in size.
+## Usage
 
-Executing `python run.py` command will launch this window:
-![gui-demo](instruction.png)
+**1. Image/Video Mode**
 
-Choose a face (image with desired face) and the target image/video (image/video in which you want to replace the face) and click on `Start`. Open file explorer and navigate to the directory you select your output to be in. You will find a directory named `<video_title>` where you can see the frames being swapped in realtime. Once the processing is done, it will create the output file. That's it.
+-   Execute `python run.py`.
+-   Choose a source face image and a target image/video.
+-   Click "Start".
+-   The output will be saved in a directory named after the target video.
 
-## For the webcam mode
-Just follow the clicks on the screenshot
-1. Select a face
-2. Click live
-3. Wait for a few second (it takes a longer time, usually 10 to 30 seconds before the preview shows up)
+**2. Webcam Mode**
 
-![demo-gif](demo.gif)
+-   Execute `python run.py`.
+-   Select a source face image.
+-   Click "Live".
+-   Wait for the preview to appear (10-30 seconds).
+-   Use a screen capture tool like OBS to stream.
+-   To change the face, select a new source image.
 
-Just use your favorite screencapture to stream like OBS
-> Note: In case you want to change your face, just select another picture, the preview mode will then restart (so just wait a bit).
-
-You can now use the virtual camera output (uses pyvirtualcam) by turning on the `Virtual Cam Output (OBS)` toggle which should output to the OBS Virtual Camera. Note: this may not work on macOS. You will get a preview as before, but now you will also have a virtual camera output which can be used in applications like Zoom.
-
-Additional command line arguments are given below. To learn out what they do, check [this guide](https://github.com/s0md3v/roop/wiki/Advanced-Options).
+## Download all models in this huggingface link
+- [**Download models here**](https://huggingface.co/hacksider/deep-live-cam/tree/main)
 
+## Command Line Arguments (Unmaintained)
 
 ```
 options:
   -h, --help                                               show this help message and exit
-  -s SOURCE_PATH, --source SOURCE_PATH                     select an source image
-  -t TARGET_PATH, --target TARGET_PATH                     select an target image or video
+  -s SOURCE_PATH, --source SOURCE_PATH                     select a source image
+  -t TARGET_PATH, --target TARGET_PATH                     select a target image or video
   -o OUTPUT_PATH, --output OUTPUT_PATH                     select output file or directory
-  --frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...]  frame processors (choices: face_swapper, face_enhancer, super_resolution...)
+  --frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...]  frame processors (choices: face_swapper, face_enhancer, ...)
   --keep-fps                                               keep original fps
   --keep-audio                                             keep original audio
   --keep-frames                                            keep temporary frames
   --many-faces                                             process every face
+  --map-faces                                              map source target faces
+  --mouth-mask                                             mask the mouth region
   --video-encoder {libx264,libx265,libvpx-vp9}             adjust output video encoder
   --video-quality [0-51]                                   adjust output video quality
   --live-mirror                                            the live camera display as you see it in the front-facing camera frame
@@ -165,24 +363,54 @@ options:
   --max-memory MAX_MEMORY                                  maximum amount of RAM in GB
   --execution-provider {cpu} [{cpu} ...]                   available execution provider (choices: cpu, ...)
   --execution-threads EXECUTION_THREADS                    number of execution threads
-  --headless                                               run in headless mode
-  --enhancer-upscale-factor                                Sets the upscale factor for the enhancer. Only applies if `face_enhancer` is set as a frame-processor
-  --source-image-scaling-factor                            Set the upscale factor for source images. Only applies if `face_swapper` is set as a frame-processor
-  -r SCALE, --super-resolution-scale-factor SCALE          Super resolution scale factor, choices are 2, 3, 4
   -v, --version                                            show program's version number and exit
 ```
 
 Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.
 
-To improve the video quality, you can use the `super_resolution` frame processor after swapping the faces. It will enhance the video quality by 2x, 3x or 4x. You can set the upscale factor using the `-r` or `--super-resolution-scale-factor` argument. 
-Processing time will increase with the upscale factor, but it's quite quick.
+## Press
+
+ - [**Ars Technica**](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - *"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*
+ - [**Yahoo!**](https://www.yahoo.com/tech/ok-viral-ai-live-stream-080041056.html) - *"OK, this viral AI live stream software is truly terrifying"*
+ - [**CNN Brasil**](https://www.cnnbrasil.com.br/tecnologia/ia-consegue-clonar-rostos-na-webcam-entenda-funcionamento/) - *"AI can clone faces on webcam; understand how it works"*
+ - [**Bloomberg Technoz**](https://www.bloombergtechnoz.com/detail-news/71032/kenalan-dengan-teknologi-deep-live-cam-bisa-jadi-alat-menipu) - *"Get to know Deep Live Cam technology, it can be used as a tool for deception."*
+ - [**TrendMicro**](https://www.trendmicro.com/vinfo/gb/security/news/cyber-attacks/ai-vs-ai-deepfakes-and-ekyc) - *"AI vs AI: DeepFakes and eKYC"*
+ - [**PetaPixel**](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - *"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*
+ - [**SomeOrdinaryGamers**](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - *"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*
+ - [**IShowSpeed**](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - *"Alright look look look, now look chat, we can do any face we want to look like chat"*
+ - [**TechLinked (Linus Tech Tips)**](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - *"They do a pretty good job matching poses, expression and even the lighting"*
+ - [**IShowSpeed**](https://youtu.be/JbUPRmXRUtE?t=3964) - *"What the F***! Why do I look like Vinny Jr? I look exactly like Vinny Jr!? No, this shit is crazy! Bro This is F*** Crazy!"*
 
-```
 
 ## Credits
-- [henryruhs](https://github.com/henryruhs): for being an irreplaceable contributor to the project
-- [ffmpeg](https://ffmpeg.org/): for making video related operations easy
-- [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models.
-- [havok2-htwo](https://github.com/havok2-htwo) : for sharing the code for webcam
-- [GosuDRM](https://github.com/GosuDRM/nsfw-roop) : for uncensoring roop
-- and all developers behind libraries used in this project.
+
+-   [ffmpeg](https://ffmpeg.org/): for making video-related operations easy
+-   [Henry](https://github.com/henryruhs): One of the major contributor in this repo
+-   [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. Please be reminded that the [use of the model is for non-commercial research purposes only](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).
+-   [havok2-htwo](https://github.com/havok2-htwo): for sharing the code for webcam
+-   [GosuDRM](https://github.com/GosuDRM): for the open version of roop
+-   [pereiraroland26](https://github.com/pereiraroland26): Multiple faces support
+-   [vic4key](https://github.com/vic4key): For supporting/contributing to this project
+-   [kier007](https://github.com/kier007): for improving the user experience
+-   [qitianai](https://github.com/qitianai): for multi-lingual support
+-   [laurigates](https://github.com/laurigates): Decoupling stuffs to make everything faster!
+-   [maxwbuckley](https://github.com/maxwbuckley): For making the effort to optimize this for mac!
+-   and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
+-   Footnote: Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
+-   All the wonderful users who helped make this project go viral by starring the repo ❤️
+
+[![Stargazers](https://reporoster.com/stars/hacksider/Deep-Live-Cam)](https://github.com/hacksider/Deep-Live-Cam/stargazers)
+
+## Contributions
+
+![Alt](https://repobeats.axiom.co/api/embed/fec8e29c45dfdb9c5916f3a7830e1249308d20e1.svg "Repobeats analytics image")
+
+## Stars to the Moon 🚀
+
+<a href="https://star-history.com/#hacksider/deep-live-cam&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
+ </picture>
+</a>

benchmark_pipeline.py

@@ -0,0 +1,181 @@
+"""Standalone pipeline benchmark — no UI required.
+
+Captures 200 frames from the webcam and runs the full face swap pipeline,
+printing per-stage timing and effective FPS.
+"""
+import os, sys, time, cv2, numpy as np, queue, threading
+
+# PATH fix for cuDNN (Windows only)
+if sys.platform == "win32":
+    _sp = os.path.join(sys.prefix, "Lib", "site-packages")
+    _torch_lib = os.path.join(_sp, "torch", "lib")
+    if os.path.isdir(_torch_lib):
+        os.environ["PATH"] = _torch_lib + os.pathsep + os.environ["PATH"]
+
+import insightface
+from insightface.app import FaceAnalysis
+from modules.processors.frame.face_swapper import _fast_paste_back
+from modules import platform_info
+
+platform_info.print_banner()
+
+# Pick providers based on what's actually available on this machine.
+if platform_info.HAS_CUDA_PROVIDER:
+    _providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+elif platform_info.HAS_COREML_PROVIDER:
+    _providers = ["CoreMLExecutionProvider", "CPUExecutionProvider"]
+else:
+    _providers = ["CPUExecutionProvider"]
+
+# --- Init models (same as the app) ---
+print(f"Loading models with providers={_providers}...")
+fa = FaceAnalysis(
+    name="buffalo_l",
+    providers=_providers,
+    allowed_modules=["detection", "recognition", "landmark_2d_106"],
+)
+fa.prepare(ctx_id=0, det_size=(640, 640))
+swap_model = insightface.model_zoo.get_model(
+    "models/inswapper_128.onnx",
+    providers=_providers,
+)
+face_size = swap_model.input_size[0]
+aimg_dummy = np.empty((face_size, face_size, 3), dtype=np.uint8)
+
+# --- Camera setup ---
+# Windows: DirectShow explicit for MJPEG 1080p60 support.
+# macOS/Linux: default backend (AVFoundation / V4L2).
+print("Opening camera at 1080p60 MJPEG...")
+if sys.platform == "win32":
+    cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
+else:
+    cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
+cap.set(cv2.CAP_PROP_FPS, 60)
+time.sleep(0.5)
+
+# Warmup + get source face
+for _ in range(15):
+    cap.read()
+ret, src_frame = cap.read()
+faces = fa.get(src_frame)
+if not faces:
+    print("ERROR: No face detected in warmup frame")
+    cap.release()
+    sys.exit(1)
+source_face = faces[0]
+print(f"Source face acquired. Frame: {src_frame.shape}")
+
+# --- Capture thread (same as app) ---
+capture_queue = queue.Queue(maxsize=2)
+stop_event = threading.Event()
+
+def capture_thread():
+    while not stop_event.is_set():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        try:
+            capture_queue.put_nowait(frame)
+        except queue.Full:
+            try:
+                capture_queue.get_nowait()
+            except queue.Empty:
+                pass
+            try:
+                capture_queue.put_nowait(frame)
+            except queue.Full:
+                pass
+
+cap_t = threading.Thread(target=capture_thread, daemon=True)
+cap_t.start()
+
+# --- Warmup processing ---
+print("Warming up pipeline...")
+for _ in range(20):
+    try:
+        frame = capture_queue.get(timeout=0.1)
+    except queue.Empty:
+        continue
+    f = frame.copy()
+    det_faces = fa.get(f)
+    if det_faces:
+        tgt = min(det_faces, key=lambda x: x.bbox[0])
+        bgr_fake, M = swap_model.get(f, tgt, source_face, paste_back=False)
+        _fast_paste_back(f, bgr_fake, aimg_dummy, M)
+
+# --- Benchmark ---
+N = 200
+print(f"\nBenchmarking {N} frames...")
+
+t_queue, t_det, t_onnx, t_paste, t_copy, t_cvt, t_total = [], [], [], [], [], [], []
+det_count = 0
+cached_face = None
+
+for i in range(N):
+    tt = time.perf_counter()
+
+    t0 = time.perf_counter()
+    try:
+        frame = capture_queue.get(timeout=0.1)
+    except queue.Empty:
+        continue
+    t_queue.append((time.perf_counter() - t0) * 1000)
+
+    # Detection every 3rd frame — det-only (no landmark/recognition)
+    det_count += 1
+    if det_count % 3 == 0:
+        t0 = time.perf_counter()
+        from insightface.app.common import Face as _Face
+        bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric='default')
+        if bboxes.shape[0] > 0:
+            idx = int(bboxes[:, 0].argmin())
+            cached_face = _Face(bbox=bboxes[idx, :4], kps=kpss[idx], det_score=bboxes[idx, 4])
+        t_det.append((time.perf_counter() - t0) * 1000)
+
+    if cached_face is not None:
+        # No frame.copy() — _fast_paste_back writes in-place, we own the frame
+        t0 = time.perf_counter()
+        bgr_fake, M = swap_model.get(frame, cached_face, source_face, paste_back=False)
+        t_onnx.append((time.perf_counter() - t0) * 1000)
+
+        t0 = time.perf_counter()
+        result = _fast_paste_back(frame, bgr_fake, aimg_dummy, M)
+        t_paste.append((time.perf_counter() - t0) * 1000)
+
+        # Display prep — resize then flip (no cvtColor needed)
+        t0 = time.perf_counter()
+        small = cv2.resize(result, (640, 360))
+        _ = small[:, :, ::-1]  # BGR→RGB zero-copy
+        t_cvt.append((time.perf_counter() - t0) * 1000)
+
+    t_total.append((time.perf_counter() - tt) * 1000)
+
+stop_event.set()
+cap.release()
+
+# --- Results ---
+def s(name, arr):
+    if not arr:
+        return
+    avg = sum(arr) / len(arr)
+    print(f"  {name:25s}: avg={avg:6.1f}ms  min={min(arr):5.1f}ms  max={max(arr):6.1f}ms  n={len(arr)}")
+
+print(f"\n{'='*55}")
+print(f"  1080p Pipeline Benchmark ({len(t_total)} frames)")
+print(f"{'='*55}")
+s("queue.get (wait for cam)", t_queue)
+s("detection (fa.get)", t_det)
+s("frame.copy()", t_copy)
+s("ONNX swap", t_onnx)
+s("_fast_paste_back", t_paste)
+s("cvtColor BGR->RGB", t_cvt)
+s("TOTAL per frame", t_total)
+
+avg_total = sum(t_total) / len(t_total)
+avg_queue = sum(t_queue) / len(t_queue)
+print(f"\n  Effective FPS:          {1000/avg_total:.1f}")
+print(f"  FPS (excl. cam wait):   {1000/(avg_total - avg_queue):.1f}")
+print(f"{'='*55}")

locales/de.json

@@ -0,0 +1,46 @@
+{
+    "Source x Target Mapper": "Quelle x Ziel Zuordnung",
+    "select a source image": "Wähle ein Quellbild",
+    "Preview": "Vorschau",
+    "select a target image or video": "Wähle ein Zielbild oder Video",
+    "save image output file": "Bildausgabedatei speichern",
+    "save video output file": "Videoausgabedatei speichern",
+    "select a target image": "Wähle ein Zielbild",
+    "source": "Quelle",
+    "Select a target": "Wähle ein Ziel",
+    "Select a face": "Wähle ein Gesicht",
+    "Keep audio": "Audio beibehalten",
+    "Face Enhancer": "Gesichtsverbesserung",
+    "Many faces": "Mehrere Gesichter",
+    "Show FPS": "FPS anzeigen",
+    "Keep fps": "FPS beibehalten",
+    "Keep frames": "Frames beibehalten",
+    "Fix Blueish Cam": "Bläuliche Kamera korrigieren",
+    "Mouth Mask": "Mundmaske",
+    "Show Mouth Mask Box": "Mundmaskenrahmen anzeigen",
+    "Start": "Starten",
+    "Live": "Live",
+    "Destroy": "Beenden",
+    "Map faces": "Gesichter zuordnen",
+    "Processing...": "Verarbeitung läuft...",
+    "Processing succeed!": "Verarbeitung erfolgreich!",
+    "Processing ignored!": "Verarbeitung ignoriert!",
+    "Failed to start camera": "Kamera konnte nicht gestartet werden",
+    "Please complete pop-up or close it.": "Bitte das Pop-up komplettieren oder schließen.",
+    "Getting unique faces": "Einzigartige Gesichter erfassen",
+    "Please select a source image first": "Bitte zuerst ein Quellbild auswählen",
+    "No faces found in target": "Keine Gesichter im Zielbild gefunden",
+    "Add": "Hinzufügen",
+    "Clear": "Löschen",
+    "Submit": "Absenden",
+    "Select source image": "Quellbild auswählen",
+    "Select target image": "Zielbild auswählen",
+    "Please provide mapping!": "Bitte eine Zuordnung angeben!",
+    "At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
+    "At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
+    "Face could not be detected in last upload!": "Im letzten Upload konnte kein Gesicht erkannt werden!",
+    "Select Camera:": "Kamera auswählen:",
+    "All mappings cleared!": "Alle Zuordnungen gelöscht!",
+    "Mappings successfully submitted!": "Zuordnungen erfolgreich übermittelt!",
+    "Source x Target Mapper is already open.": "Quell-zu-Ziel-Zuordnung ist bereits geöffnet."
+}

locales/es.json

@@ -0,0 +1,46 @@
+{
+    "Source x Target Mapper": "Mapeador de fuente x destino",
+    "select a source image": "Seleccionar imagen fuente",
+    "Preview": "Vista previa",
+    "select a target image or video": "elegir un video o una imagen fuente",
+    "save image output file": "guardar imagen final",
+    "save video output file": "guardar video final",
+    "select a target image": "elegir una imagen objetiva",
+    "source": "fuente",
+    "Select a target": "Elegir un destino",
+    "Select a face": "Elegir una cara",
+    "Keep audio": "Mantener audio original",
+    "Face Enhancer": "Potenciador de caras",
+    "Many faces": "Varias caras",
+    "Show FPS": "Mostrar fps",
+    "Keep fps": "Mantener fps",
+    "Keep frames": "Mantener frames",
+    "Fix Blueish Cam": "Corregir tono azul de video",
+    "Mouth Mask": "Máscara de boca",
+    "Show Mouth Mask Box": "Mostrar área de la máscara de boca",
+    "Start": "Iniciar",
+    "Live": "En vivo",
+    "Destroy": "Borrar",
+    "Map faces": "Mapear caras",
+    "Processing...": "Procesando...",
+    "Processing succeed!": "¡Proceso terminado con éxito!",
+    "Processing ignored!": "¡Procesamiento omitido!",
+    "Failed to start camera": "No se pudo iniciar la cámara",
+    "Please complete pop-up or close it.": "Complete o cierre el pop-up",
+    "Getting unique faces": "Buscando caras únicas",
+    "Please select a source image first": "Primero, seleccione una imagen fuente",
+    "No faces found in target": "No se encontró una cara en el destino",
+    "Add": "Agregar",
+    "Clear": "Limpiar",
+    "Submit": "Enviar",
+    "Select source image": "Seleccionar imagen fuente",
+    "Select target image": "Seleccionar imagen destino",
+    "Please provide mapping!": "Por favor, proporcione un mapeo",
+    "At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
+    "At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
+    "Face could not be detected in last upload!": "¡No se pudo encontrar una cara en el último video o imagen!",
+    "Select Camera:": "Elegir cámara:",
+    "All mappings cleared!": "¡Todos los mapeos fueron borrados!",
+    "Mappings successfully submitted!": "Mapeos enviados con éxito!",
+    "Source x Target Mapper is already open.": "El mapeador de fuente x destino ya está abierto."
+}

locales/fi.json

@@ -0,0 +1,46 @@
+{
+    "Source x Target Mapper": "Source x Target Kartoitin",
+    "select an source image": "Valitse lähde kuva",
+    "Preview": "Esikatsele",
+    "select an target image or video": "Valitse kohde kuva tai video",
+    "save image output file": "tallenna kuva",
+    "save video output file": "tallenna video",
+    "select an target image": "Valitse kohde kuva",
+    "source": "lähde",
+    "Select a target": "Valitse kohde",
+    "Select a face": "Valitse kasvot",
+    "Keep audio": "Säilytä ääni",
+    "Face Enhancer": "Kasvojen Parantaja",
+    "Many faces": "Useampia kasvoja",
+    "Show FPS": "Näytä FPS",
+    "Keep fps": "Säilytä FPS",
+    "Keep frames": "Säilytä ruudut",
+    "Fix Blueish Cam": "Korjaa Sinertävä Kamera",
+    "Mouth Mask": "Suu Maski",
+    "Show Mouth Mask Box": "Näytä Suu Maski Laatiko",
+    "Start": "Aloita",
+    "Live": "Live",
+    "Destroy": "Tuhoa",
+    "Map faces": "Kartoita kasvot",
+    "Processing...": "Prosessoi...",
+    "Processing succeed!": "Prosessointi onnistui!",
+    "Processing ignored!": "Prosessointi lopetettu!",
+    "Failed to start camera": "Kameran käynnistäminen epäonnistui",
+    "Please complete pop-up or close it.": "Viimeistele tai sulje ponnahdusikkuna",
+    "Getting unique faces": "Hankitaan uniikkeja kasvoja",
+    "Please select a source image first": "Valitse ensin lähde kuva",
+    "No faces found in target": "Kasvoja ei löydetty kohteessa",
+    "Add": "Lisää",
+    "Clear": "Tyhjennä",
+    "Submit": "Lähetä",
+    "Select source image": "Valitse lähde kuva",
+    "Select target image": "Valitse kohde kuva",
+    "Please provide mapping!": "Tarjoa kartoitus!",
+    "Atleast 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
+    "At least 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
+    "Face could not be detected in last upload!": "Kasvoja ei voitu tunnistaa edellisessä latauksessa!",
+    "Select Camera:": "Valitse Kamera:",
+    "All mappings cleared!": "Kaikki kartoitukset tyhjennetty!",
+    "Mappings successfully submitted!": "Kartoitukset lähetety onnistuneesti!",
+    "Source x Target Mapper is already open.": "Lähde x Kohde Kartoittaja on jo auki."
+}

locales/id.json

@@ -0,0 +1,45 @@
+{
+    "Source x Target Mapper": "Pemetaan Sumber x Target",
+    "select a source image": "Pilih gambar sumber",
+    "Preview": "Pratinjau",
+    "select a target image or video": "Pilih gambar atau video target",
+    "save image output file": "Simpan file keluaran gambar",
+    "save video output file": "Simpan file keluaran video",
+    "select a target image": "Pilih gambar target",
+    "source": "Sumber",
+    "Select a target": "Pilih target",
+    "Select a face": "Pilih wajah",
+    "Keep audio": "Pertahankan audio",
+    "Face Enhancer": "Peningkat wajah",
+    "Many faces": "Banyak wajah",
+    "Show FPS": "Tampilkan FPS",
+    "Keep fps": "Pertahankan FPS",
+    "Keep frames": "Pertahankan frame",
+    "Fix Blueish Cam": "Perbaiki kamera kebiruan",
+    "Mouth Mask": "Masker mulut",
+    "Show Mouth Mask Box": "Tampilkan kotak masker mulut",
+    "Start": "Mulai",
+    "Live": "Langsung",
+    "Destroy": "Hentikan",
+    "Map faces": "Petakan wajah",
+    "Processing...": "Sedang memproses...",
+    "Processing succeed!": "Pemrosesan berhasil!",
+    "Processing ignored!": "Pemrosesan diabaikan!",
+    "Failed to start camera": "Gagal memulai kamera",
+    "Please complete pop-up or close it.": "Harap selesaikan atau tutup pop-up.",
+    "Getting unique faces": "Mengambil wajah unik",
+    "Please select a source image first": "Silakan pilih gambar sumber terlebih dahulu",
+    "No faces found in target": "Tidak ada wajah ditemukan pada target",
+    "Add": "Tambah",
+    "Clear": "Bersihkan",
+    "Submit": "Kirim",
+    "Select source image": "Pilih gambar sumber",
+    "Select target image": "Pilih gambar target",
+    "Please provide mapping!": "Harap tentukan pemetaan!",
+    "At least 1 source with target is required!": "Minimal 1 sumber dengan target diperlukan!",
+    "Face could not be detected in last upload!": "Wajah tidak dapat terdeteksi pada unggahan terakhir!",
+    "Select Camera:": "Pilih Kamera:",
+    "All mappings cleared!": "Semua pemetaan telah dibersihkan!",
+    "Mappings successfully submitted!": "Pemetaan berhasil dikirim!",
+    "Source x Target Mapper is already open.": "Pemetaan Sumber x Target sudah terbuka."
+}

locales/km.json

@@ -0,0 +1,45 @@
+{
+    "Source x Target Mapper": "ប្រភប x បន្ថែម Mapper",
+    "select a source image": "ជ្រើសរើសប្រភពរូបភាព",
+    "Preview": "បង្ហាញ",
+    "select a target image or video": "ជ្រើសរើសគោលដៅរូបភាពឬវីដេអូ",
+    "save image output file": "រក្សាទុកលទ្ធផលឯកសាររូបភាព",
+    "save video output file": "រក្សាទុកលទ្ធផលឯកសារវីដេអូ",
+    "select a target image": "ជ្រើសរើសគោលដៅរូបភាព",
+    "source": "ប្រភព",
+    "Select a target": "ជ្រើសរើសគោលដៅ",
+    "Select a face": "ជ្រើសរើសមុខ",
+    "Keep audio": "រម្លងសម្លេង",
+    "Face Enhancer": "ឧបករណ៍ពង្រឹងមុខ",
+    "Many faces": "ទម្រង់មុខច្រើន",
+    "Show FPS": "បង្ហាញ FPS",
+    "Keep fps": "រម្លង fps",
+    "Keep frames": "រម្លងទម្រង់",
+    "Fix Blueish Cam": "ជួសជុល Cam Blueish",
+    "Mouth Mask": "របាំងមាត់",
+    "Show Mouth Mask Box": "បង្ហាញប្រអប់របាំងមាត់",
+    "Start": "ចាប់ផ្ដើម",
+    "Live": "ផ្សាយផ្ទាល់",
+    "Destroy": "លុប",
+    "Map faces": "ផែនទីមុខ",
+    "Processing...": "កំពុងដំណើរការ...",
+    "Processing succeed!": "ការដំណើរការទទួលបានជោគជ័យ!",
+    "Processing ignored!": "ការដំណើរការមិនទទួលបានជោគជ័យ!",
+    "Failed to start camera": "បរាជ័យដើម្បីចាប់ផ្ដើមបើកកាមេរ៉ា",
+    "Please complete pop-up or close it.": "សូមបញ្ចប់ផ្ទាំងផុស ឬបិទវា.",
+    "Getting unique faces": "ការចាប់ផ្ដើមទម្រង់មុខប្លែក",
+    "Please select a source image first": "សូមជ្រើសរើសប្រភពរូបភាពដំបូង",
+    "No faces found in target": "រកអត់ឃើញមុខនៅក្នុងគោលដៅ",
+    "Add": "បន្ថែម",
+    "Clear": "សម្អាត",
+    "Submit": "បញ្ចូន",
+    "Select source image": "ជ្រើសរើសប្រភពរូបភាព",
+    "Select target image": "ជ្រើសរើសគោលដៅរូបភាព",
+    "Please provide mapping!": "សូមផ្ដល់នៅផែនទី",
+    "At least 1 source with target is required!": "ត្រូវការប្រភពយ៉ាងហោចណាស់ ១ ដែលមានគោលដៅ!",
+    "Face could not be detected in last upload!": "មុខមិនអាចភ្ជាប់នៅក្នុងការបង្ហេាះចុងក្រោយ!",
+    "Select Camera:": "ជ្រើសរើសកាមេរ៉ា",
+    "All mappings cleared!": "ផែនទីទាំងអស់ត្រូវបានសម្អាត!",
+    "Mappings successfully submitted!": "ផែនទីត្រូវបានបញ្ជូនជោគជ័យ!",
+    "Source x Target Mapper is already open.": "ប្រភព x Target Mapper បានបើករួចហើយ។"
+}

locales/ko.json

@@ -0,0 +1,45 @@
+{
+    "Source x Target Mapper": "소스 x 타겟 매퍼",
+    "select a source image": "소스 이미지 선택",
+    "Preview": "미리보기",
+    "select a target image or video": "타겟 이미지 또는 영상 선택",
+    "save image output file": "이미지 출력 파일 저장",
+    "save video output file": "영상 출력 파일 저장",
+    "select a target image": "타겟 이미지 선택",
+    "source": "소스",
+    "Select a target": "타겟 선택",
+    "Select a face": "얼굴 선택",
+    "Keep audio": "오디오 유지",
+    "Face Enhancer": "얼굴 향상",
+    "Many faces": "여러 얼굴",
+    "Show FPS": "FPS 표시",
+    "Keep fps": "FPS 유지",
+    "Keep frames": "프레임 유지",
+    "Fix Blueish Cam": "푸른빛 카메라 보정",
+    "Mouth Mask": "입 마스크",
+    "Show Mouth Mask Box": "입 마스크 박스 표시",
+    "Start": "시작",
+    "Live": "라이브",
+    "Destroy": "종료",
+    "Map faces": "얼굴 매핑",
+    "Processing...": "처리 중...",
+    "Processing succeed!": "처리 성공!",
+    "Processing ignored!": "처리 무시됨!",
+    "Failed to start camera": "카메라 시작 실패",
+    "Please complete pop-up or close it.": "팝업을 완료하거나 닫아주세요.",
+    "Getting unique faces": "고유 얼굴 가져오는 중",
+    "Please select a source image first": "먼저 소스 이미지를 선택해주세요",
+    "No faces found in target": "타겟에서 얼굴을 찾을 수 없음",
+    "Add": "추가",
+    "Clear": "지우기",
+    "Submit": "제출",
+    "Select source image": "소스 이미지 선택",
+    "Select target image": "타겟 이미지 선택",
+    "Please provide mapping!": "매핑을 입력해주세요!",
+    "At least 1 source with target is required!": "최소 하나의 소스와 타겟이 필요합니다!",
+    "Face could not be detected in last upload!": "최근 업로드에서 얼굴을 감지할 수 없습니다!",
+    "Select Camera:": "카메라 선택:",
+    "All mappings cleared!": "모든 매핑이 삭제되었습니다!",
+    "Mappings successfully submitted!": "매핑이 성공적으로 제출되었습니다!",
+    "Source x Target Mapper is already open.": "소스 x 타겟 매퍼가 이미 열려 있습니다."
+}

locales/pt-br.json

@@ -0,0 +1,46 @@
+{
+    "Source x Target Mapper": "Mapeador de Origem x Destino",
+    "select an source image": "Escolha uma imagem de origem",
+    "Preview": "Prévia",
+    "select an target image or video": "Escolha uma imagem ou vídeo de destino",
+    "save image output file": "Salvar imagem final",
+    "save video output file": "Salvar vídeo final",
+    "select an target image": "Escolha uma imagem de destino",
+    "source": "Origem",
+    "Select a target": "Escolha o destino",
+    "Select a face": "Escolha um rosto",
+    "Keep audio": "Manter o áudio original",
+    "Face Enhancer": "Melhorar rosto",
+    "Many faces": "Vários rostos",
+    "Show FPS": "Mostrar FPS",
+    "Keep fps": "Manter FPS",
+    "Keep frames": "Manter frames",
+    "Fix Blueish Cam": "Corrigir tom azulado da câmera",
+    "Mouth Mask": "Máscara da boca",
+    "Show Mouth Mask Box": "Mostrar área da máscara da boca",
+    "Start": "Começar",
+    "Live": "Ao vivo",
+    "Destroy": "Destruir",
+    "Map faces": "Mapear rostos",
+    "Processing...": "Processando...",
+    "Processing succeed!": "Tudo certo!",
+    "Processing ignored!": "Processamento ignorado!",
+    "Failed to start camera": "Não foi possível iniciar a câmera",
+    "Please complete pop-up or close it.": "Finalize ou feche o pop-up",
+    "Getting unique faces": "Buscando rostos diferentes",
+    "Please select a source image first": "Selecione primeiro uma imagem de origem",
+    "No faces found in target": "Nenhum rosto encontrado na imagem de destino",
+    "Add": "Adicionar",
+    "Clear": "Limpar",
+    "Submit": "Enviar",
+    "Select source image": "Escolha a imagem de origem",
+    "Select target image": "Escolha a imagem de destino",
+    "Please provide mapping!": "Você precisa realizar o mapeamento!",
+    "Atleast 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
+    "At least 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
+    "Face could not be detected in last upload!": "Não conseguimos detectar o rosto na última imagem!",
+    "Select Camera:": "Escolher câmera:",
+    "All mappings cleared!": "Todos os mapeamentos foram removidos!",
+    "Mappings successfully submitted!": "Mapeamentos enviados com sucesso!",
+    "Source x Target Mapper is already open.": "O Mapeador de Origem x Destino já está aberto."
+}

locales/ru.json

@@ -0,0 +1,45 @@
+{
+    "Source x Target Mapper": "Сопоставитель Источник x Цель",
+    "select a source image": "выберите исходное изображение",
+    "Preview": "Предпросмотр",
+    "select a target image or video": "выберите целевое изображение или видео",
+    "save image output file": "сохранить выходной файл изображения",
+    "save video output file": "сохранить выходной файл видео",
+    "select a target image": "выберите целевое изображение",
+    "source": "источник",
+    "Select a target": "Выберите целевое изображение",
+    "Select a face": "Выберите лицо",
+    "Keep audio": "Сохранить аудио",
+    "Face Enhancer": "Улучшение лица",
+    "Many faces": "Несколько лиц",
+    "Show FPS": "Показать FPS",
+    "Keep fps": "Сохранить FPS",
+    "Keep frames": "Сохранить кадры",
+    "Fix Blueish Cam": "Исправить синеву камеры",
+    "Mouth Mask": "Маска рта",
+    "Show Mouth Mask Box": "Показать рамку маски рта",
+    "Start": "Старт",
+    "Live": "В реальном времени",
+    "Destroy": "Остановить",
+    "Map faces": "Сопоставить лица",
+    "Processing...": "Обработка...",
+    "Processing succeed!": "Обработка успешна!",
+    "Processing ignored!": "Обработка проигнорирована!",
+    "Failed to start camera": "Не удалось запустить камеру",
+    "Please complete pop-up or close it.": "Пожалуйста, заполните всплывающее окно или закройте его.",
+    "Getting unique faces": "Получение уникальных лиц",
+    "Please select a source image first": "Сначала выберите исходное изображение, пожалуйста",
+    "No faces found in target": "В целевом изображении не найдено лиц",
+    "Add": "Добавить",
+    "Clear": "Очистить",
+    "Submit": "Отправить",
+    "Select source image": "Выбрать исходное изображение",
+    "Select target image": "Выбрать целевое изображение",
+    "Please provide mapping!": "Пожалуйста, укажите сопоставление!",
+    "At least 1 source with target is required!": "Требуется хотя бы 1 источник с целью!",
+    "Face could not be detected in last upload!": "Лицо не обнаружено в последнем загруженном изображении!",
+    "Select Camera:": "Выберите камеру:",
+    "All mappings cleared!": "Все сопоставления очищены!",
+    "Mappings successfully submitted!": "Сопоставления успешно отправлены!",
+    "Source x Target Mapper is already open.": "Сопоставитель Источник-Цель уже открыт."
+}

locales/th.json

@@ -0,0 +1,45 @@
+{
+    "Source x Target Mapper": "ตัวจับคู่ต้นทาง x ปลายทาง",
+    "select a source image": "เลือกรูปภาพต้นฉบับ",
+    "Preview": "ตัวอย่าง",
+    "select a target image or video": "เลือกรูปภาพหรือวิดีโอเป้าหมาย",
+    "save image output file": "บันทึกไฟล์รูปภาพ",
+    "save video output file": "บันทึกไฟล์วิดีโอ",
+    "select a target image": "เลือกรูปภาพเป้าหมาย",
+    "source": "ต้นฉบับ",
+    "Select a target": "เลือกเป้าหมาย",
+    "Select a face": "เลือกใบหน้า",
+    "Keep audio": "เก็บเสียง",
+    "Face Enhancer": "ปรับปรุงใบหน้า",
+    "Many faces": "หลายใบหน้า",
+    "Show FPS": "แสดง FPS",
+    "Keep fps": "คงค่า FPS",
+    "Keep frames": "คงค่าเฟรม",
+    "Fix Blueish Cam": "แก้ไขภาพอมฟ้าจากกล้อง",
+    "Mouth Mask": "มาสก์ปาก",
+    "Show Mouth Mask Box": "แสดงกรอบมาสก์ปาก",
+    "Start": "เริ่ม",
+    "Live": "สด",
+    "Destroy": "หยุด",
+    "Map faces": "จับคู่ใบหน้า",
+    "Processing...": "กำลังประมวลผล...",
+    "Processing succeed!": "ประมวลผลสำเร็จแล้ว!",
+    "Processing ignored!": "การประมวลผลถูกละเว้น",
+    "Failed to start camera": "ไม่สามารถเริ่มกล้องได้",
+    "Please complete pop-up or close it.": "โปรดดำเนินการในป๊อปอัปให้เสร็จสิ้น หรือปิด",
+    "Getting unique faces": "กำลังค้นหาใบหน้าที่ไม่ซ้ำกัน",
+    "Please select a source image first": "โปรดเลือกภาพต้นฉบับก่อน",
+    "No faces found in target": "ไม่พบใบหน้าในภาพเป้าหมาย",
+    "Add": "เพิ่ม",
+    "Clear": "ล้าง",
+    "Submit": "ส่ง",
+    "Select source image": "เลือกภาพต้นฉบับ",
+    "Select target image": "เลือกภาพเป้าหมาย",
+    "Please provide mapping!": "โปรดระบุการจับคู่!",
+    "At least 1 source with target is required!": "ต้องมีการจับคู่ต้นฉบับกับเป้าหมายอย่างน้อย 1 คู่!",
+    "Face could not be detected in last upload!": "ไม่สามารถตรวจพบใบหน้าในไฟล์อัปโหลดล่าสุด!",
+    "Select Camera:": "เลือกกล้อง:",
+    "All mappings cleared!": "ล้างการจับคู่ทั้งหมดแล้ว!",
+    "Mappings successfully submitted!": "ส่งการจับคู่สำเร็จแล้ว!",
+    "Source x Target Mapper is already open.": "ตัวจับคู่ต้นทาง x ปลายทาง เปิดอยู่แล้ว"
+}

locales/zh.json

@@ -0,0 +1,46 @@
+{
+    "Source x Target Mapper": "Source x Target Mapper",
+    "select a source image": "选择一个源图像",
+    "Preview": "预览",
+    "select a target image or video": "选择一个目标图像或视频",
+    "save image output file": "保存图像输出文件",
+    "save video output file": "保存视频输出文件",
+    "select a target image": "选择一个目标图像",
+    "source": "源",
+    "Select a target": "选择一个目标",
+    "Select a face": "选择一张脸",
+    "Keep audio": "保留音频",
+    "Face Enhancer": "面纹增强器",
+    "Many faces": "多脸",
+    "Show FPS": "显示帧率",
+    "Keep fps": "保持帧率",
+    "Keep frames": "保持帧数",
+    "Fix Blueish Cam": "修复偏蓝的摄像头",
+    "Mouth Mask": "口罩",
+    "Show Mouth Mask Box": "显示口罩盒",
+    "Start": "开始",
+    "Live": "直播",
+    "Destroy": "结束",
+    "Map faces": "识别人脸",
+    "Processing...": "处理中...",
+    "Processing succeed!": "处理成功!",
+    "Processing ignored!": "处理被忽略!",
+    "Failed to start camera": "启动相机失败",
+    "Please complete pop-up or close it.": "请先完成弹出窗口或者关闭它",
+    "Getting unique faces": "获取独特面部",
+    "Please select a source image first": "请先选择一个源图像",
+    "No faces found in target": "目标图像中没有人脸",
+    "Add": "添加",
+    "Clear": "清除",
+    "Submit": "确认",
+    "Select source image": "请选取源图像",
+    "Select target image": "请选取目标图像",
+    "Please provide mapping!": "请提供映射",
+    "At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
+    "At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
+    "Face could not be detected in last upload!": "最近上传的图像中没有检测到人脸!",
+    "Select Camera:": "选择摄像头",
+    "All mappings cleared!": "所有映射均已清除!",
+    "Mappings successfully submitted!": "成功提交映射！",
+    "Source x Target Mapper is already open.": "源 x 目标映射器已打开。"
+}

models/instructions.txt

@@ -1 +1,4 @@
-just put the models in this folder
+just put the models in this folder -
+
+https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx?download=true
+https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth

modules/__init__.py

@@ -0,0 +1,38 @@
+import os
+import cv2
+import numpy as np
+
+
+# Utility function to support unicode characters in file paths for reading.
+# OpenCV's cv2.imread() encodes the path with the locale ANSI code page on
+# Windows, so it silently returns None for paths containing non-ASCII
+# characters (Chinese, Japanese, Cyrillic, accents, ...). Reading the bytes
+# through NumPy (which uses Python's unicode-aware file I/O) and decoding them
+# in memory sidesteps that limitation. Returns None on failure, matching
+# cv2.imread() so it stays a drop-in replacement.
+def imread_unicode(path, flags=cv2.IMREAD_COLOR):
+    try:
+        data = np.fromfile(path, dtype=np.uint8)
+        if data.size == 0:
+            return None
+        return cv2.imdecode(data, flags)
+    except Exception:
+        return None
+
+
+# Utility function to support unicode characters in file paths for writing.
+# cv2.imwrite() has the same ANSI-path limitation, so we encode the image in
+# memory and write the bytes out with NumPy's unicode-aware file I/O. Returns
+# True/False like cv2.imwrite() so it stays a drop-in replacement.
+def imwrite_unicode(path, img, params=None):
+    try:
+        root, ext = os.path.splitext(path)
+        if not ext:
+            ext = ".png"
+        result, encoded_img = cv2.imencode(ext, img, params if params is not None else [])
+        if not result:
+            return False
+        encoded_img.tofile(path)
+        return True
+    except Exception:
+        return False

modules/capturer.py

@@ -1,38 +1,38 @@
-from typing import Any, Optional
+from typing import Any
 import cv2
+import modules.globals  # Import the globals to check the color correction toggle
+from modules.gpu_processing import gpu_cvt_color
 
-def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Any]:
-    """Retrieve a specific frame from a video."""
+
+def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
     capture = cv2.VideoCapture(video_path)
 
-    if not capture.isOpened():
-        print(f"Error: Cannot open video file {video_path}")
-        return None
-
-    frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    # Ensure frame_number is within the valid range
-    frame_number = max(0, min(frame_number, frame_total - 1))
-
-    capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
-    has_frame, frame = capture.read()
-    capture.release()
-
-    if not has_frame:
-        print(f"Error: Cannot read frame {frame_number} from {video_path}")
-        return None
+    # Set MJPEG format to ensure correct color space handling
+    capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
     
-    return frame
+    # Only force RGB conversion if color correction is enabled
+    if modules.globals.color_correction:
+        capture.set(cv2.CAP_PROP_CONVERT_RGB, 1)
+    
+    frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    if frame_total <= 0:
+        capture.release()
+        return None
+
+    target_index = 0 if frame_number <= 1 else min(frame_total - 1, frame_number - 1)
+    capture.set(cv2.CAP_PROP_POS_FRAMES, target_index)
+    has_frame, frame = capture.read()
+
+    if has_frame and modules.globals.color_correction:
+        # Convert the frame color if necessary
+        frame = gpu_cvt_color(frame, cv2.COLOR_BGR2RGB)
+
+    capture.release()
+    return frame if has_frame else None
+
 
 def get_video_frame_total(video_path: str) -> int:
-    """Get the total number of frames in a video."""
     capture = cv2.VideoCapture(video_path)
-
-    if not capture.isOpened():
-        print(f"Error: Cannot open video file {video_path}")
-        return 0
-    
-    frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
     capture.release()
-
-    return frame_total
+    return video_frame_total

modules/cluster_analysis.py

@@ -0,0 +1,43 @@
+import numpy as np
+from sklearn.cluster import KMeans
+from typing import Any
+
+
+def find_cluster_centroids(embeddings, max_k=10) -> Any:
+    n_samples = len(embeddings)
+    if n_samples == 0:
+        raise ValueError("embeddings must not be empty")
+    if max_k < 1:
+        raise ValueError("max_k must be at least 1")
+
+    max_k = min(max_k, n_samples)
+    if max_k == 1:
+        kmeans = KMeans(n_clusters=1, random_state=0)
+        kmeans.fit(embeddings)
+        return kmeans.cluster_centers_
+
+    inertia = []
+    cluster_centroids = []
+    K = range(1, max_k+1)
+
+    for k in K:
+        kmeans = KMeans(n_clusters=k, random_state=0)
+        kmeans.fit(embeddings)
+        inertia.append(kmeans.inertia_)
+        cluster_centroids.append({"k": k, "centroids": kmeans.cluster_centers_})
+
+    diffs = [inertia[i] - inertia[i+1] for i in range(len(inertia)-1)]
+    optimal_centroids = cluster_centroids[diffs.index(max(diffs)) + 1]['centroids']
+
+    return optimal_centroids
+
+def find_closest_centroid(centroids: list, normed_face_embedding) -> list:
+    try:
+        centroids = np.array(centroids)
+        normed_face_embedding = np.array(normed_face_embedding)
+        similarities = np.dot(centroids, normed_face_embedding)
+        closest_centroid_index = np.argmax(similarities)
+        
+        return closest_centroid_index, centroids[closest_centroid_index]
+    except ValueError:
+        return None

modules/core.py

@@ -1,92 +1,67 @@
 import os
 import sys
+# single thread doubles cuda performance - needs to be set before torch import
+if any(arg.startswith('--execution-provider') for arg in sys.argv):
+    os.environ['OMP_NUM_THREADS'] = '6'
+# reduce tensorflow log level
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 import warnings
+from typing import List
 import platform
 import signal
 import shutil
 import argparse
-from typing import List
-
-# Set environment variables for CUDA performance and TensorFlow logging
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-
-import torch
+try:
+    import torch
+    HAS_TORCH = True
+except ImportError:
+    HAS_TORCH = False
 import onnxruntime
-import tensorflow
+try:
+    import tensorflow
+    HAS_TENSORFLOW = True
+except ImportError:
+    HAS_TENSORFLOW = False
 
 import modules.globals
 import modules.metadata
 import modules.ui as ui
-from modules.processors.frame.core import get_frame_processors_modules
-from modules.utilities import (
-    has_image_extension,
-    is_image,
-    is_video,
-    detect_fps,
-    create_video,
-    extract_frames,
-    get_temp_frame_paths,
-    restore_audio,
-    create_temp,
-    move_temp,
-    clean_temp,
-    normalize_output_path
-)
+from modules.processors.frame.core import get_frame_processors_modules, process_video_in_memory
+from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
 
-# Filter warnings
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-# Cross-platform resource management
-if platform.system() == 'Darwin' and 'ROCMExecutionProvider' in modules.globals.execution_providers:
+if HAS_TORCH and 'ROCMExecutionProvider' in modules.globals.execution_providers:
     del torch
 
+warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
+if HAS_TORCH:
+    warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
+
 
 def parse_args() -> None:
     signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
     program = argparse.ArgumentParser()
-    program.add_argument('-s', '--source', help='Select a source image', dest='source_path')
-    program.add_argument('-t', '--target', help='Select a target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='Select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='Pipeline of frame processors', dest='frame_processor',
-                         default=['face_swapper'], choices=['face_swapper', 'face_enhancer', 'super_resolution'],
-                         nargs='+')
-    program.add_argument('--keep-fps', help='Keep original fps', dest='keep_fps', action='store_true', default=False)
-    program.add_argument('--keep-audio', help='Keep original audio', dest='keep_audio', action='store_true',
-                         default=True)
-    program.add_argument('--keep-frames', help='Keep temporary frames', dest='keep_frames', action='store_true',
-                         default=False)
-    program.add_argument('--many-faces', help='Process every face', dest='many_faces', action='store_true',
-                         default=False)
-    program.add_argument('--video-encoder', help='Adjust output video encoder', dest='video_encoder', default='libx264',
-                         choices=['libx264', 'libx265', 'libvpx-vp9'])
-    program.add_argument('--video-quality', help='Adjust output video quality', dest='video_quality', type=int,
-                         default=18,
-                         choices=range(52), metavar='[0-51]')
-    program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame',
-                         dest='live_mirror', action='store_true', default=False)
-    program.add_argument('--live-resizable', help='The live camera frame is resizable',
-                         dest='live_resizable', action='store_true', default=False)
-    program.add_argument('--max-memory', help='Maximum amount of RAM in GB', dest='max_memory', type=int,
-                         default=suggest_max_memory())
-    program.add_argument('--execution-provider', help='Execution provider', dest='execution_provider', default=['cpu'],
-                         choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='Number of execution threads', dest='execution_threads', type=int,
-                         default=suggest_execution_threads())
-    program.add_argument('--headless', help='Run in headless mode', dest='headless', default=False, action='store_true')
-    program.add_argument('--enhancer-upscale-factor',
-                         help='Sets the upscale factor for the enhancer. Only applies if `face_enhancer` is set as a frame-processor',
-                         dest='enhancer_upscale_factor', type=int, default=1)
-    program.add_argument('--source-image-scaling-factor', help='Set the upscale factor for source images',
-                         dest='source_image_scaling_factor', default=2, type=int)
-    program.add_argument('-r', '--super-resolution-scale-factor', dest='super_resolution_scale_factor',
-                         help='Set the upscale factor for super resolution', default=4, choices=[2, 3, 4], type=int)
-    program.add_argument('-v', '--version', action='version',
-                         version=f'{modules.metadata.name} {modules.metadata.version}')
+    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
+    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
+    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
+    program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'], nargs='+')
+    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
+    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
+    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
+    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
+    program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
+    program.add_argument('--map-faces', help='map source target faces', dest='map_faces', action='store_true', default=False)
+    program.add_argument('--mouth-mask', help='mask the mouth region', dest='mouth_mask', action='store_true', default=False)
+    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
+    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
+    program.add_argument('-l', '--lang', help='Ui language', default="en")
+    program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
+    program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
+    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
+    program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=[suggest_default_execution_provider()], choices=suggest_execution_providers(), nargs='+')
+    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=None)
+    program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')
 
-    # Register deprecated args
+    # register deprecated args
     program.add_argument('-f', '--face', help=argparse.SUPPRESS, dest='source_path_deprecated')
     program.add_argument('--cpu-cores', help=argparse.SUPPRESS, dest='cpu_cores_deprecated', type=int)
     program.add_argument('--gpu-vendor', help=argparse.SUPPRESS, dest='gpu_vendor_deprecated')
@@ -96,14 +71,16 @@ def parse_args() -> None:
 
     modules.globals.source_path = args.source_path
     modules.globals.target_path = args.target_path
-    modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path,
-                                                        args.output_path)
+    modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path)
     modules.globals.frame_processors = args.frame_processor
     modules.globals.headless = args.source_path or args.target_path or args.output_path
     modules.globals.keep_fps = args.keep_fps
     modules.globals.keep_audio = args.keep_audio
     modules.globals.keep_frames = args.keep_frames
     modules.globals.many_faces = args.many_faces
+    modules.globals.mouth_mask = args.mouth_mask
+    modules.globals.nsfw_filter = args.nsfw_filter
+    modules.globals.map_faces = args.map_faces
     modules.globals.video_encoder = args.video_encoder
     modules.globals.video_quality = args.video_quality
     modules.globals.live_mirror = args.live_mirror
@@ -111,26 +88,23 @@ def parse_args() -> None:
     modules.globals.max_memory = args.max_memory
     modules.globals.execution_providers = decode_execution_providers(args.execution_provider)
     modules.globals.execution_threads = args.execution_threads
-    modules.globals.headless = args.headless
-    modules.globals.enhancer_upscale_factor = args.enhancer_upscale_factor
-    modules.globals.source_image_scaling_factor = args.source_image_scaling_factor
-    modules.globals.sr_scale_factor = args.super_resolution_scale_factor
-    # Handle face enhancer tumbler
-    modules.globals.fp_ui['face_enhancer'] = 'face_enhancer' in args.frame_processor
+    modules.globals.lang = args.lang
 
-    modules.globals.nsfw = False
+    # The argparse default (None) avoids evaluating suggest_execution_threads()
+    # before providers are decoded, and deprecated-arg overrides above may
+    # have already set execution_threads.
+    if modules.globals.execution_threads is None:
+        modules.globals.execution_threads = suggest_execution_threads()
 
-    # Handle deprecated arguments
-    handle_deprecated_args(args)
+    #for ENHANCER tumblers:
+    for enhancer_key in ('face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'):
+        modules.globals.fp_ui[enhancer_key] = enhancer_key in args.frame_processor
 
-
-def handle_deprecated_args(args) -> None:
-    """Handle deprecated arguments by translating them to the new format."""
+    # translate deprecated args
     if args.source_path_deprecated:
         print('\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\033[0m')
         modules.globals.source_path = args.source_path_deprecated
-        modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path,
-                                                            args.output_path)
+        modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path, args.output_path)
     if args.cpu_cores_deprecated:
         print('\033[33mArgument --cpu-cores is deprecated. Use --execution-threads instead.\033[0m')
         modules.globals.execution_threads = args.cpu_cores_deprecated
@@ -141,7 +115,7 @@ def handle_deprecated_args(args) -> None:
         print('\033[33mArgument --gpu-vendor nvidia is deprecated. Use --execution-provider cuda instead.\033[0m')
         modules.globals.execution_providers = decode_execution_providers(['cuda'])
     if args.gpu_vendor_deprecated == 'amd':
-        print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider rocm instead.\033[0m')
+        print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider cuda instead.\033[0m')
         modules.globals.execution_providers = decode_execution_providers(['rocm'])
     if args.gpu_threads_deprecated:
         print('\033[33mArgument --gpu-threads is deprecated. Use --execution-threads instead.\033[0m')
@@ -149,22 +123,27 @@ def handle_deprecated_args(args) -> None:
 
 
 def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider.replace('ExecutionProvider', '').lower() for provider in execution_providers]
+    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
 
 
 def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    available_providers = onnxruntime.get_available_providers()
-    encoded_providers = encode_execution_providers(available_providers)
-
-    selected_providers = [available_providers[encoded_providers.index(req)] for req in execution_providers
-                          if req in encoded_providers]
-
-    # Default to CPU if no suitable providers are found
-    return selected_providers if selected_providers else ['CPUExecutionProvider']
+    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
+            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
 
 
 def suggest_max_memory() -> int:
-    return 4 if platform.system().lower() == 'darwin' else 16
+    if platform.system().lower() == 'darwin':
+        return 4
+    return 16
+
+
+def suggest_default_execution_provider() -> str:
+    """Pick the best available provider: cuda > rocm > coreml > openvino > dml > cpu."""
+    available = encode_execution_providers(onnxruntime.get_available_providers())
+    for pref in ('cuda', 'rocm', 'coreml', 'openvino', 'dml'):
+        if pref in available:
+            return pref
+    return 'cpu'
 
 
 def suggest_execution_providers() -> List[str]:
@@ -172,43 +151,45 @@ def suggest_execution_providers() -> List[str]:
 
 
 def suggest_execution_threads() -> int:
-    if 'dml' in modules.globals.execution_providers:
+    """Suggest optimal thread count based on hardware and execution provider."""
+    import os
+    
+    # Get CPU count
+    cpu_count = os.cpu_count() or 4
+    
+    if 'DmlExecutionProvider' in modules.globals.execution_providers:
         return 1
-    if 'rocm' in modules.globals.execution_providers:
+    if 'ROCMExecutionProvider' in modules.globals.execution_providers:
         return 1
-    return 8
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+        return 2
+    if 'OpenVINOExecutionProvider' in modules.globals.execution_providers:
+        return 1
+    
+    # For CPU execution, use most cores but leave some for system
+    return max(4, min(cpu_count - 2, 16))
 
 
 def limit_resources() -> None:
-    # Prevent TensorFlow memory leak
-    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    for gpu in gpus:
-        tensorflow.config.experimental.set_memory_growth(gpu, True)
-
-    # Limit memory usage
+    # prevent tensorflow memory leak
+    if HAS_TENSORFLOW:
+        gpus = tensorflow.config.experimental.list_physical_devices('GPU')
+        for gpu in gpus:
+            tensorflow.config.experimental.set_memory_growth(gpu, True)
+    # limit memory usage
     if modules.globals.max_memory:
         memory = modules.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = modules.globals.max_memory * 1024 ** 3
-        elif platform.system().lower() == 'windows':
+        if platform.system().lower() == 'windows':
             import ctypes
             kernel32 = ctypes.windll.kernel32
             kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
         else:
             import resource
-            try:
-                soft, hard = resource.getrlimit(resource.RLIMIT_DATA)
-                if memory > hard:
-                    print(
-                        f"Warning: Requested memory limit {memory / (1024 ** 3)} GB exceeds system's hard limit. Setting to maximum allowed {hard / (1024 ** 3)} GB.")
-                    memory = hard
-                resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-            except ValueError as e:
-                print(f"Warning: Could not set memory limit: {e}. Continuing with default limits.")
+            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
 
 
 def release_resources() -> None:
-    if 'cuda' in modules.globals.execution_providers:
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers and HAS_TORCH:
         torch.cuda.empty_cache()
 
 
@@ -219,97 +200,114 @@ def pre_check() -> bool:
     if not shutil.which('ffmpeg'):
         update_status('ffmpeg is not installed.')
         return False
-    if 'cuda' in modules.globals.execution_providers and not torch.cuda.is_available():
-        update_status('CUDA is not available. Please check your GPU or CUDA installation.')
-        return False
     return True
 
 
 def update_status(message: str, scope: str = 'DLC.CORE') -> None:
     print(f'[{scope}] {message}')
-    if not modules.globals.headless and ui.status_label:
+    if not modules.globals.headless:
         ui.update_status(message)
 
-
 def start() -> None:
+    """Start processing with performance monitoring."""
+    import time
+    
+    start_time = time.time()
+    
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         if not frame_processor.pre_start():
             return
-
-    # Process image to image
+    update_status('Processing...')
+    
+    # process image to image
     if has_image_extension(modules.globals.target_path):
-        process_image_to_image()
+        if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
+            return
+        try:
+            shutil.copy2(modules.globals.target_path, modules.globals.output_path)
+        except Exception as e:
+            print("Error copying file:", str(e))
+        for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
+            update_status('Progressing...', frame_processor.NAME)
+            frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
+            release_resources()
+        if is_image(modules.globals.target_path):
+            elapsed = time.time() - start_time
+            update_status(f'Processing to image succeed! (Time: {elapsed:.2f}s)')
+        else:
+            update_status('Processing to image failed!')
+        return
+    
+    # process image to videos
+    if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
         return
 
-    # Process image to video
-    process_image_to_video()
-
-
-def process_image_to_image() -> None:
-    if modules.globals.nsfw:
-        from modules.predicter import predict_image
-        if predict_image(modules.globals.target_path):
-            destroy(to_quit=False)
-            update_status('Processing to image ignored!')
-            return
-
-    try:
-        shutil.copy2(modules.globals.target_path, modules.globals.output_path)
-    except Exception as e:
-        print("Error copying file:", str(e))
-
-    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
-        update_status('Processing...', frame_processor.NAME)
-        frame_processor.process_image(modules.globals.source_path, modules.globals.output_path,
-                                      modules.globals.output_path)
-        release_resources()
-
-    if is_image(modules.globals.target_path):
-        update_status('Processing to image succeeded!')
-    else:
-        update_status('Processing to image failed!')
-
-
-def process_image_to_video() -> None:
-    if modules.globals.nsfw:
-        from modules.predicter import predict_video
-        if predict_video(modules.globals.target_path):
-            destroy(to_quit=False)
-            update_status('Processing to video ignored!')
-            return
-
-    update_status('Creating temporary resources...')
-    create_temp(modules.globals.target_path)
-    update_status('Extracting frames...')
-    extract_frames(modules.globals.target_path)
-    temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
-    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
-        update_status('Processing...', frame_processor.NAME)
-        frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
-        release_resources()
-
-    handle_video_fps()
-    handle_video_audio()
-    clean_temp(modules.globals.target_path)
-
-    if is_video(modules.globals.target_path):
-        update_status('Processing to video succeeded!')
-    else:
-        update_status('Processing to video failed!')
-
-
-def handle_video_fps() -> None:
+    # Detect FPS early (needed by both pipelines)
     if modules.globals.keep_fps:
         update_status('Detecting fps...')
         fps = detect_fps(modules.globals.target_path)
-        update_status(f'Creating video with {fps} fps...')
-        create_video(modules.globals.target_path, fps)
     else:
-        update_status('Creating video with 30.0 fps...')
-        create_video(modules.globals.target_path)
+        fps = 30.0
 
+    video_created = False
 
-def handle_video_audio() -> None:
+    # --- In-memory pipeline (non-map_faces only) ---
+    # Reads frames from FFmpeg pipe, processes in memory, encodes directly.
+    # Eliminates all per-frame PNG disk I/O for a major speed-up.
+    if not modules.globals.map_faces:
+        update_status(f'Processing video in-memory at {fps} fps...')
+        create_temp(modules.globals.target_path)
+
+        processing_start = time.time()
+        video_created = process_video_in_memory(
+            modules.globals.source_path,
+            modules.globals.target_path,
+            fps,
+        )
+        processing_time = time.time() - processing_start
+        release_resources()
+
+        if video_created:
+            update_status(f'In-memory processing + encoding completed in {processing_time:.2f}s')
+
+    # --- Disk-based fallback (required for map_faces, or if pipe failed) ---
+    if not video_created:
+        if not modules.globals.map_faces:
+            update_status('Falling back to disk-based processing...')
+
+        extraction_start = time.time()
+        if not modules.globals.map_faces:
+            create_temp(modules.globals.target_path)
+            update_status('Extracting frames...')
+            extract_frames(modules.globals.target_path)
+        extraction_time = time.time() - extraction_start
+
+        temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
+        total_frames = len(temp_frame_paths)
+        update_status(f'Processing {total_frames} frames with {modules.globals.execution_threads} threads...')
+
+        processing_start = time.time()
+        for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
+            update_status('Progressing...', frame_processor.NAME)
+            frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
+            release_resources()
+        processing_time = time.time() - processing_start
+        fps_processing = total_frames / processing_time if processing_time > 0 else 0
+        update_status(f'Frame processing completed in {processing_time:.2f}s ({fps_processing:.2f} fps)')
+
+        encoding_start = time.time()
+        update_status(f'Creating video with {fps} fps...')
+        video_created = create_video(modules.globals.target_path, fps)
+        encoding_time = time.time() - encoding_start
+        if video_created:
+            update_status(f'Video encoding completed in {encoding_time:.2f}s')
+
+    if not video_created:
+        update_status('Video encoding failed. No temporary output video was created.')
+        clean_temp(modules.globals.target_path)
+        return
+    
+    # handle audio
     if modules.globals.keep_audio:
         if modules.globals.keep_fps:
             update_status('Restoring audio...')
@@ -318,29 +316,37 @@ def handle_video_audio() -> None:
         restore_audio(modules.globals.target_path, modules.globals.output_path)
     else:
         move_temp(modules.globals.target_path, modules.globals.output_path)
+    
+    # clean and validate
+    clean_temp(modules.globals.target_path)
+    
+    total_time = time.time() - start_time
+    if is_video(modules.globals.target_path) and modules.globals.output_path and os.path.isfile(modules.globals.output_path):
+        update_status(f'Video processing succeeded! Total time: {total_time:.2f}s')
+    else:
+        update_status('Processing to video failed!')
 
 
 def destroy(to_quit=True) -> None:
     if modules.globals.target_path:
         clean_temp(modules.globals.target_path)
-    if to_quit: quit()
+    if to_quit:
+        quit()
 
 
 def run() -> None:
-    try:
-        parse_args()
-        if not pre_check():
+    parse_args()
+    if not pre_check():
+        return
+    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
+        if not frame_processor.pre_check():
             return
-        for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
-            if not frame_processor.pre_check():
-                return
-        limit_resources()
-        if modules.globals.headless:
-            start()
-        else:
-            window = ui.init(start, destroy)
-            window.mainloop()
-    except Exception as e:
-        print(f"UI initialization failed: {str(e)}")
-        update_status(f"UI initialization failed: {str(e)}")
-        destroy()  # Ensure any resources are cleaned up on failure
+    # Pre-load face analyser in main thread before GUI starts
+    #from modules.face_analyser import get_face_analyser
+    #get_face_analyser()
+    limit_resources()
+    if modules.globals.headless:
+        start()
+    else:
+        window = ui.init(start, destroy, modules.globals.lang)
+        window.mainloop()

modules/custom_types.py

@@ -0,0 +1,7 @@
+from typing import Any
+
+from insightface.app.common import Face
+import numpy
+ 
+Face = Face
+Frame = numpy.ndarray[Any, Any] 

modules/face_analyser.py

@@ -1,27 +1,374 @@
-from typing import Any, Optional
+import os
+import shutil
+from typing import Any
 import insightface
+import threading
 
 import modules.globals
+from modules import imread_unicode, imwrite_unicode
+from tqdm import tqdm
 from modules.typing import Frame
+from modules.cluster_analysis import find_cluster_centroids, find_closest_centroid
+from modules.utilities import get_temp_directory_path, create_temp, extract_frames, clean_temp, get_temp_frame_paths
+from pathlib import Path
 
-FACE_ANALYSER: Optional[insightface.app.FaceAnalysis] = None
+FACE_ANALYSER = None
+FACE_ANALYSER_LOCK = threading.Lock()
 
-def get_face_analyser() -> insightface.app.FaceAnalysis:
+DET_SIZE = (640, 640)
+
+
+def get_face_analyser() -> Any:
+    """Get face analyser with thread-safe initialization."""
     global FACE_ANALYSER
 
     if FACE_ANALYSER is None:
-        FACE_ANALYSER = insightface.app.FaceAnalysis(
-            name='buffalo_l', 
-            providers=modules.globals.execution_providers
-        )
-        FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
-    
+        with FACE_ANALYSER_LOCK:
+            # Double-check after acquiring lock
+            if FACE_ANALYSER is None:
+                from modules.processors.frame._onnx_enhancer import (
+                    build_provider_config,
+                )
+                providers = build_provider_config()
+                FACE_ANALYSER = insightface.app.FaceAnalysis(
+                    name='buffalo_l',
+                    providers=providers,
+                    allowed_modules=['detection', 'recognition', 'landmark_2d_106']
+                )
+                FACE_ANALYSER.prepare(ctx_id=0, det_size=DET_SIZE)
+                _optimize_det_model(FACE_ANALYSER, providers)
     return FACE_ANALYSER
 
-def get_one_face(frame: Frame) -> Optional[Any]:
-    faces = get_face_analyser().get(frame)
-    return min(faces, key=lambda x: x.bbox[0], default=None)
 
-def get_many_faces(frame: Frame) -> Optional[Any]:
-    faces = get_face_analyser().get(frame)
-    return faces if faces else None
+def _optimize_det_model(fa: Any, providers) -> None:
+    """Replace the detection model's ONNX session with a CoreML-optimized one.
+
+    Folds dynamic Shape→Gather chains into constants (the input size is
+    fixed at det_size), eliminating CPU↔ANE partition boundaries in the
+    RetinaFace FPN upsampling path.  21ms → 4ms on M3 Max.
+    """
+    from modules.onnx_optimize import optimize_for_coreml, IS_APPLE_SILICON
+    if not IS_APPLE_SILICON:
+        return
+
+    det_model = fa.det_model
+    model_path = getattr(det_model, 'model_file', None)
+    if model_path is None or not os.path.exists(model_path):
+        return
+
+    input_shape = (1, 3, DET_SIZE[1], DET_SIZE[0])
+    optimized_path = optimize_for_coreml(model_path, input_shape=input_shape)
+    if optimized_path == model_path:
+        return
+
+    import onnxruntime
+    session_options = onnxruntime.SessionOptions()
+    session_options.graph_optimization_level = (
+        onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+    )
+
+    # Route detection to GPU shader cores (CPUAndGPU) instead of ANE.
+    # This lets detection run concurrently with the swap model on the
+    # ANE, overlapping the two inference calls.  Detection is fast
+    # enough on GPU (~4ms) and this frees ANE for the heavier swap.
+    det_providers = []
+    for p in providers:
+        name = p[0] if isinstance(p, tuple) else p
+        if name == "CoreMLExecutionProvider":
+            det_providers.append((
+                "CoreMLExecutionProvider",
+                {"ModelFormat": "MLProgram", "MLComputeUnits": "CPUAndGPU"},
+            ))
+        else:
+            det_providers.append(p)
+
+    det_model.session = onnxruntime.InferenceSession(
+        optimized_path, sess_options=session_options, providers=det_providers,
+    )
+
+
+def _needs_landmark() -> bool:
+    """Check whether any active feature requires 106-point landmarks.
+
+    Landmarks are needed by face enhancers and mouth masking, but not
+    by the face swapper alone.
+    """
+    if getattr(modules.globals, "mouth_mask", False):
+        return True
+    processors = getattr(modules.globals, "frame_processors", [])
+    return any(p in processors for p in
+               ("face_enhancer", "face_enhancer_gpen256", "face_enhancer_gpen512"))
+
+
+def _is_dml() -> bool:
+    return any("DmlExecutionProvider" in p for p in modules.globals.execution_providers)
+
+
+def _analyse_faces(frame: Frame) -> list:
+    """Run face detection, then recognition (and optionally landmark).
+
+    Replaces InsightFace's ``FaceAnalysis.get()`` to skip the
+    landmark_2d_106 model when only face_swapper is active (saves ~1ms
+    per face and avoids an unnecessary ONNX session call).
+    """
+    fa = get_face_analyser()
+
+    bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric="default")
+    if bboxes.shape[0] == 0:
+        return []
+
+    need_landmark = _needs_landmark()
+    rec_model = fa.models.get("recognition")
+    lmk_model = fa.models.get("landmark_2d_106") if need_landmark else None
+
+    from insightface.app.common import Face
+
+    faces = []
+    for i in range(bboxes.shape[0]):
+        face = Face(bbox=bboxes[i, 0:4],
+                    kps=kpss[i] if kpss is not None else None,
+                    det_score=bboxes[i, 4])
+        if rec_model is not None:
+            rec_model.get(frame, face)
+        if lmk_model is not None:
+            lmk_model.get(frame, face)
+        faces.append(face)
+
+    return faces
+
+
+def get_one_face(frame: Frame, faces: Any = None) -> Any:
+    if faces is None:
+        if _is_dml():
+            with modules.globals.dml_lock:
+                faces = _analyse_faces(frame)
+        else:
+            faces = _analyse_faces(frame)
+    try:
+        return min(faces, key=lambda x: x.bbox[0])
+    except ValueError:
+        return None
+
+
+def get_many_faces(frame: Frame) -> Any:
+    try:
+        if _is_dml():
+            with modules.globals.dml_lock:
+                return _analyse_faces(frame)
+        else:
+            return _analyse_faces(frame)
+    except IndexError:
+        return None
+
+def detect_one_face_fast(frame: Frame) -> Any:
+    """Detection-only — skips landmark and recognition models.
+
+    Returns a Face with bbox, kps, det_score (enough for face swap).
+    ~10ms vs ~16ms for full get_one_face() at 1080p.
+    """
+    from insightface.app.common import Face
+    fa = get_face_analyser()
+    bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric='default')
+    if bboxes.shape[0] == 0:
+        return None
+    idx = int(bboxes[:, 0].argmin())
+    return Face(bbox=bboxes[idx, :4], kps=kpss[idx], det_score=bboxes[idx, 4])
+
+
+def detect_many_faces_fast(frame: Frame) -> Any:
+    """Detection-only multi-face — skips landmark and recognition."""
+    from insightface.app.common import Face
+    fa = get_face_analyser()
+    bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric='default')
+    if bboxes.shape[0] == 0:
+        return None
+    return [Face(bbox=bboxes[i, :4], kps=kpss[i], det_score=bboxes[i, 4])
+            for i in range(bboxes.shape[0])]
+
+
+def ensure_landmarks(frame: Frame, faces: Any) -> None:
+    """Run the 2d106 landmark model in-place on faces that lack it.
+
+    The fast webcam path (detect_one_face_fast / detect_many_faces_fast)
+    produces detection-only Face objects with no ``landmark_2d_106``.
+    Mouth masking needs those landmarks, so add them on demand only when
+    the feature is active — keeping the fast path fast otherwise.
+    """
+    if faces is None:
+        return
+    if not isinstance(faces, (list, tuple)):
+        faces = [faces]
+
+    fa = get_face_analyser()
+    lmk_model = fa.models.get("landmark_2d_106")
+    if lmk_model is None:
+        return
+
+    for face in faces:
+        if face is None:
+            continue
+        # insightface Face is a dict; missing keys raise AttributeError,
+        # so getattr(..., None) is the safe presence check.
+        if getattr(face, "landmark_2d_106", None) is None:
+            try:
+                lmk_model.get(frame, face)
+            except Exception as e:  # pragma: no cover - never break the swap
+                print(f"Error computing 2d106 landmarks: {e}")
+
+
+def has_valid_map() -> bool:
+    for map in modules.globals.source_target_map:
+        if "source" in map and "target" in map:
+            return True
+    return False
+
+def default_source_face() -> Any:
+    for map in modules.globals.source_target_map:
+        if "source" in map:
+            return map['source']['face']
+    return None
+
+def simplify_maps() -> Any:
+    centroids = []
+    faces = []
+    for map in modules.globals.source_target_map:
+        if "source" in map and "target" in map:
+            centroids.append(map['target']['face'].normed_embedding)
+            faces.append(map['source']['face'])
+
+    modules.globals.simple_map = {'source_faces': faces, 'target_embeddings': centroids}
+    return None
+
+def add_blank_map() -> Any:
+    try:
+        max_id = -1
+        if len(modules.globals.source_target_map) > 0:
+            max_id = max(modules.globals.source_target_map, key=lambda x: x['id'])['id']
+
+        modules.globals.source_target_map.append({
+                'id' : max_id + 1
+                })
+    except ValueError:
+        return None
+    
+def get_unique_faces_from_target_image() -> Any:
+    try:
+        modules.globals.source_target_map = []
+        target_frame = imread_unicode(modules.globals.target_path)
+        many_faces = get_many_faces(target_frame)
+        if many_faces is None:
+            return None
+        i = 0
+
+        for face in many_faces:
+            x_min, y_min, x_max, y_max = face['bbox']
+            modules.globals.source_target_map.append({
+                'id' : i, 
+                'target' : {
+                            'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
+                            'face' : face
+                            }
+                })
+            i = i + 1
+    except ValueError:
+        return None
+    
+    
+def get_unique_faces_from_target_video() -> Any:
+    try:
+        modules.globals.source_target_map = []
+        frame_face_embeddings = []
+        face_embeddings = []
+    
+        print('Creating temp resources...')
+        clean_temp(modules.globals.target_path)
+        create_temp(modules.globals.target_path)
+        print('Extracting frames...')
+        extract_frames(modules.globals.target_path)
+
+        temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
+
+        i = 0
+        for temp_frame_path in tqdm(temp_frame_paths, desc="Extracting face embeddings from frames"):
+            temp_frame = imread_unicode(temp_frame_path)
+            many_faces = get_many_faces(temp_frame)
+            if many_faces is None:
+                continue
+
+            for face in many_faces:
+                face_embeddings.append(face.normed_embedding)
+            
+            frame_face_embeddings.append({'frame': i, 'faces': many_faces, 'location': temp_frame_path})
+            i += 1
+
+        centroids = find_cluster_centroids(face_embeddings)
+
+        for frame in frame_face_embeddings:
+            for face in frame['faces']:
+                closest_centroid_index, _ = find_closest_centroid(centroids, face.normed_embedding)
+                face['target_centroid'] = closest_centroid_index
+
+        for i in range(len(centroids)):
+            modules.globals.source_target_map.append({
+                'id' : i
+            })
+
+            temp = []
+            for frame in tqdm(frame_face_embeddings, desc=f"Mapping frame embeddings to centroids-{i}"):
+                temp.append({'frame': frame['frame'], 'faces': [face for face in frame['faces'] if face['target_centroid'] == i], 'location': frame['location']})
+
+            modules.globals.source_target_map[i]['target_faces_in_frame'] = temp
+
+        # dump_faces(centroids, frame_face_embeddings)
+        default_target_face()
+    except ValueError:
+        return None
+    
+
+def default_target_face():
+    for map in modules.globals.source_target_map:
+        best_face = None
+        best_frame = None
+        for frame in map['target_faces_in_frame']:
+            if len(frame['faces']) > 0:
+                best_face = frame['faces'][0]
+                best_frame = frame
+                break
+
+        if best_face is None:
+            continue  # No faces detected in this cluster — skip
+
+        for frame in map['target_faces_in_frame']:
+            for face in frame['faces']:
+                if face['det_score'] > best_face['det_score']:
+                    best_face = face
+                    best_frame = frame
+
+        x_min, y_min, x_max, y_max = best_face['bbox']
+
+        target_frame = imread_unicode(best_frame['location'])
+        map['target'] = {
+                        'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
+                        'face' : best_face
+                        }
+
+
+def dump_faces(centroids: Any, frame_face_embeddings: list):
+    temp_directory_path = get_temp_directory_path(modules.globals.target_path)
+
+    for i in range(len(centroids)):
+        if os.path.exists(temp_directory_path + f"/{i}") and os.path.isdir(temp_directory_path + f"/{i}"):
+            shutil.rmtree(temp_directory_path + f"/{i}")
+        Path(temp_directory_path + f"/{i}").mkdir(parents=True, exist_ok=True)
+
+        for frame in tqdm(frame_face_embeddings, desc=f"Copying faces to temp/./{i}"):
+            temp_frame = imread_unicode(frame['location'])
+
+            j = 0
+            for face in frame['faces']:
+                if face['target_centroid'] == i:
+                    x_min, y_min, x_max, y_max = face['bbox']
+
+                    if temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)].size > 0:
+                        imwrite_unicode(temp_directory_path + f"/{i}/{frame['frame']}_{j}.png", temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)])
+                j += 1

modules/gettext.py

@@ -0,0 +1,26 @@
+import json
+from pathlib import Path
+
+class LanguageManager:
+    def __init__(self, default_language="en"):
+        self.current_language = default_language
+        self.translations = {}
+        self.load_language(default_language)
+
+    def load_language(self, language_code) -> bool:
+        """load language file"""
+        if language_code == "en":
+            return True
+        try:
+            file_path = Path(__file__).parent.parent / f"locales/{language_code}.json"
+            with open(file_path, "r", encoding="utf-8") as file:
+                self.translations = json.load(file)
+            self.current_language = language_code
+            return True
+        except FileNotFoundError:
+            print(f"Language file not found: {language_code}")
+            return False
+
+    def _(self, key, default=None) -> str:
+        """get translate text"""
+        return self.translations.get(key, default if default else key)

modules/globals.py

@@ -1,35 +1,76 @@
+# --- START OF FILE globals.py ---
+
 import os
-from typing import List, Dict
+from typing import List, Dict, Any
 
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-WORKFLOW_DIR = os.path.join(ROOT_DIR, 'workflow')
+WORKFLOW_DIR = os.path.join(ROOT_DIR, "workflow")
 
 file_types = [
-    ('Image', ('*.png','*.jpg','*.jpeg','*.gif','*.bmp')),
-    ('Video', ('*.mp4','*.mkv'))
+    ("Image", ("*.png", "*.jpg", "*.jpeg", "*.gif", "*.bmp")),
+    ("Video", ("*.mp4", "*.mkv")),
 ]
 
-source_path = None
-target_path = None
-output_path = None
+# Face Mapping Data
+source_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing
+simple_map: Dict[str, Any] = {}             # Stores simplified map (embeddings/faces) for live/simple mode
+
+# Paths
+source_path: str | None = None
+target_path: str | None = None
+output_path: str | None = None
+
+# Processing Options
 frame_processors: List[str] = []
-keep_fps = None
-keep_audio = None
-keep_frames = None
-many_faces = None
-video_encoder = None
-video_quality = None
-live_mirror = None
-live_resizable = None
-max_memory = None
-execution_providers: List[str] = []
-execution_threads = None
-headless = None
-log_level = 'error'
-fp_ui: Dict[str, bool] = {}
-nsfw = None
-camera_input_combobox = None
-webcam_preview_running = False
-enhancer_upscale_factor = 1
-source_image_scaling_factor = 2
-sr_scale_factor = 4
+keep_fps: bool = True
+keep_audio: bool = True
+keep_frames: bool = False
+many_faces: bool = False         # Process all detected faces with default source
+map_faces: bool = False          # Use source_target_map or simple_map for specific swaps
+poisson_blend: bool = False      # Enable Poisson Blending for smoother face swaps
+color_correction: bool = False   # Enable color correction (implementation specific)
+nsfw_filter: bool = False
+
+# Video Output Options
+video_encoder: str | None = None
+video_quality: int | None = None # Typically a CRF value or bitrate
+
+# Live Mode Options
+live_mirror: bool = False
+live_resizable: bool = True
+camera_input_combobox: Any | None = None # Placeholder for UI element if needed
+webcam_preview_running: bool = False
+show_fps: bool = False
+
+# System Configuration
+max_memory: int | None = None        # Memory limit in GB? (Needs clarification)
+execution_providers: List[str] = []  # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
+execution_threads: int | None = None # Number of threads for CPU execution
+headless: bool | None = None         # Run without UI?
+log_level: str = "error"             # Logging level (e.g., 'debug', 'info', 'warning', 'error')
+
+# Face Processor UI Toggles (Example)
+fp_ui: Dict[str, bool] = {"face_enhancer": False, "face_enhancer_gpen256": False, "face_enhancer_gpen512": False}
+
+# Face Swapper Specific Options
+face_swapper_enabled: bool = True # General toggle for the swapper processor
+opacity: float = 1.0              # Blend factor for the swapped face (0.0-1.0)
+sharpness: float = 0.0            # Sharpness enhancement for swapped face (0.0-1.0+)
+
+# Mouth Mask Options
+mouth_mask: bool = False           # Enable mouth area masking/pasting
+show_mouth_mask_box: bool = False  # Visualize the mouth mask area (for debugging)
+mask_feather_ratio: int = 12       # Denominator for feathering calculation (higher = smaller feather)
+mask_down_size: float = 0.1        # Expansion factor for lower lip mask (relative)
+mask_size: float = 1.0             # Expansion factor for upper lip mask (relative)
+mouth_mask_size: float = 0.0       # Mouth mask size (0-100; 0=off, 100=mouth to chin)
+
+# --- START: Added for Frame Interpolation ---
+enable_interpolation: bool = True # Toggle temporal smoothing
+interpolation_weight: float = 0  # Blend weight for current frame (0.0-1.0). Lower=smoother.
+# --- END: Added for Frame Interpolation ---
+
+# --- END OF FILE globals.py ---
+
+import threading
+dml_lock = threading.Lock()

modules/gpu_processing.py

@@ -0,0 +1,285 @@
+# --- START OF FILE gpu_processing.py ---
+"""
+GPU-accelerated image processing using OpenCV CUDA (cv2.cuda.GpuMat).
+
+Provides drop-in replacements for common cv2 functions.  When OpenCV is built
+with CUDA support the functions transparently upload → process → download via
+GpuMat; otherwise they fall back to the regular CPU path so the rest of the
+codebase never has to care whether CUDA is available.
+
+Usage
+-----
+    from modules.gpu_processing import (
+        gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted,
+        gpu_resize, gpu_cvt_color, gpu_flip,
+        is_gpu_accelerated,
+    )
+"""
+
+from __future__ import annotations
+
+import os
+import cv2
+import numpy as np
+from typing import Tuple
+
+# ---------------------------------------------------------------------------
+# CUDA availability detection (evaluated once at import time)
+# ---------------------------------------------------------------------------
+CUDA_AVAILABLE: bool = False
+
+# OpenCV CUDA per-operation acceleration is DISABLED by default.
+# Each gpu_* call uploads to GPU, processes, then downloads back to CPU.
+# At webcam resolution (~960x540) this upload/download overhead far exceeds
+# the time saved on the actual operation, making it slower than pure CPU.
+# The heavy lifting (face detection, swap, enhancement) runs on GPU via
+# ONNX Runtime's CUDAExecutionProvider, which is where GPU matters.
+#
+# To force-enable, set OPENCV_CUDA_PROCESSING=1 in your environment.
+if os.environ.get("OPENCV_CUDA_PROCESSING") == "1":
+    try:
+        _test_mat = cv2.cuda.GpuMat()
+        _has_gauss = hasattr(cv2.cuda, "createGaussianFilter")
+        _has_resize = hasattr(cv2.cuda, "resize")
+        _has_cvt = hasattr(cv2.cuda, "cvtColor")
+        if _has_gauss and _has_resize and _has_cvt:
+            CUDA_AVAILABLE = True
+            print("[gpu_processing] OpenCV CUDA processing enabled via OPENCV_CUDA_PROCESSING=1.")
+    except Exception:
+        pass
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+def _ensure_uint8(img: np.ndarray) -> np.ndarray:
+    """Clip and convert to uint8 if necessary."""
+    if img.dtype != np.uint8:
+        return np.clip(img, 0, 255).astype(np.uint8)
+    return img
+
+
+def _ksize_odd(ksize: Tuple[int, int]) -> Tuple[int, int]:
+    """Ensure kernel dimensions are positive and odd (required by GaussianBlur)."""
+    kw = max(1, ksize[0] // 2 * 2 + 1) if ksize[0] > 0 else 0
+    kh = max(1, ksize[1] // 2 * 2 + 1) if ksize[1] > 0 else 0
+    return (kw, kh)
+
+
+def _cv_type_for(img: np.ndarray) -> int:
+    """Return the OpenCV type constant matching *img* (uint8 only)."""
+    channels = 1 if img.ndim == 2 else img.shape[2]
+    if channels == 1:
+        return cv2.CV_8UC1
+    elif channels == 3:
+        return cv2.CV_8UC3
+    elif channels == 4:
+        return cv2.CV_8UC4
+    return cv2.CV_8UC3  # fallback
+
+
+# ---------------------------------------------------------------------------
+# Public API – Gaussian Blur
+# ---------------------------------------------------------------------------
+
+def gpu_gaussian_blur(
+    src: np.ndarray,
+    ksize: Tuple[int, int],
+    sigma_x: float,
+    sigma_y: float = 0,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.GaussianBlur`` with CUDA acceleration.
+
+    Parameters match ``cv2.GaussianBlur(src, ksize, sigmaX, sigmaY)``.
+    When *ksize* is ``(0, 0)`` OpenCV computes the kernel size from *sigma_x*.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            cv_type = _cv_type_for(src_u8)
+            ks = _ksize_odd(ksize) if ksize != (0, 0) else ksize
+
+            gauss = cv2.cuda.createGaussianFilter(cv_type, cv_type, ks, sigma_x, sigma_y)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_dst = gauss.apply(gpu_src)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.GaussianBlur(src, ksize, sigma_x, sigmaY=sigma_y)
+
+
+# ---------------------------------------------------------------------------
+# Public API – addWeighted
+# ---------------------------------------------------------------------------
+
+def gpu_add_weighted(
+    src1: np.ndarray,
+    alpha: float,
+    src2: np.ndarray,
+    beta: float,
+    gamma: float,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.addWeighted`` with CUDA acceleration."""
+    if CUDA_AVAILABLE:
+        try:
+            s1 = _ensure_uint8(src1)
+            s2 = _ensure_uint8(src2)
+            g1 = cv2.cuda.GpuMat()
+            g2 = cv2.cuda.GpuMat()
+            g1.upload(s1)
+            g2.upload(s2)
+            gpu_dst = cv2.cuda.addWeighted(g1, alpha, g2, beta, gamma)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.addWeighted(src1, alpha, src2, beta, gamma)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Unsharp-mask sharpening
+# ---------------------------------------------------------------------------
+
+def gpu_sharpen(
+    src: np.ndarray,
+    strength: float,
+    sigma: float = 3,
+) -> np.ndarray:
+    """Unsharp-mask sharpening, optionally GPU-accelerated.
+
+    Equivalent to::
+
+        blurred = GaussianBlur(src, (0,0), sigma)
+        result  = addWeighted(src, 1+strength, blurred, -strength, 0)
+    """
+    if strength <= 0:
+        return src
+
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            cv_type = _cv_type_for(src_u8)
+
+            gauss = cv2.cuda.createGaussianFilter(cv_type, cv_type, (0, 0), sigma)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_blurred = gauss.apply(gpu_src)
+            gpu_sharp = cv2.cuda.addWeighted(gpu_src, 1.0 + strength, gpu_blurred, -strength, 0)
+            result = gpu_sharp.download()
+            return np.clip(result, 0, 255).astype(np.uint8)
+        except cv2.error:
+            pass
+
+    blurred = cv2.GaussianBlur(src, (0, 0), sigma)
+    sharpened = cv2.addWeighted(src, 1.0 + strength, blurred, -strength, 0)
+    return np.clip(sharpened, 0, 255).astype(np.uint8)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Resize
+# ---------------------------------------------------------------------------
+
+# Map common cv2 interpolation flags to their CUDA equivalents
+_INTERP_MAP = {
+    cv2.INTER_NEAREST: cv2.INTER_NEAREST,
+    cv2.INTER_LINEAR: cv2.INTER_LINEAR,
+    cv2.INTER_CUBIC: cv2.INTER_CUBIC,
+    cv2.INTER_AREA: cv2.INTER_AREA,
+    cv2.INTER_LANCZOS4: cv2.INTER_LANCZOS4,
+}
+
+
+def gpu_resize(
+    src: np.ndarray,
+    dsize: Tuple[int, int],
+    fx: float = 0,
+    fy: float = 0,
+    interpolation: int = cv2.INTER_LINEAR,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.resize`` with CUDA acceleration.
+
+    Parameters match ``cv2.resize(src, dsize, fx=fx, fy=fy, interpolation=...)``.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+
+            interp = _INTERP_MAP.get(interpolation, cv2.INTER_LINEAR)
+
+            if dsize and dsize[0] > 0 and dsize[1] > 0:
+                gpu_dst = cv2.cuda.resize(gpu_src, dsize, interpolation=interp)
+            else:
+                gpu_dst = cv2.cuda.resize(gpu_src, (0, 0), fx=fx, fy=fy, interpolation=interp)
+
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.resize(src, dsize, fx=fx, fy=fy, interpolation=interpolation)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Color conversion
+# ---------------------------------------------------------------------------
+
+def gpu_cvt_color(
+    src: np.ndarray,
+    code: int,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.cvtColor`` with CUDA acceleration.
+
+    Parameters match ``cv2.cvtColor(src, code)``.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_dst = cv2.cuda.cvtColor(gpu_src, code)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.cvtColor(src, code)
+
+
+# ---------------------------------------------------------------------------
+# Public API – Flip
+# ---------------------------------------------------------------------------
+
+def gpu_flip(
+    src: np.ndarray,
+    flip_code: int,
+) -> np.ndarray:
+    """Drop-in replacement for ``cv2.flip`` with CUDA acceleration.
+
+    Parameters match ``cv2.flip(src, flipCode)``.
+    *flip_code*: 0 = vertical, 1 = horizontal, -1 = both.
+    """
+    if CUDA_AVAILABLE:
+        try:
+            src_u8 = _ensure_uint8(src)
+            gpu_src = cv2.cuda.GpuMat()
+            gpu_src.upload(src_u8)
+            gpu_dst = cv2.cuda.flip(gpu_src, flip_code)
+            return gpu_dst.download()
+        except cv2.error:
+            pass
+
+    return cv2.flip(src, flip_code)
+
+
+# ---------------------------------------------------------------------------
+# Convenience: check at runtime whether GPU path is active
+# ---------------------------------------------------------------------------
+
+def is_gpu_accelerated() -> bool:
+    """Return ``True`` when the CUDA path will be used."""
+    return CUDA_AVAILABLE
+
+# --- END OF FILE gpu_processing.py ---

modules/metadata.py

@@ -1,3 +1,3 @@
-name = 'Deep Live Cam'
-version = '1.3.0'
-edition = 'Portable'
+name = 'Deep-Live-Cam'
+version = '2.1.5'
+edition = 'GitHub Edition'

modules/onnx_optimize.py

@@ -0,0 +1,550 @@
+"""ONNX model optimizations for CoreML execution on Apple Silicon.
+
+Each pass eliminates a different CPU↔ANE round-trip that ORT's CoreML EP
+would otherwise introduce:
+
+1. **Shape/Gather constant folding** — Dynamic ``Shape`` → ``Gather`` chains
+   (e.g. for FPN upsample target sizes in RetinaFace) force ops onto CPU even
+   when the input dimensions are known at load time.  We run ONNX shape
+   inference with the known input size and replace these chains with constants.
+   Float32-noise-level differences only (max ~6e-6).
+
+2. **Pad(reflect) decomposition** — CoreML doesn't support ``Pad(mode=reflect)``.
+   Models using reflect padding (e.g. inswapper_128) get split into many CoreML
+   subgraphs with CPU fallbacks between each.  We rewrite each ``Pad(reflect)``
+   as equivalent ``Slice`` + ``Concat`` ops that CoreML handles natively.
+   Bit-for-bit identical output. (Fixed upstream in microsoft/onnxruntime#28073.)
+
+3. **Split → Slice decomposition** — CoreML's EP doesn't support the ONNX
+   ``Split`` op, causing partition boundaries in models with channel-wise
+   splits (e.g. GFPGAN's SFT modulation). Each 2-way Split becomes two Slices.
+
+4. **Scalar Gather widening** — ORT's CoreML EP rejects ``Gather`` nodes with
+   rank-0 (scalar) indices. StyleGAN-derived models (GFPGAN) slice per-layer
+   style codes using exactly this pattern. We widen each scalar index to
+   ``[1]`` and squeeze the added axis on the Gather output.
+   (Filed upstream as microsoft/onnxruntime#28180.)
+
+All passes are cached on disk with a ``_coreml`` suffix so the rewrite cost
+is paid only once per model.
+"""
+
+import os
+import platform
+
+import numpy as np
+
+IS_APPLE_SILICON = platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def optimize_for_coreml(model_path: str, input_shape: tuple = None) -> str:
+    """Return path to a CoreML-optimized ONNX model.
+
+    Applies all applicable optimizations and caches the result next to
+    the original model (with ``_coreml`` suffix).
+
+    Args:
+        model_path: Path to the original ONNX model.
+        input_shape: Optional fixed input shape (e.g. ``(1, 3, 640, 640)``).
+            When provided, enables Shape/Gather constant folding.
+
+    Returns the optimized path, or the original path if no optimizations
+    apply or we're not on Apple Silicon.
+    """
+    if not IS_APPLE_SILICON:
+        return model_path
+
+    base, ext = os.path.splitext(model_path)
+    optimized_path = f"{base}_coreml{ext}"
+    if os.path.exists(optimized_path):
+        if os.path.getmtime(optimized_path) >= os.path.getmtime(model_path):
+            return optimized_path
+
+    import onnx
+    from onnx import numpy_helper
+
+    model = onnx.load(model_path)
+    changed = False
+
+    if _fold_shape_gather(model, input_shape):
+        changed = True
+
+    # TODO(ort>=1.26): drop this pass. Fixed upstream by microsoft/onnxruntime#28073.
+    if _decompose_reflect_pad(model):
+        changed = True
+
+    if _decompose_split(model):
+        changed = True
+
+    # TODO: drop this pass once microsoft/onnxruntime#28180 ships. The CoreML
+    # Gather op builder rejects rank-0 (scalar) indices; we widen them to [1]
+    # + Squeeze so StyleGAN-family models (GFPGAN) stay on ANE.
+    if _rewrite_scalar_gather(model):
+        changed = True
+
+    if not changed:
+        return model_path
+
+    # Preserve insightface's emap convention: the INSwapper class reads
+    # graph.initializer[-1] as the embedding map.  If the original model
+    # had a (512, 512) matrix as its last initializer, keep it last.
+    _preserve_emap_position(model, numpy_helper)
+
+    onnx.save(model, optimized_path)
+    return optimized_path
+
+
+# ---------------------------------------------------------------------------
+# Pass 1: Fold Shape → Gather chains into constants
+# ---------------------------------------------------------------------------
+
+def _fold_shape_gather(model, input_shape) -> bool:
+    """Replace dynamic Shape→Gather chains with constants when input size is known.
+
+    Only removes a Shape node when ALL of its consumers are Gather nodes
+    that are also being folded.  This prevents breaking graphs where
+    a Shape output feeds into other ops as well.
+    """
+    if input_shape is None:
+        return False
+
+    from onnx import numpy_helper, shape_inference
+
+    graph = model.graph
+
+    # Set fixed input dimensions for shape inference
+    inp = graph.input[0]
+    dims = inp.type.tensor_type.shape.dim
+    for i, size in enumerate(input_shape):
+        if i < len(dims):
+            dims[i].dim_value = size
+
+    try:
+        model_inferred = shape_inference.infer_shapes(model)
+    except Exception:
+        return False
+
+    # Extract inferred shapes
+    value_shapes = {}
+    for vi in list(model_inferred.graph.value_info) + list(graph.input) + list(graph.output):
+        shape_dims = vi.type.tensor_type.shape.dim
+        shape = []
+        for d in shape_dims:
+            if d.dim_value > 0:
+                shape.append(d.dim_value)
+            else:
+                shape.append(None)
+        value_shapes[vi.name] = shape
+
+    inits = {init.name: numpy_helper.to_array(init) for init in graph.initializer}
+
+    # Build consumer map: output_name → list of consuming nodes
+    consumers = {}
+    for node in graph.node:
+        for i in node.input:
+            consumers.setdefault(i, []).append(node)
+
+    # Also check graph outputs — an output name consumed by the graph
+    # output list must not be removed
+    graph_output_names = {o.name for o in graph.output}
+
+    # Find Shape nodes with fully-known output
+    shape_constants = {}
+    for node in graph.node:
+        if node.op_type == "Shape":
+            inp_shape = value_shapes.get(node.input[0])
+            if inp_shape and all(isinstance(d, int) for d in inp_shape):
+                shape_constants[node.output[0]] = np.array(inp_shape, dtype=np.int64)
+
+    if not shape_constants:
+        return False
+
+    # Find Gather nodes consuming Shape constants
+    gather_constants = {}
+    for node in graph.node:
+        if node.op_type == "Gather" and node.input[0] in shape_constants:
+            idx_name = node.input[1]
+            if idx_name in inits:
+                idx = int(inits[idx_name])
+                val = int(shape_constants[node.input[0]][idx])
+                gather_constants[node.output[0]] = np.array(val, dtype=np.int64)
+
+    if not gather_constants:
+        return False
+
+    # Determine which Gather nodes to fold (always safe — we replace
+    # the output with a constant initializer)
+    gather_remove_ids = set()
+    for node in graph.node:
+        if node.op_type == "Gather" and node.output[0] in gather_constants:
+            gather_remove_ids.add(id(node))
+
+    # Determine which Shape nodes are safe to remove: only if ALL
+    # consumers of the Shape output are Gather nodes being folded,
+    # and the output isn't a graph output.
+    shape_remove_ids = set()
+    for node in graph.node:
+        if node.op_type == "Shape" and node.output[0] in shape_constants:
+            out_name = node.output[0]
+            if out_name in graph_output_names:
+                continue
+            node_consumers = consumers.get(out_name, [])
+            if all(id(c) in gather_remove_ids for c in node_consumers):
+                shape_remove_ids.add(id(node))
+
+    remove_ids = gather_remove_ids | shape_remove_ids
+
+    # Add Gather output constants as initializers
+    existing = {i.name for i in graph.initializer}
+    for name, val in gather_constants.items():
+        if name not in existing:
+            graph.initializer.append(numpy_helper.from_array(val, name=name))
+
+    new_nodes = [n for n in graph.node if id(n) not in remove_ids]
+    del graph.node[:]
+    graph.node.extend(new_nodes)
+    return True
+
+
+# ---------------------------------------------------------------------------
+# Pass 2: Decompose Pad(reflect) → Slice + Concat
+#
+# TEMPORARY: fixed upstream in microsoft/onnxruntime#28073 (merged 2026-04-20).
+# Once the ORT floor is >= 1.26.0, MLProgram handles Pad(mode=reflect) natively
+# via MIL tensor_operation.pad and this entire pass can be deleted.
+# ---------------------------------------------------------------------------
+
+def _decompose_reflect_pad(model) -> bool:
+    """Rewrite Pad(reflect) as Slice+Concat sequences CoreML can handle."""
+    from onnx import numpy_helper, helper
+
+    graph = model.graph
+    inits = {init.name: numpy_helper.to_array(init) for init in graph.initializer}
+
+    reflect_pads = []
+    for node in graph.node:
+        if node.op_type == "Pad":
+            mode = "constant"
+            for attr in node.attribute:
+                if attr.name == "mode":
+                    mode = attr.s.decode()
+            if mode == "reflect" and len(node.input) > 1 and node.input[1] in inits:
+                reflect_pads.append(node)
+
+    if not reflect_pads:
+        return False
+
+    existing_names = {i.name for i in graph.initializer}
+
+    def ensure_const(name, value):
+        if name not in existing_names:
+            graph.initializer.append(
+                numpy_helper.from_array(np.array(value, dtype=np.int64), name=name)
+            )
+            existing_names.add(name)
+
+    ensure_const("_rp_ax2", [2])
+    ensure_const("_rp_ax3", [3])
+
+    max_pad = 0
+    for node in reflect_pads:
+        pads = inits[node.input[1]].tolist()
+        max_pad = max(max_pad, int(pads[2]), int(pads[3]))
+
+    for v in range(1, max_pad + 2):
+        ensure_const(f"_rp_p{v}", [v])
+        ensure_const(f"_rp_n{v}", [-v])
+
+    _counter = [0]
+
+    def uid():
+        _counter[0] += 1
+        return _counter[0]
+
+    pad_ids = {id(n) for n in reflect_pads}
+    pad_init_names = set()
+
+    new_nodes = []
+    for node in graph.node:
+        if id(node) not in pad_ids:
+            new_nodes.append(node)
+            continue
+
+        pads = inits[node.input[1]].tolist()
+        h_pad, w_pad = int(pads[2]), int(pads[3])
+
+        for inp in node.input[1:]:
+            if inp in inits:
+                pad_init_names.add(inp)
+
+        current = node.input[0]
+
+        if h_pad > 0:
+            top = []
+            for i in range(h_pad, 0, -1):
+                name = f"_rp_t{uid()}"
+                new_nodes.append(helper.make_node(
+                    "Slice",
+                    inputs=[current, f"_rp_p{i}", f"_rp_p{i+1}", "_rp_ax2"],
+                    outputs=[name],
+                ))
+                top.append(name)
+
+            bot = []
+            for i in range(1, h_pad + 1):
+                name = f"_rp_b{uid()}"
+                new_nodes.append(helper.make_node(
+                    "Slice",
+                    inputs=[current, f"_rp_n{i+1}", f"_rp_n{i}", "_rp_ax2"],
+                    outputs=[name],
+                ))
+                bot.append(name)
+
+            h_out = f"_rp_h{uid()}"
+            new_nodes.append(helper.make_node(
+                "Concat", inputs=top + [current] + bot, outputs=[h_out], axis=2
+            ))
+            current = h_out
+
+        if w_pad > 0:
+            left = []
+            for i in range(w_pad, 0, -1):
+                name = f"_rp_l{uid()}"
+                new_nodes.append(helper.make_node(
+                    "Slice",
+                    inputs=[current, f"_rp_p{i}", f"_rp_p{i+1}", "_rp_ax3"],
+                    outputs=[name],
+                ))
+                left.append(name)
+
+            right = []
+            for i in range(1, w_pad + 1):
+                name = f"_rp_r{uid()}"
+                new_nodes.append(helper.make_node(
+                    "Slice",
+                    inputs=[current, f"_rp_n{i+1}", f"_rp_n{i}", "_rp_ax3"],
+                    outputs=[name],
+                ))
+                right.append(name)
+
+            new_nodes.append(helper.make_node(
+                "Concat",
+                inputs=left + [current] + right,
+                outputs=[node.output[0]],
+                axis=3,
+            ))
+        elif h_pad > 0:
+            new_nodes.append(helper.make_node(
+                "Identity", inputs=[current], outputs=[node.output[0]]
+            ))
+
+    # Remove old Pad initializers
+    clean_inits = [i for i in graph.initializer if i.name not in pad_init_names]
+    del graph.initializer[:]
+    graph.initializer.extend(clean_inits)
+
+    del graph.node[:]
+    graph.node.extend(new_nodes)
+    return True
+
+
+# ---------------------------------------------------------------------------
+# Pass 3: Decompose Split → Slice pairs
+# ---------------------------------------------------------------------------
+
+def _decompose_split(model) -> bool:
+    """Rewrite Split(axis=1) as Slice pairs that CoreML can handle.
+
+    CoreML's EP doesn't support the ONNX ``Split`` op, causing partition
+    boundaries in models that use channel-wise splits (e.g. GFPGAN's SFT
+    modulation layers).  Each Split with two outputs becomes two Slice ops.
+    """
+    from onnx import numpy_helper, helper
+
+    graph = model.graph
+
+    splits = []
+    for node in graph.node:
+        if node.op_type == "Split":
+            axis = 0
+            split_sizes = []
+            for attr in node.attribute:
+                if attr.name == "axis":
+                    axis = attr.i
+                if attr.name == "split":
+                    split_sizes = list(attr.ints)
+            if axis == 1 and len(split_sizes) == 2 and len(node.output) == 2:
+                splits.append((node, split_sizes))
+
+    if not splits:
+        return False
+
+    existing = {i.name for i in graph.initializer}
+
+    def ensure_const(name, value):
+        if name not in existing:
+            graph.initializer.append(
+                numpy_helper.from_array(np.array(value, dtype=np.int64), name=name)
+            )
+            existing.add(name)
+
+    ensure_const("_sp_ax1", [1])
+
+    # Collect all needed boundary constants
+    for _, (a, b) in splits:
+        ensure_const("_sp_s0", [0])
+        ensure_const(f"_sp_s{a}", [a])
+        ensure_const(f"_sp_s{a + b}", [a + b])
+
+    split_ids = {id(node) for node, _ in splits}
+    replacements = {}
+    for node, (a, b) in splits:
+        slice0 = helper.make_node(
+            "Slice",
+            inputs=[node.input[0], "_sp_s0", f"_sp_s{a}", "_sp_ax1"],
+            outputs=[node.output[0]],
+        )
+        slice1 = helper.make_node(
+            "Slice",
+            inputs=[node.input[0], f"_sp_s{a}", f"_sp_s{a + b}", "_sp_ax1"],
+            outputs=[node.output[1]],
+        )
+        replacements[id(node)] = [slice0, slice1]
+
+    new_nodes = []
+    for node in graph.node:
+        if id(node) in split_ids:
+            new_nodes.extend(replacements[id(node)])
+        else:
+            new_nodes.append(node)
+
+    del graph.node[:]
+    graph.node.extend(new_nodes)
+    return True
+
+
+# ---------------------------------------------------------------------------
+# Pass 4: Widen scalar Gather indices to [1] + Squeeze
+#
+# TEMPORARY: filed upstream as microsoft/onnxruntime#28180. ORT's CoreML EP
+# GatherOpBuilder::IsOpSupportedImpl rejects rank-0 (scalar) indices with
+# `Gather does not support scalar 'indices'`. The builder's own comment
+# describes the workaround (promote to [1], squeeze the added axis) but
+# doesn't apply it. We do the same thing at the ONNX level so StyleGAN-
+# family models (GFPGAN is the hot example — 16 per-layer style-code
+# slices) don't split the CoreML subgraph. Once the upstream fix ships
+# and the ORT floor is raised, delete this pass.
+# ---------------------------------------------------------------------------
+
+def _rewrite_scalar_gather(model) -> bool:
+    """Rewrite Gather(data, scalar_idx) as Gather(data, [scalar_idx]) + Squeeze.
+
+    Only touches Gather nodes whose index is a rank-0 int64 constant or
+    initializer; everything else passes through unchanged. The rewrite
+    is semantically identical — indices get an added leading axis, the
+    Squeeze removes it after the gather.
+    """
+    from onnx import numpy_helper, helper, TensorProto
+
+    graph = model.graph
+
+    # Opset 13 moved Squeeze's axes from attribute to input.
+    opset = next(
+        (o.version for o in model.opset_import if o.domain in ("", "ai.onnx")),
+        11,
+    )
+
+    const_values = {}
+    for n in graph.node:
+        if n.op_type == "Constant":
+            for a in n.attribute:
+                if a.name == "value":
+                    const_values[n.output[0]] = a.t
+    init_values = {i.name: i for i in graph.initializer}
+
+    def scalar_int64(name):
+        """Return int value if `name` resolves to a rank-0 int64 constant, else None."""
+        tensor = const_values.get(name) or init_values.get(name)
+        if tensor is None or tensor.data_type != TensorProto.INT64:
+            return None
+        arr = numpy_helper.to_array(tensor)
+        return int(arr) if arr.ndim == 0 else None
+
+    rewrote = 0
+    new_nodes = []
+    for n in graph.node:
+        if n.op_type == "Gather":
+            val = scalar_int64(n.input[1])
+            if val is not None:
+                axis = next((a.i for a in n.attribute if a.name == "axis"), 0)
+                idx_1d_name = f"{n.input[1]}_1d_{rewrote}"
+                idx_const = helper.make_node(
+                    "Constant",
+                    inputs=[],
+                    outputs=[idx_1d_name],
+                    value=helper.make_tensor(idx_1d_name, TensorProto.INT64, [1], [val]),
+                )
+                gather_out = f"{n.output[0]}_pre_squeeze_{rewrote}"
+                new_gather = helper.make_node(
+                    "Gather",
+                    inputs=[n.input[0], idx_1d_name],
+                    outputs=[gather_out],
+                    name=n.name,
+                    axis=axis,
+                )
+                if opset < 13:
+                    squeeze = helper.make_node(
+                        "Squeeze",
+                        inputs=[gather_out],
+                        outputs=[n.output[0]],
+                        name=(n.name or "gather") + "_squeeze",
+                        axes=[axis],
+                    )
+                    new_nodes.extend([idx_const, new_gather, squeeze])
+                else:
+                    axes_name = f"{idx_1d_name}_sq_axes"
+                    axes_const = helper.make_node(
+                        "Constant",
+                        inputs=[],
+                        outputs=[axes_name],
+                        value=helper.make_tensor(axes_name, TensorProto.INT64, [1], [axis]),
+                    )
+                    squeeze = helper.make_node(
+                        "Squeeze",
+                        inputs=[gather_out, axes_name],
+                        outputs=[n.output[0]],
+                        name=(n.name or "gather") + "_squeeze",
+                    )
+                    new_nodes.extend([idx_const, axes_const, new_gather, squeeze])
+                rewrote += 1
+                continue
+        new_nodes.append(n)
+
+    if rewrote == 0:
+        return False
+
+    del graph.node[:]
+    graph.node.extend(new_nodes)
+    return True
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _preserve_emap_position(model, numpy_helper):
+    """Keep the insightface emap (512×512 matrix) as the last initializer."""
+    graph = model.graph
+    emap_init = None
+    for init in graph.initializer:
+        if not init.name.startswith("_rp_"):
+            arr = numpy_helper.to_array(init)
+            if len(arr.shape) == 2 and arr.shape[0] == 512 and arr.shape[1] == 512:
+                emap_init = init
+                break
+
+    if emap_init is not None:
+        inits = [i for i in graph.initializer if i.name != emap_init.name]
+        del graph.initializer[:]
+        graph.initializer.extend(inits)
+        graph.initializer.append(emap_init)

modules/paths.py

@@ -0,0 +1,6 @@
+"""Shared path constants for the Deep-Live-Cam project."""
+
+import os
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MODELS_DIR = os.path.join(ROOT_DIR, "models")

modules/platform_info.py

@@ -0,0 +1,91 @@
+"""Centralized platform + accelerator detection.
+
+Imported once at startup to expose typed flags the rest of the codebase
+can branch on without re-querying `platform`, `torch.cuda`, or
+`onnxruntime.get_available_providers()` repeatedly.
+
+The banner printed by :func:`print_banner` is the single user-facing
+report of which code path the app will take.
+"""
+from __future__ import annotations
+
+import platform as _platform
+import sys
+from typing import List, Tuple
+
+IS_WINDOWS: bool = _platform.system() == "Windows"
+IS_MACOS: bool = _platform.system() == "Darwin"
+IS_LINUX: bool = _platform.system() == "Linux"
+IS_APPLE_SILICON: bool = IS_MACOS and _platform.machine() == "arm64"
+
+
+def _detect_torch_cuda() -> bool:
+    try:
+        import torch  # noqa: WPS433 — local import, avoid hard dep at module load
+        return bool(torch.cuda.is_available())
+    except Exception:
+        return False
+
+
+def _detect_onnx_providers() -> List[str]:
+    try:
+        import onnxruntime
+        return list(onnxruntime.get_available_providers())
+    except Exception:
+        return []
+
+
+HAS_TORCH_CUDA: bool = _detect_torch_cuda()
+ONNX_PROVIDERS: List[str] = _detect_onnx_providers()
+HAS_CUDA_PROVIDER: bool = "CUDAExecutionProvider" in ONNX_PROVIDERS
+HAS_COREML_PROVIDER: bool = "CoreMLExecutionProvider" in ONNX_PROVIDERS
+HAS_DML_PROVIDER: bool = "DmlExecutionProvider" in ONNX_PROVIDERS
+HAS_OPENVINO_PROVIDER: bool = "OpenVINOExecutionProvider" in ONNX_PROVIDERS
+
+# OpenVINO execution-provider config shared by every ONNX session builder.
+# AUTO:GPU,NPU,CPU lets OpenVINO pick the best available device in priority
+# order (Intel GPU → NPU → CPU).
+OPENVINO_PROVIDER_CONFIG = (
+    "OpenVINOExecutionProvider",
+    {"device_type": "AUTO:GPU,NPU,CPU"},
+)
+
+
+def camera_backends() -> List[Tuple[int, int]]:
+    """Return an ordered list of ``(device_index, cv2_backend)`` attempts.
+
+    Windows prefers MSMF (60fps capable) with DirectShow as fallback.
+    macOS/Linux use the default backend (AVFoundation / V4L2).
+    """
+    import cv2
+    if IS_WINDOWS:
+        return [
+            (0, cv2.CAP_MSMF),
+            (0, cv2.CAP_DSHOW),
+            (0, cv2.CAP_ANY),
+        ]
+    return [(0, cv2.CAP_ANY)]
+
+
+def accelerator_label() -> str:
+    if HAS_CUDA_PROVIDER:
+        return "CUDA (NVIDIA)"
+    if IS_APPLE_SILICON and HAS_COREML_PROVIDER:
+        return "CoreML (Apple Neural Engine)"
+    if HAS_COREML_PROVIDER:
+        return "CoreML"
+    if HAS_OPENVINO_PROVIDER:
+        return "OpenVINO (Intel)"
+    if HAS_DML_PROVIDER:
+        return "DirectML"
+    return "CPU"
+
+
+def print_banner() -> None:
+    """Print a one-line summary of the platform + accelerator selection."""
+    os_label = f"{_platform.system()} {_platform.machine()}"
+    print(
+        f"[platform] {os_label} | python {sys.version.split()[0]} | "
+        f"accelerator: {accelerator_label()} | providers: {ONNX_PROVIDERS}",
+        flush=True,
+    )

modules/predicter.py

@@ -1,6 +1,23 @@
-import numpy as np
+import importlib.util
+import os
+
+import numpy
+
+# Keras 3 defaults to the TensorFlow backend, which has no Python 3.14 wheels.
+# opennsfw2 only runs inference, so any installed backend works; pick one that
+# is actually present before opennsfw2 imports keras.
+if "KERAS_BACKEND" not in os.environ:
+    for _backend in ("torch", "tensorflow", "jax"):
+        if importlib.util.find_spec(_backend) is not None:
+            os.environ["KERAS_BACKEND"] = _backend
+            break
+
 import opennsfw2
 from PIL import Image
+import cv2  # Add OpenCV import
+import modules.globals  # Import globals to access the color correction toggle
+from modules.gpu_processing import gpu_cvt_color
+
 from modules.typing import Frame
 
 MAX_PROBABILITY = 0.85
@@ -9,17 +26,24 @@ MAX_PROBABILITY = 0.85
 model = None
 
 def predict_frame(target_frame: Frame) -> bool:
-    global model
-    if model is None: model = opennsfw2.make_open_nsfw_model()
+    # Convert the frame to RGB before processing if color correction is enabled
+    if modules.globals.color_correction:
+        target_frame = gpu_cvt_color(target_frame, cv2.COLOR_BGR2RGB)
+        
     image = Image.fromarray(target_frame)
     image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
-    views = np.expand_dims(image, axis=0)
+    global model
+    if model is None: 
+        model = opennsfw2.make_open_nsfw_model()
+        
+    views = numpy.expand_dims(image, axis=0)
     _, probability = model.predict(views)[0]
     return probability > MAX_PROBABILITY
 
+
 def predict_image(target_path: str) -> bool:
-    probability = opennsfw2.predict_image(target_path)
-    return probability > MAX_PROBABILITY
+    return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
+
 
 def predict_video(target_path: str) -> bool:
     _, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100)

modules/processors/frame/_onnx_enhancer.py

@@ -0,0 +1,244 @@
+"""Shared ONNX-based face enhancement utilities for GPEN-BFR models.
+
+Provides session creation, pre/post processing, and the core
+enhance-face-via-ONNX pipeline.
+"""
+
+import os
+import platform
+import threading
+from typing import Any
+
+import cv2
+import numpy as np
+import onnxruntime
+
+import modules.globals
+from modules.platform_info import OPENVINO_PROVIDER_CONFIG
+
+IS_APPLE_SILICON = platform.system() == "Darwin" and platform.machine() == "arm64"
+
+# Limit concurrent ONNX calls to avoid VRAM exhaustion on multi-face frames
+THREAD_SEMAPHORE = threading.Semaphore(min(max(1, (os.cpu_count() or 1)), 8))
+
+
+def build_provider_config(providers=None):
+    """Wrap raw provider name strings with optimised CUDA / CoreML options.
+
+    Providers that are already ``(name, options_dict)`` tuples are passed
+    through unchanged.  Non-CUDA providers are left as bare strings.
+    """
+    if providers is None:
+        providers = modules.globals.execution_providers
+
+    config = []
+    for p in providers:
+        if isinstance(p, tuple):
+            # Already configured – pass through
+            config.append(p)
+        elif p == "CUDAExecutionProvider":
+            # Use bare provider — ONNX Runtime's defaults are fastest on
+            # modern GPUs (Blackwell/sm_120).  Custom options like
+            # EXHAUSTIVE cudnn_conv_algo_search hurt performance on these
+            # architectures.
+            config.append(p)
+        elif p == "CoreMLExecutionProvider" and IS_APPLE_SILICON:
+            config.append((
+                "CoreMLExecutionProvider",
+                {
+                    "ModelFormat": "MLProgram",
+                    "MLComputeUnits": "ALL",
+                    "AllowLowPrecisionAccumulationOnGPU": 1,
+                },
+            ))
+        elif p == "OpenVINOExecutionProvider":
+            # AUTO lets OpenVINO select the best device
+            config.append(OPENVINO_PROVIDER_CONFIG)
+        else:
+            config.append(p)
+    return config
+
+
+def run_inference(session: onnxruntime.InferenceSession,
+                  input_name: str,
+                  input_tensor: "np.ndarray") -> "np.ndarray":
+    """Run ONNX inference, using IO binding when a CUDA session is active.
+
+    IO binding avoids redundant host↔device copies by transferring the
+    input tensor directly to GPU memory and letting ONNX Runtime allocate
+    the output on the device.  Falls back to the standard ``session.run``
+    path for non-CUDA providers or if binding fails.
+    """
+    if "CUDAExecutionProvider" in session.get_providers():
+        try:
+            io_binding = session.io_binding()
+
+            # Input: numpy → GPU
+            ort_input = onnxruntime.OrtValue.ortvalue_from_numpy(
+                input_tensor, "cuda", 0,
+            )
+            io_binding.bind_ortvalue_input(input_name, ort_input)
+
+            # Output: allocate on GPU (avoids a CPU-side allocation)
+            output_name = session.get_outputs()[0].name
+            io_binding.bind_output(output_name, "cuda", 0)
+
+            session.run_with_iobinding(io_binding)
+
+            return io_binding.get_outputs()[0].numpy()
+        except Exception:
+            # Fall back to standard path (e.g. ORT version mismatch,
+            # unsupported op, or VRAM pressure)
+            pass
+
+    return session.run(None, {input_name: input_tensor})[0]
+
+
+def create_onnx_session(model_path: str) -> onnxruntime.InferenceSession:
+    """Create an ONNX Runtime session with optimised provider config.
+
+    On Apple Silicon, applies CoreML graph optimizations (Pad decomposition,
+    Shape/Gather folding, Split decomposition) to reduce CPU↔ANE partition
+    boundaries.
+    """
+    if IS_APPLE_SILICON:
+        from modules.onnx_optimize import optimize_for_coreml
+        # Infer input shape from the model for Shape/Gather folding
+        try:
+            import onnx
+            m = onnx.load(model_path)
+            inp = m.graph.input[0]
+            dims = inp.type.tensor_type.shape.dim
+            shape = tuple(d.dim_value for d in dims if d.dim_value > 0)
+            input_shape = shape if len(shape) == 4 else None
+        except Exception:
+            input_shape = None
+        model_path = optimize_for_coreml(model_path, input_shape=input_shape)
+
+    providers = build_provider_config()
+    session_options = onnxruntime.SessionOptions()
+    session_options.graph_optimization_level = (
+        onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+    )
+    session = onnxruntime.InferenceSession(
+        model_path, sess_options=session_options, providers=providers,
+    )
+    return session
+
+
+def warmup_session(session: onnxruntime.InferenceSession) -> None:
+    """Run a dummy inference pass to trigger JIT / compile caching."""
+    try:
+        input_feed = {
+            inp.name: np.zeros(
+                [d if isinstance(d, int) and d > 0 else 1 for d in inp.shape],
+                dtype=np.float32,
+            )
+            for inp in session.get_inputs()
+        }
+        session.run(None, input_feed)
+    except Exception as e:
+        print(f"ONNX enhancer warmup skipped (non-fatal): {e}")
+
+
+def preprocess_face(face_img: np.ndarray, input_size: int) -> np.ndarray:
+    """Resize, normalize, and convert a BGR face crop to ONNX input blob.
+
+    GPEN-BFR expects [1, 3, H, W] float32 in RGB, normalized to [-1, 1].
+    """
+    resized = cv2.resize(face_img, (input_size, input_size), interpolation=cv2.INTER_LINEAR)
+    rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
+    blob = rgb.astype(np.float32) / 255.0 * 2.0 - 1.0
+    blob = np.transpose(blob, (2, 0, 1))[np.newaxis, ...]
+    return blob
+
+
+def postprocess_face(output: np.ndarray) -> np.ndarray:
+    """Convert ONNX output [1, 3, H, W] float32 back to BGR uint8 image."""
+    img = output[0].transpose(1, 2, 0)
+    img = ((img + 1.0) / 2.0 * 255.0)
+    img = np.clip(img, 0, 255).astype(np.uint8)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    return img
+
+
+def _get_face_affine(face: Any, input_size: int):
+    """Compute affine transform to align a face to GPEN input space.
+
+    Returns (M, inv_M) — forward and inverse affine matrices.
+    """
+    template = np.array([
+        [0.31556875, 0.4615741],
+        [0.68262291, 0.4615741],
+        [0.50009375, 0.6405054],
+        [0.34947187, 0.8246919],
+        [0.65343645, 0.8246919],
+    ], dtype=np.float32) * input_size
+
+    landmarks = None
+    if hasattr(face, "kps") and face.kps is not None:
+        landmarks = face.kps.astype(np.float32)
+    elif hasattr(face, "landmark_2d_106") and face.landmark_2d_106 is not None:
+        lm106 = face.landmark_2d_106
+        landmarks = np.array([
+            lm106[38],  # left eye
+            lm106[88],  # right eye
+            lm106[86],  # nose tip
+            lm106[52],  # left mouth
+            lm106[61],  # right mouth
+        ], dtype=np.float32)
+
+    if landmarks is None or len(landmarks) < 5:
+        return None, None
+
+    M = cv2.estimateAffinePartial2D(landmarks, template, method=cv2.LMEDS)[0]
+    if M is None:
+        return None, None
+    inv_M = cv2.invertAffineTransform(M)
+    return M, inv_M
+
+
+def enhance_face_onnx(
+    frame: np.ndarray,
+    face: Any,
+    session: onnxruntime.InferenceSession,
+    input_size: int,
+) -> np.ndarray:
+    """Enhance a single face in the frame using an ONNX face restoration model."""
+    M, inv_M = _get_face_affine(face, input_size)
+    if M is None:
+        return frame
+
+    face_crop = cv2.warpAffine(
+        frame, M, (input_size, input_size),
+        flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE,
+    )
+
+    blob = preprocess_face(face_crop, input_size)
+    with THREAD_SEMAPHORE:
+        input_name = session.get_inputs()[0].name
+        output = run_inference(session, input_name, blob)
+    enhanced = postprocess_face(output)
+
+    # Create mask for blending (feathered edges)
+    mask = np.ones((input_size, input_size), dtype=np.float32)
+    border = max(1, input_size // 16)
+    mask[:border, :] = np.linspace(0, 1, border)[:, np.newaxis]
+    mask[-border:, :] = np.linspace(1, 0, border)[:, np.newaxis]
+    mask[:, :border] = np.minimum(mask[:, :border], np.linspace(0, 1, border)[np.newaxis, :])
+    mask[:, -border:] = np.minimum(mask[:, -border:], np.linspace(1, 0, border)[np.newaxis, :])
+
+    h, w = frame.shape[:2]
+    warped_enhanced = cv2.warpAffine(
+        enhanced, inv_M, (w, h),
+        flags=cv2.INTER_LINEAR, borderValue=(0, 0, 0),
+    )
+    warped_mask = cv2.warpAffine(
+        mask, inv_M, (w, h),
+        flags=cv2.INTER_LINEAR, borderValue=0,
+    )
+
+    mask_3ch = warped_mask[:, :, np.newaxis]
+    result = (warped_enhanced.astype(np.float32) * mask_3ch +
+              frame.astype(np.float32) * (1.0 - mask_3ch))
+    return np.clip(result, 0, 255).astype(np.uint8)

modules/processors/frame/core.py

@@ -1,12 +1,17 @@
+import os
+import subprocess
 import sys
 import importlib
 from concurrent.futures import ThreadPoolExecutor
 from types import ModuleType
 from typing import Any, List, Callable
+
+import numpy as np
 from tqdm import tqdm
 
 import modules
-import modules.globals                   
+import modules.globals
+from modules.face_analyser import get_one_face
 
 FRAME_PROCESSORS_MODULES: List[ModuleType] = []
 FRAME_PROCESSORS_INTERFACE = [
@@ -17,56 +22,386 @@ FRAME_PROCESSORS_INTERFACE = [
     'process_video'
 ]
 
-def load_frame_processor_module(frame_processor: str) -> ModuleType:
+ALLOWED_PROCESSORS = {
+    'face_swapper',
+    'face_enhancer',
+    'face_enhancer_gpen256',
+    'face_enhancer_gpen512'
+}
+
+def load_frame_processor_module(frame_processor: str) -> Any:
+    if frame_processor not in ALLOWED_PROCESSORS:
+        print(f"Frame processor {frame_processor} is not allowed")
+        sys.exit()
     try:
         frame_processor_module = importlib.import_module(f'modules.processors.frame.{frame_processor}')
-        # Ensure all required methods are present
         for method_name in FRAME_PROCESSORS_INTERFACE:
             if not hasattr(frame_processor_module, method_name):
-                raise AttributeError(f"Missing required method {method_name} in {frame_processor} module.")
+                print(f"Frame processor {frame_processor} is missing required method {method_name}")
+                sys.exit()
     except ImportError:
-        print(f"Error: Frame processor '{frame_processor}' not found.")
-        sys.exit(1)
-    except AttributeError as e:
-        print(e)
-        sys.exit(1)
-    
+        print(f"Frame processor {frame_processor} not found")
+        sys.exit()
     return frame_processor_module
 
+
 def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
     global FRAME_PROCESSORS_MODULES
 
     if not FRAME_PROCESSORS_MODULES:
-        FRAME_PROCESSORS_MODULES = [load_frame_processor_module(fp) for fp in frame_processors]
-    
+        for frame_processor in frame_processors:
+            frame_processor_module = load_frame_processor_module(frame_processor)
+            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
     set_frame_processors_modules_from_ui(frame_processors)
     return FRAME_PROCESSORS_MODULES
 
 def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
     global FRAME_PROCESSORS_MODULES
+    current_processor_names = [proc.__name__.split('.')[-1] for proc in FRAME_PROCESSORS_MODULES]
+
     for frame_processor, state in modules.globals.fp_ui.items():
-        if state and frame_processor not in frame_processors:
-            module = load_frame_processor_module(frame_processor)
-            FRAME_PROCESSORS_MODULES.append(module)
-            modules.globals.frame_processors.append(frame_processor)
-        elif not state and frame_processor in frame_processors:
-            module = load_frame_processor_module(frame_processor)
-            FRAME_PROCESSORS_MODULES.remove(module)
-            modules.globals.frame_processors.remove(frame_processor)
+        if state and frame_processor not in current_processor_names:
+            try:
+                frame_processor_module = load_frame_processor_module(frame_processor)
+                FRAME_PROCESSORS_MODULES.append(frame_processor_module)
+                if frame_processor not in modules.globals.frame_processors:
+                     modules.globals.frame_processors.append(frame_processor)
+            except SystemExit:
+                 print(f"Warning: Failed to load frame processor {frame_processor} requested by UI state.")
+            except Exception as e:
+                 print(f"Warning: Error loading frame processor {frame_processor} requested by UI state: {e}")
+
+        elif not state and frame_processor in current_processor_names:
+            try:
+                module_to_remove = next((mod for mod in FRAME_PROCESSORS_MODULES if mod.__name__.endswith(f'.{frame_processor}')), None)
+                if module_to_remove:
+                    FRAME_PROCESSORS_MODULES.remove(module_to_remove)
+                if frame_processor in modules.globals.frame_processors:
+                    modules.globals.frame_processors.remove(frame_processor)
+            except Exception as e:
+                 print(f"Warning: Error removing frame processor {frame_processor}: {e}")
 
 def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
-    with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
-        futures = [executor.submit(process_frames, source_path, [path], progress) for path in temp_frame_paths]
-        for future in futures:
-            future.result()
+    """Process frames in parallel with optimized batching and memory management."""
+    max_workers = modules.globals.execution_threads
+    
+    # Determine optimal batch size based on available memory and thread count
+    # Process frames in batches to avoid memory overflow
+    batch_size = max(1, min(32, len(temp_frame_paths) // max(1, max_workers)))
+    
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # Process in batches to manage memory better
+        for i in range(0, len(temp_frame_paths), batch_size):
+            batch = temp_frame_paths[i:i + batch_size]
+            futures = []
+            
+            for path in batch:
+                future = executor.submit(process_frames, source_path, [path], progress)
+                futures.append(future)
+            
+            # Wait for batch to complete before starting next batch
+            for future in futures:
+                try:
+                    future.result()
+                except Exception as e:
+                    print(f"Error processing frame: {e}")
 
-def process_video(source_path: str, frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
+
+def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
     progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
     total = len(frame_paths)
     with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
-        progress.set_postfix({
-            'execution_providers': modules.globals.execution_providers, 
-            'execution_threads': modules.globals.execution_threads, 
-            'max_memory': modules.globals.max_memory
-        })
+        progress.set_postfix({'execution_providers': modules.globals.execution_providers, 'execution_threads': modules.globals.execution_threads, 'max_memory': modules.globals.max_memory})
         multi_process_frame(source_path, frame_paths, process_frames, progress)
+
+
+def process_video_in_memory(source_path: str, target_path: str, fps: float) -> bool:
+    """Process video frames in-memory using FFmpeg pipes, eliminating disk I/O.
+
+    Reads raw frames from the source video via an FFmpeg decoder pipe, runs each
+    frame through all active frame processors sequentially, and writes the
+    result directly to an FFmpeg encoder pipe.  This avoids extracting frames to
+    PNG on disk, which is the biggest I/O bottleneck in the disk-based pipeline.
+
+    Returns True on success, False on failure (caller should fall back to the
+    disk-based pipeline).
+    """
+    from modules import imread_unicode
+    from modules.face_analyser import get_one_face
+    from modules.utilities import (
+        get_video_dimensions,
+        estimate_frame_count,
+        get_temp_output_path,
+    )
+
+    temp_output_path = get_temp_output_path(target_path)
+
+    # --- Pre-load source face (needed by face_swapper in simple mode) ---
+    source_face = None
+    if source_path and os.path.exists(source_path):
+        source_img = imread_unicode(source_path)
+        if source_img is not None:
+            source_face = get_one_face(source_img)
+            del source_img
+        if source_face is None:
+            print("[DLC.CORE] Warning: No face detected in source image. "
+                  "Face swapping will be skipped.")
+
+    # --- Collect frame processors & reset per-video state ---
+    frame_processors = get_frame_processors_modules(modules.globals.frame_processors)
+    for fp in frame_processors:
+        if hasattr(fp, 'PREVIOUS_FRAME_RESULT'):
+            fp.PREVIOUS_FRAME_RESULT = None
+
+    # --- Video metadata ---
+    try:
+        width, height = get_video_dimensions(target_path)
+    except Exception as e:
+        print(f"[DLC.CORE] Failed to get video dimensions: {e}")
+        return False
+
+    total_frames = estimate_frame_count(target_path, fps)
+    frame_size = width * height * 3
+
+    # --- Build encoder arguments ---
+    encoder = modules.globals.video_encoder
+    encoder_options: List[str] = []
+    is_hw_encoder = False
+
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+        if encoder == 'libx264':
+            encoder = 'h264_nvenc'
+            is_hw_encoder = True
+            encoder_options = [
+                '-preset', 'p4', '-tune', 'hq', '-rc', 'vbr',
+                '-cq', str(modules.globals.video_quality), '-b:v', '0',
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_nvenc'
+            is_hw_encoder = True
+            encoder_options = [
+                '-preset', 'p4', '-tune', 'hq', '-rc', 'vbr',
+                '-cq', str(modules.globals.video_quality), '-b:v', '0',
+            ]
+    elif 'DmlExecutionProvider' in modules.globals.execution_providers:
+        if encoder == 'libx264':
+            encoder = 'h264_amf'
+            is_hw_encoder = True
+            encoder_options = [
+                '-quality', 'quality', '-rc', 'vbr_latency',
+                '-qp_i', str(modules.globals.video_quality),
+                '-qp_p', str(modules.globals.video_quality),
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_amf'
+            is_hw_encoder = True
+            encoder_options = [
+                '-quality', 'quality', '-rc', 'vbr_latency',
+                '-qp_i', str(modules.globals.video_quality),
+                '-qp_p', str(modules.globals.video_quality),
+            ]
+
+    if not is_hw_encoder:
+        if encoder == 'libx264':
+            encoder_options = [
+                '-preset', 'medium',
+                '-crf', str(modules.globals.video_quality),
+                '-tune', 'film',
+            ]
+        elif encoder == 'libx265':
+            encoder_options = [
+                '-preset', 'medium',
+                '-crf', str(modules.globals.video_quality),
+                '-x265-params', 'log-level=error',
+            ]
+        elif encoder == 'libvpx-vp9':
+            encoder_options = [
+                '-crf', str(modules.globals.video_quality),
+                '-b:v', '0', '-cpu-used', '2',
+            ]
+
+    # --- Attempt pipeline (hw encoder first, then sw fallback) ---
+    encoders_to_try = [(encoder, encoder_options)]
+    if is_hw_encoder:
+        # Software fallback
+        sw_encoder = 'libx264'
+        sw_options = [
+            '-preset', 'medium',
+            '-crf', str(modules.globals.video_quality),
+            '-tune', 'film',
+        ]
+        encoders_to_try.append((sw_encoder, sw_options))
+
+    for attempt, (enc, enc_opts) in enumerate(encoders_to_try):
+        # Reset interpolation state on retry
+        if attempt > 0:
+            for fp in frame_processors:
+                if hasattr(fp, 'PREVIOUS_FRAME_RESULT'):
+                    fp.PREVIOUS_FRAME_RESULT = None
+
+        success = _run_pipe_pipeline(
+            target_path, temp_output_path, fps,
+            source_face, frame_processors,
+            width, height, frame_size, total_frames,
+            enc, enc_opts,
+        )
+        if success:
+            return True
+
+        if attempt == 0 and is_hw_encoder:
+            print(f"[DLC.CORE] Hardware encoder '{enc}' failed, "
+                  f"retrying with software encoder...")
+
+    return False
+
+
+def _run_pipe_pipeline(
+    target_path: str,
+    temp_output_path: str,
+    fps: float,
+    source_face: Any,
+    frame_processors: List[Any],
+    width: int,
+    height: int,
+    frame_size: int,
+    total_frames: int,
+    encoder: str,
+    encoder_options: List[str],
+) -> bool:
+    """Run the FFmpeg-pipe read → process → encode pipeline once."""
+
+    # --- Reader: decode source video to raw BGR24 on stdout ---
+    reader_cmd = [
+        'ffmpeg', '-hide_banner',
+        '-hwaccel', 'auto',
+        '-i', target_path,
+        '-f', 'rawvideo',
+        '-pix_fmt', 'bgr24',
+        '-v', 'error',
+        '-',
+    ]
+
+    # --- Writer: encode raw BGR24 from stdin ---
+    writer_cmd = [
+        'ffmpeg', '-hide_banner',
+        '-f', 'rawvideo',
+        '-pix_fmt', 'bgr24',
+        '-s', f'{width}x{height}',
+        '-r', str(fps),
+        '-i', '-',
+        '-c:v', encoder,
+    ]
+    writer_cmd.extend(encoder_options)
+    writer_cmd.extend([
+        '-pix_fmt', 'yuv420p',
+        '-movflags', '+faststart',
+        '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1',
+        '-v', 'error',
+        '-y', temp_output_path,
+    ])
+
+    reader = None
+    writer = None
+    try:
+        reader = subprocess.Popen(
+            reader_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+        )
+        writer = subprocess.Popen(
+            writer_cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE,
+        )
+    except Exception as e:
+        print(f"[DLC.CORE] Failed to start FFmpeg pipes: {e}")
+        for proc in (reader, writer):
+            if proc:
+                try:
+                    proc.kill()
+                except Exception:
+                    pass
+        return False
+
+    processed_count = 0
+    bar_fmt = ('{l_bar}{bar}| {n_fmt}/{total_fmt} '
+               '[{elapsed}<{remaining}, {rate_fmt}{postfix}]')
+
+    try:
+        with tqdm(total=total_frames, desc='Processing', unit='frame',
+                  dynamic_ncols=True, bar_format=bar_fmt) as progress:
+            progress.set_postfix({
+                'execution_providers': modules.globals.execution_providers,
+                'threads': modules.globals.execution_threads,
+                'mode': 'in-memory',
+            })
+
+            # Pipelined detection: while processing frame N (swap on
+            # ANE), start detecting the face in the next frame
+            # (detection on GPU).  They use different hardware units
+            # so the work overlaps.
+            detect_executor = ThreadPoolExecutor(max_workers=1)
+            pending_detect = None
+            use_pipeline = not modules.globals.many_faces
+
+            while True:
+                raw = reader.stdout.read(frame_size)
+                if len(raw) != frame_size:
+                    break
+
+                frame = np.frombuffer(raw, dtype=np.uint8).reshape(
+                    (height, width, 3)
+                ).copy()
+
+                # Get the detection result for THIS frame
+                if use_pipeline:
+                    if pending_detect is not None:
+                        target_face = pending_detect.result()
+                    else:
+                        target_face = get_one_face(frame)
+                    # Start detecting on THIS frame eagerly — the result
+                    # will be used for the next iteration.  At video
+                    # frame rates the face barely moves between frames.
+                    # Hand the detector its own copy: the frame processors
+                    # below mutate `frame` in place (paste-back), which
+                    # would otherwise race with detection.
+                    pending_detect = detect_executor.submit(
+                        get_one_face, frame.copy())
+                else:
+                    target_face = None
+
+                # Run frame through every active processor
+                for fp in frame_processors:
+                    try:
+                        frame = fp.process_frame(source_face, frame, target_face=target_face)
+                    except TypeError:
+                        frame = fp.process_frame(source_face, frame)
+
+                writer.stdin.write(frame.tobytes())
+                processed_count += 1
+                progress.update(1)
+
+            detect_executor.shutdown(wait=True)
+
+        # Graceful shutdown
+        writer.stdin.close()
+        writer.wait()
+        reader.wait()
+
+        if writer.returncode != 0:
+            stderr_out = writer.stderr.read().decode(errors='ignore').strip()
+            if stderr_out:
+                print(f"[DLC.CORE] FFmpeg encoder error: {stderr_out}")
+            return False
+
+        return processed_count > 0 and os.path.isfile(temp_output_path)
+
+    except BrokenPipeError:
+        print("[DLC.CORE] FFmpeg pipe broken (encoder may not be available).")
+        return False
+    except Exception as e:
+        print(f"[DLC.CORE] In-memory processing error: {e}")
+        return False
+    finally:
+        for proc in (reader, writer):
+            if proc:
+                try:
+                    proc.kill()
+                except Exception:
+                    pass

modules/processors/frame/face_enhancer.py

@@ -1,70 +1,445 @@
+# Uses ONNX Runtime for GFPGAN face enhancement (no torch/gfpgan dependency)
+
 from typing import Any, List
 import cv2
 import threading
-import gfpgan
+import numpy as np
 import os
 
+import onnxruntime
+
 import modules.globals
 import modules.processors.frame.core
+from modules import imread_unicode, imwrite_unicode
 from modules.core import update_status
-from modules.face_analyser import get_one_face
-from modules.typing import Frame, Face  # Ensure these are imported
-from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
+from modules.face_analyser import get_many_faces
+from modules.typing import Frame, Face
+from modules.utilities import (
+    is_image,
+    is_video,
+)
 
 FACE_ENHANCER = None
 THREAD_SEMAPHORE = threading.Semaphore()
 THREAD_LOCK = threading.Lock()
-NAME = 'DLC.FACE-ENHANCER'
+NAME = "DLC.FACE-ENHANCER"
+
+abs_dir = os.path.dirname(os.path.abspath(__file__))
+models_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
+)
+
+# Standard FFHQ 5-point face template for 512x512 resolution
+# Points: left_eye, right_eye, nose, left_mouth, right_mouth
+FFHQ_TEMPLATE_512 = np.array(
+    [
+        [192.98138, 239.94708],
+        [318.90277, 240.19366],
+        [256.63416, 314.01935],
+        [201.26117, 371.41043],
+        [313.08905, 371.15118],
+    ],
+    dtype=np.float32,
+)
+
 
 def pre_check() -> bool:
-    download_directory_path = resolve_relative_path('..\models')
-    conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth'])
-    return True
-
-def pre_start() -> bool:
-    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
-        update_status('Select an image or video for target path.', NAME)
+    model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
+    if not os.path.exists(model_path):
+        update_status(
+            f"GFPGAN ONNX model not found at {model_path}. "
+            "Please place gfpgan-1024.onnx in the models folder.",
+            NAME,
+        )
         return False
     return True
 
-def get_face_enhancer() -> Any:
+
+def pre_start() -> bool:
+    if not is_image(modules.globals.target_path) and not is_video(
+        modules.globals.target_path
+    ):
+        update_status("Select an image or video for target path.", NAME)
+        return False
+    return True
+
+
+def get_face_enhancer() -> onnxruntime.InferenceSession:
+    """
+    Initializes and returns the GFPGAN ONNX Runtime inference session,
+    using the execution providers configured in modules.globals.
+    """
     global FACE_ENHANCER
 
     with THREAD_LOCK:
         if FACE_ENHANCER is None:
-            model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
-            FACE_ENHANCER = gfpgan.GFPGANer(
-                model_path=model_path,
-                upscale=modules.globals.enhancer_upscale_factor
-            )  # type: ignore[attr-defined]
+            model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
+
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(
+                    f"{NAME}: Model not found at {model_path}"
+                )
+
+            try:
+                from modules.processors.frame._onnx_enhancer import (
+                    create_onnx_session,
+                )
+
+                FACE_ENHANCER = create_onnx_session(model_path)
+
+                input_info = FACE_ENHANCER.get_inputs()[0]
+                output_info = FACE_ENHANCER.get_outputs()[0]
+                active_providers = FACE_ENHANCER.get_providers()
+                print(
+                    f"{NAME}: GFPGAN ONNX model loaded successfully."
+                )
+                print(
+                    f"{NAME}: Input: {input_info.name}, "
+                    f"shape: {input_info.shape}, type: {input_info.type}"
+                )
+                print(
+                    f"{NAME}: Output: {output_info.name}, "
+                    f"shape: {output_info.shape}, type: {output_info.type}"
+                )
+                print(f"{NAME}: Active providers: {active_providers}")
+
+            except Exception as e:
+                print(f"{NAME}: Error loading GFPGAN ONNX model: {e}")
+                FACE_ENHANCER = None
+                raise RuntimeError(
+                    f"{NAME}: Failed to load GFPGAN ONNX model: {e}"
+                )
+
+    if FACE_ENHANCER is None:
+        raise RuntimeError(
+            f"{NAME}: Failed to initialize GFPGAN ONNX session. Check logs."
+        )
+
     return FACE_ENHANCER
 
-def enhance_face(temp_frame: Frame) -> Frame:
-    with THREAD_SEMAPHORE:
-        _, _, temp_frame = get_face_enhancer().enhance(
-            temp_frame,
-            paste_back=True
-        )
+
+def _align_face(
+    frame: Frame, landmarks_5: np.ndarray, output_size: int
+) -> tuple:
+    """
+    Align and crop a face from the frame using 5-point landmarks and the
+    standard FFHQ template.
+
+    Returns:
+        (aligned_face, affine_matrix) or (None, None) on failure.
+    """
+    # Scale the 512-base template to the desired output size
+    scale = output_size / 512.0
+    template = FFHQ_TEMPLATE_512 * scale
+
+    # Estimate a similarity transform (4 DOF: rotation, scale, tx, ty)
+    affine_matrix, _ = cv2.estimateAffinePartial2D(
+        landmarks_5, template, method=cv2.LMEDS
+    )
+    if affine_matrix is None:
+        return None, None
+
+    # Warp the face to the aligned position
+    aligned_face = cv2.warpAffine(
+        frame,
+        affine_matrix,
+        (output_size, output_size),
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=(135, 133, 132),
+    )
+
+    return aligned_face, affine_matrix
+
+
+_HAS_TORCH_CUDA = False
+try:
+    import torch
+    if torch.cuda.is_available():
+        _HAS_TORCH_CUDA = True
+except ImportError:
+    pass
+
+# Cache the feathered mask — it's the same for every call at a given size
+_enhancer_cache: dict = {'mask': None, 'mask_size': 0}
+
+
+def _paste_back(
+    frame: Frame,
+    enhanced_face: np.ndarray,
+    affine_matrix: np.ndarray,
+    output_size: int,
+) -> Frame:
+    """
+    Paste an enhanced (aligned) face back onto the original frame using the
+    inverse affine transform with feathered-edge blending.
+
+    Optimized: operates on a tight crop around the face bbox instead of the
+    full frame, and uses GPU for blending when available.
+    """
+    h, w = frame.shape[:2]
+    inv_matrix = cv2.invertAffineTransform(affine_matrix)
+
+    # Build or reuse cached feathered mask (uint8 — blended via cv2 SIMD ops)
+    if _enhancer_cache['mask_size'] != output_size:
+        face_mask_f = np.ones((output_size, output_size), dtype=np.float32)
+        border = max(1, int(output_size * 0.05))
+        ramp_up = np.linspace(0.0, 1.0, border, dtype=np.float32)
+        ramp_down = np.linspace(1.0, 0.0, border, dtype=np.float32)
+        face_mask_f[:border, :] *= ramp_up[:, None]
+        face_mask_f[-border:, :] *= ramp_down[:, None]
+        face_mask_f[:, :border] *= ramp_up[None, :]
+        face_mask_f[:, -border:] *= ramp_down[None, :]
+        _enhancer_cache['mask'] = (face_mask_f * 255.0).astype(np.uint8)
+        _enhancer_cache['mask_size'] = output_size
+
+    # Compute tight bbox from affine corners (avoids full-frame warpAffine scan)
+    corners = np.array([[0, 0], [output_size, 0],
+                        [output_size, output_size], [0, output_size]],
+                       dtype=np.float32)
+    transformed = (inv_matrix[:, :2] @ corners.T).T + inv_matrix[:, 2]
+    x1 = max(0, int(np.floor(transformed[:, 0].min())))
+    x2 = min(w, int(np.ceil(transformed[:, 0].max())))
+    y1 = max(0, int(np.floor(transformed[:, 1].min())))
+    y2 = min(h, int(np.ceil(transformed[:, 1].max())))
+    if x1 >= x2 or y1 >= y2:
+        return frame
+
+    # Pad a few pixels for feathering
+    pad = max(1, int(output_size * 0.05)) + 2
+    y1p, y2p = max(0, y1 - pad), min(h, y2 + pad)
+    x1p, x2p = max(0, x1 - pad), min(w, x2 + pad)
+    crop_w, crop_h = x2p - x1p, y2p - y1p
+
+    # Warp enhanced face and mask into crop space only
+    inv_crop = inv_matrix.copy()
+    inv_crop[0, 2] -= x1p
+    inv_crop[1, 2] -= y1p
+
+    inv_restored_crop = cv2.warpAffine(
+        enhanced_face, inv_crop, (crop_w, crop_h),
+        borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0),
+    )
+    inv_mask_crop = cv2.warpAffine(
+        _enhancer_cache['mask'], inv_crop, (crop_w, crop_h),
+        borderMode=cv2.BORDER_CONSTANT, borderValue=0,
+    )
+
+    target_crop = frame[y1p:y2p, x1p:x2p]
+
+    if _HAS_TORCH_CUDA:
+        # Upload uint8 alpha — smaller transfer, scale on device.
+        mask_t = torch.from_numpy(inv_mask_crop).cuda().float().mul_(1.0 / 255.0).unsqueeze(2)
+        enhanced_t = torch.from_numpy(inv_restored_crop).float().cuda()
+        target_t = torch.from_numpy(target_crop).float().cuda()
+        blended = (mask_t * enhanced_t + (1.0 - mask_t) * target_t
+                   ).to(torch.uint8).cpu().numpy()
+        frame[y1p:y2p, x1p:x2p] = blended
+    else:
+        # Fused uint8 blend via cv2 SIMD — ~7× faster than the float32 round-trip.
+        alpha_3c = cv2.merge([inv_mask_crop, inv_mask_crop, inv_mask_crop])
+        inv_alpha = 255 - alpha_3c
+        a_enh = cv2.multiply(inv_restored_crop, alpha_3c, scale=1.0 / 255.0)
+        a_tgt = cv2.multiply(target_crop, inv_alpha, scale=1.0 / 255.0)
+        frame[y1p:y2p, x1p:x2p] = cv2.add(a_enh, a_tgt)
+
+    return frame
+
+
+def _preprocess_face(aligned_face: np.ndarray) -> np.ndarray:
+    """
+    Convert an aligned BGR uint8 face image to the ONNX model input tensor.
+    Format: NCHW float32, normalised to [-1, 1].
+    """
+    # BGR -> RGB, normalize, and transpose in one pass
+    # Fused: (x / 255.0 - 0.5) / 0.5 = x / 127.5 - 1.0
+    rgb = aligned_face[:, :, ::-1]  # BGR->RGB zero-copy view
+    chw = np.transpose(rgb, (2, 0, 1)).astype(np.float32)
+    chw *= (1.0 / 127.5)
+    chw -= 1.0
+    return chw[np.newaxis, ...]  # shape: (1, 3, H, W)
+
+
+def _postprocess_face(output: np.ndarray) -> np.ndarray:
+    """
+    Convert the ONNX model output tensor back to a BGR uint8 image.
+    Expects input in NCHW format with values in [-1, 1].
+    """
+    # Fused: ((x + 1.0) / 2.0) * 255 = (x + 1.0) * 127.5
+    face = output[0]  # remove batch dim -> (3, H, W)
+    face = (face + 1.0) * 127.5
+    np.clip(face, 0, 255, out=face)
+    face = face.astype(np.uint8).transpose(1, 2, 0)  # CHW -> HWC
+    return face[:, :, ::-1].copy()  # RGB -> BGR
+
+
+# Cache for temporal enhancement skipping in live mode.
+# GFPGAN output barely changes between consecutive frames (same face,
+# same position), so we run inference every _ENH_INTERVAL frames and
+# reuse the cached enhanced face + affine matrix in between.
+_enh_live_cache: dict = {
+    'enhanced_bgr': None,
+    'affine_matrix': None,
+    'align_size': 0,
+    'frame_count': 0,
+}
+_ENH_INTERVAL = 2  # run inference every N frames, paste cached result otherwise
+
+
+def enhance_face(temp_frame: Frame, detected_faces=None) -> Frame:
+    """Enhances all faces in a frame using the GFPGAN ONNX model.
+
+    Args:
+        detected_faces: Pre-detected face list. When provided, skips
+            the internal detection call (saves ~15-20ms per frame).
+            Also enables temporal caching — inference runs every
+            _ENH_INTERVAL frames, reusing the cached result otherwise.
+    """
+    session = get_face_enhancer()
+
+    # Determine model input resolution from the session metadata
+    input_info = session.get_inputs()[0]
+    input_name = input_info.name
+    input_shape = input_info.shape  # e.g. [1, 3, 512, 512]
+    try:
+        align_size = int(input_shape[2])
+        if align_size <= 0:
+            align_size = 512
+    except (ValueError, TypeError, IndexError):
+        align_size = 512
+
+    # Use pre-detected faces if available, otherwise detect
+    faces = detected_faces if detected_faces is not None else get_many_faces(temp_frame)
+    if not faces:
+        return temp_frame
+
+    # Temporal caching: only available when faces are pre-detected (live mode)
+    # AND we're in single-face mode — the cache holds exactly one enhancement,
+    # so reusing it in many_faces mode would paste the same face onto every
+    # detected target.
+    many_faces_mode = getattr(modules.globals, "many_faces", False)
+    use_cache = detected_faces is not None and not many_faces_mode
+    if use_cache:
+        _enh_live_cache['frame_count'] += 1
+        run_inference_this_frame = (_enh_live_cache['frame_count'] % _ENH_INTERVAL == 0
+                                   or _enh_live_cache['enhanced_bgr'] is None)
+    else:
+        run_inference_this_frame = True
+
+    for face in faces:
+        if not hasattr(face, "kps") or face.kps is None:
+            continue
+
+        landmarks_5 = face.kps.astype(np.float32)
+        if landmarks_5.shape[0] < 5:
+            continue
+
+        if run_inference_this_frame:
+            aligned_face, affine_matrix = _align_face(
+                temp_frame, landmarks_5, output_size=align_size
+            )
+            if aligned_face is None or affine_matrix is None:
+                continue
+
+            try:
+                with THREAD_SEMAPHORE:
+                    from modules.processors.frame._onnx_enhancer import (
+                        run_inference,
+                    )
+                    input_tensor = _preprocess_face(aligned_face)
+                    output_tensor = run_inference(session, input_name, input_tensor)
+                    enhanced_bgr = _postprocess_face(output_tensor)
+
+                eh, ew = enhanced_bgr.shape[:2]
+                if eh != align_size or ew != align_size:
+                    enhanced_bgr = cv2.resize(
+                        enhanced_bgr,
+                        (align_size, align_size),
+                        interpolation=cv2.INTER_LANCZOS4,
+                    )
+
+                # Cache for reuse on next frame
+                if use_cache:
+                    _enh_live_cache['enhanced_bgr'] = enhanced_bgr
+                    _enh_live_cache['affine_matrix'] = affine_matrix
+                    _enh_live_cache['align_size'] = align_size
+
+                _paste_back(
+                    temp_frame, enhanced_bgr, affine_matrix, output_size=align_size
+                )
+            except Exception as e:
+                print(f"{NAME}: Error enhancing a face: {e}")
+                continue
+        else:
+            # Reuse cached enhanced face — just paste back onto current frame
+            cached = _enh_live_cache
+            if cached['enhanced_bgr'] is not None:
+                _paste_back(
+                    temp_frame, cached['enhanced_bgr'],
+                    cached['affine_matrix'],
+                    output_size=cached['align_size'],
+                )
+        if not many_faces_mode:
+            break  # single-face live mode — only process first face
+
     return temp_frame
 
-def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
-    target_face = get_one_face(temp_frame)
-    if target_face:
-        temp_frame = enhance_face(temp_frame)
-    return temp_frame
 
-def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
+def process_frame(source_face: Face | None, temp_frame: Frame,
+                   detected_faces=None) -> Frame:
+    """Processes a frame: enhances face if detected."""
+    return enhance_face(temp_frame, detected_faces=detected_faces)
+
+
+def process_frame_v2(temp_frame: Frame, detected_faces=None) -> Frame:
+    """Processes a frame without source face (used by live webcam preview)."""
+    return enhance_face(temp_frame, detected_faces=detected_faces)
+
+
+def process_frames(
+    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
+) -> None:
+    """Processes multiple frames from file paths."""
     for temp_frame_path in temp_frame_paths:
-        temp_frame = cv2.imread(temp_frame_path)
-        result = process_frame(None, temp_frame)
-        cv2.imwrite(temp_frame_path, result)
+        if not os.path.exists(temp_frame_path):
+            print(
+                f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping."
+            )
+            if progress:
+                progress.update(1)
+            continue
+
+        temp_frame = imread_unicode(temp_frame_path)
+        if temp_frame is None:
+            print(
+                f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping."
+            )
+            if progress:
+                progress.update(1)
+            continue
+
+        result_frame = process_frame(None, temp_frame)
+        imwrite_unicode(temp_frame_path, result_frame)
         if progress:
             progress.update(1)
 
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    target_frame = cv2.imread(target_path)
-    result = process_frame(None, target_frame)
-    cv2.imwrite(output_path, result)
 
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
+def process_image(
+    source_path: str | None, target_path: str, output_path: str
+) -> None:
+    """Processes a single image file."""
+    target_frame = imread_unicode(target_path)
+    if target_frame is None:
+        print(f"{NAME}: Error: Failed to read target image {target_path}")
+        return
+    result_frame = process_frame(None, target_frame)
+    imwrite_unicode(output_path, result_frame)
+    print(f"{NAME}: Enhanced image saved to {output_path}")
+
+
+def process_video(
+    source_path: str | None, temp_frame_paths: List[str]
+) -> None:
+    """Processes video frames using the frame processor core."""
+    modules.processors.frame.core.process_video(
+        source_path, temp_frame_paths, process_frames
+    )

modules/processors/frame/face_enhancer_gpen256.py

@@ -0,0 +1,126 @@
+"""GPEN-BFR-256 face enhancer — ONNX-based face restoration at 256x256."""
+
+from typing import Any, List
+import os
+import threading
+
+import modules.globals
+import modules.processors.frame.core
+from modules import imread_unicode, imwrite_unicode
+from modules.core import update_status
+from modules.face_analyser import get_one_face
+from modules.typing import Frame, Face
+from modules.utilities import (
+    is_image,
+    is_video,
+)
+from modules.processors.frame._onnx_enhancer import (
+    create_onnx_session,
+    warmup_session,
+    enhance_face_onnx,
+)
+
+NAME = "DLC.FACE-ENHANCER-GPEN256"
+INPUT_SIZE = 256
+MODEL_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-256.onnx"
+MODEL_FILE = "GPEN-BFR-256.onnx"
+
+ENHANCER = None
+THREAD_LOCK = threading.Lock()
+
+abs_dir = os.path.dirname(os.path.abspath(__file__))
+models_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
+)
+
+
+def pre_check() -> bool:
+    model_path = os.path.join(models_dir, MODEL_FILE)
+    if not os.path.exists(model_path):
+        update_status(f"Downloading {MODEL_FILE}...", NAME)
+        from modules.utilities import conditional_download
+        conditional_download(models_dir, [MODEL_URL])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
+        update_status("Select an image or video for target path.", NAME)
+        return False
+    return True
+
+
+def get_enhancer() -> Any:
+    global ENHANCER
+    with THREAD_LOCK:
+        if ENHANCER is None:
+            model_path = os.path.join(models_dir, MODEL_FILE)
+            if not os.path.exists(model_path):
+                from modules.utilities import conditional_download
+                conditional_download(models_dir, [MODEL_URL])
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(f"Model file not found: {model_path}")
+            print(f"{NAME}: Loading ONNX model from {model_path}")
+            ENHANCER = create_onnx_session(model_path)
+            warmup_session(ENHANCER)
+            print(f"{NAME}: Model loaded successfully.")
+    return ENHANCER
+
+
+def enhance_face(temp_frame: Frame, face: Face) -> Frame:
+    try:
+        session = get_enhancer()
+    except Exception as e:
+        print(f"{NAME}: {e}")
+        return temp_frame
+    try:
+        return enhance_face_onnx(temp_frame, face, session, INPUT_SIZE)
+    except Exception as e:
+        print(f"{NAME}: Error during face enhancement: {e}")
+        return temp_frame
+
+
+def process_frame(source_face: Face | None, temp_frame: Frame, detected_faces=None) -> Frame:
+    if detected_faces:
+        target_face = detected_faces[0]
+    else:
+        target_face = get_one_face(temp_frame)
+    if target_face is None:
+        return temp_frame
+    return enhance_face(temp_frame, target_face)
+
+
+def process_frame_v2(temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
+        temp_frame = enhance_face(temp_frame, target_face)
+    return temp_frame
+
+
+def process_frames(
+    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
+) -> None:
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = imread_unicode(temp_frame_path)
+        if temp_frame is None:
+            if progress:
+                progress.update(1)
+            continue
+        result = process_frame(None, temp_frame)
+        imwrite_unicode(temp_frame_path, result)
+        if progress:
+            progress.update(1)
+
+
+def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
+    target_frame = imread_unicode(target_path)
+    if target_frame is None:
+        print(f"{NAME}: Error: Failed to read target image {target_path}")
+        return
+    result_frame = process_frame(None, target_frame)
+    imwrite_unicode(output_path, result_frame)
+    print(f"{NAME}: Enhanced image saved to {output_path}")
+
+
+def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
+    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)

modules/processors/frame/face_enhancer_gpen512.py

@@ -0,0 +1,126 @@
+"""GPEN-BFR-512 face enhancer — ONNX-based face restoration at 512x512."""
+
+from typing import Any, List
+import os
+import threading
+
+import modules.globals
+import modules.processors.frame.core
+from modules import imread_unicode, imwrite_unicode
+from modules.core import update_status
+from modules.face_analyser import get_one_face
+from modules.typing import Frame, Face
+from modules.utilities import (
+    is_image,
+    is_video,
+)
+from modules.processors.frame._onnx_enhancer import (
+    create_onnx_session,
+    warmup_session,
+    enhance_face_onnx,
+)
+
+NAME = "DLC.FACE-ENHANCER-GPEN512"
+INPUT_SIZE = 512
+MODEL_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-512.onnx"
+MODEL_FILE = "GPEN-BFR-512.onnx"
+
+ENHANCER = None
+THREAD_LOCK = threading.Lock()
+
+abs_dir = os.path.dirname(os.path.abspath(__file__))
+models_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
+)
+
+
+def pre_check() -> bool:
+    model_path = os.path.join(models_dir, MODEL_FILE)
+    if not os.path.exists(model_path):
+        update_status(f"Downloading {MODEL_FILE}...", NAME)
+        from modules.utilities import conditional_download
+        conditional_download(models_dir, [MODEL_URL])
+    return True
+
+
+def pre_start() -> bool:
+    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
+        update_status("Select an image or video for target path.", NAME)
+        return False
+    return True
+
+
+def get_enhancer() -> Any:
+    global ENHANCER
+    with THREAD_LOCK:
+        if ENHANCER is None:
+            model_path = os.path.join(models_dir, MODEL_FILE)
+            if not os.path.exists(model_path):
+                from modules.utilities import conditional_download
+                conditional_download(models_dir, [MODEL_URL])
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(f"Model file not found: {model_path}")
+            print(f"{NAME}: Loading ONNX model from {model_path}")
+            ENHANCER = create_onnx_session(model_path)
+            warmup_session(ENHANCER)
+            print(f"{NAME}: Model loaded successfully.")
+    return ENHANCER
+
+
+def enhance_face(temp_frame: Frame, face: Face) -> Frame:
+    try:
+        session = get_enhancer()
+    except Exception as e:
+        print(f"{NAME}: {e}")
+        return temp_frame
+    try:
+        return enhance_face_onnx(temp_frame, face, session, INPUT_SIZE)
+    except Exception as e:
+        print(f"{NAME}: Error during face enhancement: {e}")
+        return temp_frame
+
+
+def process_frame(source_face: Face | None, temp_frame: Frame, detected_faces=None) -> Frame:
+    if detected_faces:
+        target_face = detected_faces[0]
+    else:
+        target_face = get_one_face(temp_frame)
+    if target_face is None:
+        return temp_frame
+    return enhance_face(temp_frame, target_face)
+
+
+def process_frame_v2(temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
+        temp_frame = enhance_face(temp_frame, target_face)
+    return temp_frame
+
+
+def process_frames(
+    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
+) -> None:
+    for temp_frame_path in temp_frame_paths:
+        temp_frame = imread_unicode(temp_frame_path)
+        if temp_frame is None:
+            if progress:
+                progress.update(1)
+            continue
+        result = process_frame(None, temp_frame)
+        imwrite_unicode(temp_frame_path, result)
+        if progress:
+            progress.update(1)
+
+
+def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
+    target_frame = imread_unicode(target_path)
+    if target_frame is None:
+        print(f"{NAME}: Error: Failed to read target image {target_path}")
+        return
+    result_frame = process_frame(None, target_frame)
+    imwrite_unicode(output_path, result_frame)
+    print(f"{NAME}: Enhanced image saved to {output_path}")
+
+
+def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
+    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)

modules/processors/frame/face_masking.py

@@ -0,0 +1,577 @@
+import cv2
+import numpy as np
+from modules.typing import Face, Frame
+import modules.globals
+from modules.gpu_processing import gpu_gaussian_blur, gpu_resize
+
+def apply_color_transfer(source, target):
+    """
+    Apply color transfer from target to source image using LAB color space.
+    Uses float32 throughout for performance (sufficient precision for 8-bit images).
+    """
+    # Convert to float32 [0,1] range for proper LAB conversion
+    source_f32 = source.astype(np.float32) / 255.0
+    target_f32 = target.astype(np.float32) / 255.0
+
+    source_lab = cv2.cvtColor(source_f32, cv2.COLOR_BGR2LAB)
+    target_lab = cv2.cvtColor(target_f32, cv2.COLOR_BGR2LAB)
+
+    source_mean, source_std = cv2.meanStdDev(source_lab)
+    target_mean, target_std = cv2.meanStdDev(target_lab)
+
+    # Reshape mean and std to be broadcastable (already float64 from meanStdDev, cast to f32)
+    source_mean = source_mean.reshape(1, 1, 3).astype(np.float32)
+    source_std = np.maximum(source_std.reshape(1, 1, 3), 1e-6).astype(np.float32)
+    target_mean = target_mean.reshape(1, 1, 3).astype(np.float32)
+    target_std = target_std.reshape(1, 1, 3).astype(np.float32)
+
+    # Perform the color transfer in LAB space
+    result_lab = (source_lab - source_mean) * (target_std / source_std) + target_mean
+
+    # Convert back to BGR and uint8
+    result_bgr = cv2.cvtColor(result_lab, cv2.COLOR_LAB2BGR)
+    return np.clip(result_bgr * 255.0, 0, 255).astype(np.uint8)
+
+def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Convert landmarks to int32
+        landmarks = landmarks.astype(np.int32)
+
+        # Extract facial features
+        right_side_face = landmarks[0:16]
+        left_side_face = landmarks[17:32]
+        right_eye = landmarks[33:42]
+        right_eye_brow = landmarks[43:51]
+        left_eye = landmarks[87:96]
+        left_eye_brow = landmarks[97:105]
+
+        # Calculate padding
+        padding = int(
+            np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
+        )  # 5% of face width
+
+        # Create a slightly larger convex hull for padding
+        face_outline = landmarks[0:33]
+        hull = cv2.convexHull(face_outline)
+        # Vectorized hull padding — expand each point outward from center
+        center = np.mean(face_outline, axis=0, dtype=np.float32)
+        hull_pts = hull.reshape(-1, 2).astype(np.float32)
+        directions = hull_pts - center
+        norms = np.linalg.norm(directions, axis=1, keepdims=True)
+        norms = np.maximum(norms, 1e-6)  # avoid division by zero
+        directions /= norms
+        hull_padded = (hull_pts + directions * padding).astype(np.int32)
+
+        # Fill the padded convex hull
+        cv2.fillConvexPoly(mask, hull_padded, 255)
+
+        # Smooth the mask edges (GPU-accelerated when available)
+        mask = gpu_gaussian_blur(mask, (5, 5), 3)
+
+    return mask
+
+def create_lower_mouth_mask(
+    face: Face, frame: Frame
+) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    mouth_cutout = None
+    lower_lip_polygon = None
+    mouth_box = (0,0,0,0)
+
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Use outer mouth landmarks (52-71) to capture the full mouth area
+        lower_lip_order = list(range(52, 72))
+        
+        if max(lower_lip_order) >= landmarks.shape[0]:
+            return mask, mouth_cutout, mouth_box, lower_lip_polygon
+
+        lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
+
+        # Calculate the center of the landmarks
+        center = np.mean(lower_lip_landmarks, axis=0)
+
+        # Expand the landmarks outward using the mouth_mask_size
+        mouth_mask_size = getattr(modules.globals, "mouth_mask_size", 0.0) # 0-100 slider
+        expansion_factor = 1 + (mouth_mask_size / 100.0) * 2.5
+
+        # Expand with extra downward bias toward chin
+        offsets = lower_lip_landmarks - center
+        chin_bias = 1 + (mouth_mask_size / 100.0) * 1.5
+        scale_y = np.where(offsets[:, 1] > 0, expansion_factor * chin_bias, expansion_factor)
+        expanded_landmarks = lower_lip_landmarks.copy()
+        expanded_landmarks[:, 0] = center[0] + offsets[:, 0] * expansion_factor
+        expanded_landmarks[:, 1] = center[1] + offsets[:, 1] * scale_y
+
+        # Convert back to integer coordinates
+        expanded_landmarks = expanded_landmarks.astype(np.int32)
+
+        # Calculate bounding box for the expanded lower mouth
+        min_x, min_y = np.min(expanded_landmarks, axis=0)
+        max_x, max_y = np.max(expanded_landmarks, axis=0)
+
+        # Add some padding to the bounding box
+        padding = int((max_x - min_x) * 0.1)  # 10% padding
+        min_x = max(0, min_x - padding)
+        min_y = max(0, min_y - padding)
+        max_x = min(frame.shape[1], max_x + padding)
+        max_y = min(frame.shape[0], max_y + padding)
+
+        # Ensure the bounding box dimensions are valid
+        if max_x <= min_x or max_y <= min_y:
+            if (max_x - min_x) <= 1:
+                max_x = min_x + 1
+            if (max_y - min_y) <= 1:
+                max_y = min_y + 1
+
+        # Create the mask
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        # Shift polygon coordinates relative to the ROI's top-left corner
+        polygon_relative_to_roi = expanded_landmarks - [min_x, min_y]
+        cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255)
+
+        # Apply Gaussian blur to soften the mask edges (GPU-accelerated when available)
+        mask_roi = gpu_gaussian_blur(mask_roi, (15, 15), 5)
+
+        # Place the mask ROI in the full-sized mask
+        mask[min_y:max_y, min_x:max_x] = mask_roi
+
+        # Extract the masked area from the frame
+        mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
+
+        # Return the expanded lower lip polygon in original frame coordinates
+        lower_lip_polygon = expanded_landmarks
+        mouth_box = (min_x, min_y, max_x, max_y)
+
+    return mask, mouth_cutout, mouth_box, lower_lip_polygon
+
+def create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    eyes_cutout = None
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Left eye landmarks (87-96) and right eye landmarks (33-42)
+        left_eye = landmarks[87:96]
+        right_eye = landmarks[33:42]
+        
+        # Calculate centers and dimensions for each eye
+        left_eye_center = np.mean(left_eye, axis=0).astype(np.int32)
+        right_eye_center = np.mean(right_eye, axis=0).astype(np.int32)
+        
+        # Calculate eye dimensions with size adjustment
+        def get_eye_dimensions(eye_points):
+            x_coords = eye_points[:, 0]
+            y_coords = eye_points[:, 1]
+            width = int((np.max(x_coords) - np.min(x_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
+            height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
+            return width, height
+        
+        left_width, left_height = get_eye_dimensions(left_eye)
+        right_width, right_height = get_eye_dimensions(right_eye)
+        
+        # Add extra padding
+        padding = int(max(left_width, right_width) * 0.2)
+        
+        # Calculate bounding box for both eyes
+        min_x = min(left_eye_center[0] - left_width//2, right_eye_center[0] - right_width//2) - padding
+        max_x = max(left_eye_center[0] + left_width//2, right_eye_center[0] + right_width//2) + padding
+        min_y = min(left_eye_center[1] - left_height//2, right_eye_center[1] - right_height//2) - padding
+        max_y = max(left_eye_center[1] + left_height//2, right_eye_center[1] + right_height//2) + padding
+        
+        # Ensure coordinates are within frame bounds
+        min_x = max(0, min_x)
+        min_y = max(0, min_y)
+        max_x = min(frame.shape[1], max_x)
+        max_y = min(frame.shape[0], max_y)
+        
+        # Create mask for the eyes region
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        
+        # Draw ellipses for both eyes
+        left_center = (left_eye_center[0] - min_x, left_eye_center[1] - min_y)
+        right_center = (right_eye_center[0] - min_x, right_eye_center[1] - min_y)
+        
+        # Calculate axes lengths (half of width and height)
+        left_axes = (left_width//2, left_height//2)
+        right_axes = (right_width//2, right_height//2)
+        
+        # Draw filled ellipses
+        cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
+        cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
+        
+        # Apply Gaussian blur to soften mask edges (GPU-accelerated when available)
+        mask_roi = gpu_gaussian_blur(mask_roi, (15, 15), 5)
+        
+        # Place the mask ROI in the full-sized mask
+        mask[min_y:max_y, min_x:max_x] = mask_roi
+        
+        # Extract the masked area from the frame
+        eyes_cutout = frame[min_y:max_y, min_x:max_x].copy()
+        
+        # Create polygon points for visualization
+        def create_ellipse_points(center, axes):
+            t = np.linspace(0, 2*np.pi, 32)
+            x = center[0] + axes[0] * np.cos(t)
+            y = center[1] + axes[1] * np.sin(t)
+            return np.column_stack((x, y)).astype(np.int32)
+        
+        # Generate points for both ellipses
+        left_points = create_ellipse_points((left_eye_center[0], left_eye_center[1]), (left_width//2, left_height//2))
+        right_points = create_ellipse_points((right_eye_center[0], right_eye_center[1]), (right_width//2, right_height//2))
+        
+        # Combine points for both eyes
+        eyes_polygon = np.vstack([left_points, right_points])
+        
+    return mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon
+
+def create_curved_eyebrow(points):
+    if len(points) >= 5:
+        # Sort points by x-coordinate
+        sorted_idx = np.argsort(points[:, 0])
+        sorted_points = points[sorted_idx]
+        
+        # Calculate dimensions
+        x_min, y_min = np.min(sorted_points, axis=0)
+        x_max, y_max = np.max(sorted_points, axis=0)
+        width = x_max - x_min
+        height = y_max - y_min
+        
+        # Create more points for smoother curve
+        num_points = 50
+        x = np.linspace(x_min, x_max, num_points)
+        
+        # Fit quadratic curve through points for more natural arch
+        coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
+        y = np.polyval(coeffs, x)
+        
+        # Increased offsets to create more separation
+        top_offset = height * 0.5  # Increased from 0.3 to shift up more
+        bottom_offset = height * 0.2  # Increased from 0.1 to shift down more
+        
+        # Create smooth curves
+        top_curve = y - top_offset
+        bottom_curve = y + bottom_offset
+        
+        # Create curved endpoints with more pronounced taper
+        end_points = 5
+        start_x = np.linspace(x[0] - width * 0.15, x[0], end_points)  # Increased taper
+        end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points)  # Increased taper
+        
+        # Create tapered ends
+        start_curve = np.column_stack((
+            start_x,
+            np.linspace(bottom_curve[0], top_curve[0], end_points)
+        ))
+        end_curve = np.column_stack((
+            end_x,
+            np.linspace(bottom_curve[-1], top_curve[-1], end_points)
+        ))
+        
+        # Combine all points to form a smooth contour
+        contour_points = np.vstack([
+            start_curve,
+            np.column_stack((x, top_curve)),
+            end_curve,
+            np.column_stack((x[::-1], bottom_curve[::-1]))
+        ])
+        
+        # Add slight padding for better coverage
+        center = np.mean(contour_points, axis=0)
+        vectors = contour_points - center
+        padded_points = center + vectors * 1.2  # Increased padding slightly
+        
+        return padded_points
+    return points
+
+def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    eyebrows_cutout = None
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Left eyebrow landmarks (97-105) and right eyebrow landmarks (43-51)
+        left_eyebrow = landmarks[97:105].astype(np.float32)
+        right_eyebrow = landmarks[43:51].astype(np.float32)
+        
+        # Calculate centers and dimensions for each eyebrow
+        left_center = np.mean(left_eyebrow, axis=0)
+        right_center = np.mean(right_eyebrow, axis=0)
+        
+        # Calculate bounding box with padding adjusted by size
+        all_points = np.vstack([left_eyebrow, right_eyebrow])
+        padding_factor = modules.globals.eyebrows_mask_size
+        min_x = np.min(all_points[:, 0]) - 25 * padding_factor
+        max_x = np.max(all_points[:, 0]) + 25 * padding_factor
+        min_y = np.min(all_points[:, 1]) - 20 * padding_factor
+        max_y = np.max(all_points[:, 1]) + 15 * padding_factor
+        
+        # Ensure coordinates are within frame bounds
+        min_x = max(0, int(min_x))
+        min_y = max(0, int(min_y))
+        max_x = min(frame.shape[1], int(max_x))
+        max_y = min(frame.shape[0], int(max_y))
+        
+        # Create mask for the eyebrows region
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        
+        try:
+            # Convert points to local coordinates
+            left_local = left_eyebrow - [min_x, min_y]
+            right_local = right_eyebrow - [min_x, min_y]
+            
+            def create_curved_eyebrow(points):
+                if len(points) >= 5:
+                    # Sort points by x-coordinate
+                    sorted_idx = np.argsort(points[:, 0])
+                    sorted_points = points[sorted_idx]
+                    
+                    # Calculate dimensions
+                    x_min, y_min = np.min(sorted_points, axis=0)
+                    x_max, y_max = np.max(sorted_points, axis=0)
+                    width = x_max - x_min
+                    height = y_max - y_min
+                    
+                    # Create more points for smoother curve
+                    num_points = 50
+                    x = np.linspace(x_min, x_max, num_points)
+                    
+                    # Fit quadratic curve through points for more natural arch
+                    coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
+                    y = np.polyval(coeffs, x)
+                    
+                    # Increased offsets to create more separation
+                    top_offset = height * 0.5  # Increased from 0.3 to shift up more
+                    bottom_offset = height * 0.2  # Increased from 0.1 to shift down more
+                    
+                    # Create smooth curves
+                    top_curve = y - top_offset
+                    bottom_curve = y + bottom_offset
+                    
+                    # Create curved endpoints with more pronounced taper
+                    end_points = 5
+                    start_x = np.linspace(x[0] - width * 0.15, x[0], end_points)  # Increased taper
+                    end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points)  # Increased taper
+                    
+                    # Create tapered ends
+                    start_curve = np.column_stack((
+                        start_x,
+                        np.linspace(bottom_curve[0], top_curve[0], end_points)
+                    ))
+                    end_curve = np.column_stack((
+                        end_x,
+                        np.linspace(bottom_curve[-1], top_curve[-1], end_points)
+                    ))
+                    
+                    # Combine all points to form a smooth contour
+                    contour_points = np.vstack([
+                        start_curve,
+                        np.column_stack((x, top_curve)),
+                        end_curve,
+                        np.column_stack((x[::-1], bottom_curve[::-1]))
+                    ])
+                    
+                    # Add slight padding for better coverage
+                    center = np.mean(contour_points, axis=0)
+                    vectors = contour_points - center
+                    padded_points = center + vectors * 1.2  # Increased padding slightly
+                    
+                    return padded_points
+                return points
+            
+            # Generate and draw eyebrow shapes
+            left_shape = create_curved_eyebrow(left_local)
+            right_shape = create_curved_eyebrow(right_local)
+            
+            # Apply multi-stage blurring for natural feathering (GPU-accelerated when available)
+            # First, strong Gaussian blur for initial softening
+            mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7)
+            
+            # Second, medium blur for transition areas
+            mask_roi = gpu_gaussian_blur(mask_roi, (11, 11), 3)
+            
+            # Finally, light blur for fine details
+            mask_roi = gpu_gaussian_blur(mask_roi, (5, 5), 1)
+            
+            # Normalize mask values
+            mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
+            
+            # Place the mask ROI in the full-sized mask
+            mask[min_y:max_y, min_x:max_x] = mask_roi
+            
+            # Extract the masked area from the frame
+            eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
+            
+            # Combine points for visualization
+            eyebrows_polygon = np.vstack([
+                left_shape + [min_x, min_y],
+                right_shape + [min_x, min_y]
+            ]).astype(np.int32)
+            
+        except Exception as e:
+            # Fallback to simple polygons if curve fitting fails
+            left_local = left_eyebrow - [min_x, min_y]
+            right_local = right_eyebrow - [min_x, min_y]
+            cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
+            cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
+            mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7)
+            mask[min_y:max_y, min_x:max_x] = mask_roi
+            eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
+            eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
+        
+    return mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon
+
+def apply_mask_area(
+    frame: np.ndarray,
+    cutout: np.ndarray,
+    box: tuple,
+    face_mask: np.ndarray,
+    polygon: np.ndarray,
+) -> np.ndarray:
+    min_x, min_y, max_x, max_y = box
+    box_width = max_x - min_x
+    box_height = max_y - min_y
+
+    if (
+        cutout is None
+        or box_width is None
+        or box_height is None
+        or face_mask is None
+        or polygon is None
+    ):
+        return frame
+
+    try:
+        resized_cutout = gpu_resize(cutout, (box_width, box_height))
+        roi = frame[min_y:max_y, min_x:max_x]
+
+        if roi.shape != resized_cutout.shape:
+            resized_cutout = gpu_resize(
+                resized_cutout, (roi.shape[1], roi.shape[0])
+            )
+
+        color_corrected_area = apply_color_transfer(resized_cutout, roi)
+
+        # Create mask for the area
+        polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
+        
+        # Split points for left and right parts if needed
+        if len(polygon) > 50:  # Arbitrary threshold to detect if we have multiple parts
+            mid_point = len(polygon) // 2
+            left_points = polygon[:mid_point] - [min_x, min_y]
+            right_points = polygon[mid_point:] - [min_x, min_y]
+            cv2.fillPoly(polygon_mask, [left_points], 255)
+            cv2.fillPoly(polygon_mask, [right_points], 255)
+        else:
+            adjusted_polygon = polygon - [min_x, min_y]
+            cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
+
+        # Apply strong initial feathering (GPU-accelerated when available)
+        polygon_mask = gpu_gaussian_blur(polygon_mask, (21, 21), 7)
+
+        # Apply additional feathering
+        feather_amount = min(
+            30,
+            box_width // modules.globals.mask_feather_ratio,
+            box_height // modules.globals.mask_feather_ratio,
+        )
+        feathered_mask = cv2.GaussianBlur(
+            polygon_mask.astype(np.float32), (0, 0), feather_amount
+        )
+        max_val = feathered_mask.max()
+        if max_val > 1e-6:
+            feathered_mask *= np.float32(1.0 / max_val)
+
+        # Apply additional smoothing to the mask edges
+        feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
+
+        face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
+        combined_mask = feathered_mask * (face_mask_roi.astype(np.float32) * np.float32(1.0 / 255.0))
+
+        combined_mask_3ch = combined_mask[:, :, np.newaxis]
+        inv_mask = np.float32(1.0) - combined_mask_3ch
+        blended = (
+            color_corrected_area * combined_mask_3ch + roi * inv_mask
+        ).astype(np.uint8)
+
+        # Apply face mask to blended result
+        face_mask_f32 = face_mask_roi[:, :, np.newaxis].astype(np.float32) * np.float32(1.0 / 255.0)
+        face_mask_3channel = np.broadcast_to(face_mask_f32, blended.shape)
+        final_blend = blended * face_mask_3channel + roi * (np.float32(1.0) - face_mask_3channel)
+
+        frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
+    except Exception as e:
+        pass
+
+    return frame
+
+def draw_mask_visualization(
+    frame: Frame,
+    mask_data: tuple,
+    label: str,
+    draw_method: str = "polygon"
+) -> Frame:
+    mask, cutout, (min_x, min_y, max_x, max_y), polygon = mask_data
+
+    vis_frame = frame.copy()
+
+    # Ensure coordinates are within frame bounds
+    height, width = vis_frame.shape[:2]
+    min_x, min_y = max(0, min_x), max(0, min_y)
+    max_x, max_y = min(width, max_x), min(height, max_y)
+
+    if draw_method == "ellipse" and len(polygon) > 50:  # For eyes
+        # Split points for left and right parts
+        mid_point = len(polygon) // 2
+        left_points = polygon[:mid_point]
+        right_points = polygon[mid_point:]
+        
+        try:
+            # Fit ellipses to points - need at least 5 points
+            if len(left_points) >= 5 and len(right_points) >= 5:
+                # Convert points to the correct format for ellipse fitting
+                left_points = left_points.astype(np.float32)
+                right_points = right_points.astype(np.float32)
+                
+                # Fit ellipses
+                left_ellipse = cv2.fitEllipse(left_points)
+                right_ellipse = cv2.fitEllipse(right_points)
+                
+                # Draw the ellipses
+                cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
+                cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
+        except Exception as e:
+            # If ellipse fitting fails, draw simple rectangles as fallback
+            left_rect = cv2.boundingRect(left_points)
+            right_rect = cv2.boundingRect(right_points)
+            cv2.rectangle(vis_frame, 
+                        (left_rect[0], left_rect[1]), 
+                        (left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]), 
+                        (0, 255, 0), 2)
+            cv2.rectangle(vis_frame,
+                        (right_rect[0], right_rect[1]),
+                        (right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
+                        (0, 255, 0), 2)
+    else:  # For mouth and eyebrows
+        # Draw the polygon
+        if len(polygon) > 50:  # If we have multiple parts
+            mid_point = len(polygon) // 2
+            left_points = polygon[:mid_point]
+            right_points = polygon[mid_point:]
+            cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
+            cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
+        else:
+            cv2.polylines(vis_frame, [polygon], True, (0, 255, 0), 2, cv2.LINE_AA)
+
+    # Add label
+    cv2.putText(
+        vis_frame,
+        label,
+        (min_x, min_y - 10),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.5,
+        (255, 255, 255),
+        1,
+    )
+
+    return vis_frame

modules/processors/frame/super_resolution.py

@@ -1,197 +0,0 @@
-import threading
-import traceback
-from typing import Any, List
-import cv2
-
-import os
-
-import modules.globals
-import modules.processors.frame.core
-from modules.core import update_status
-from modules.face_analyser import get_one_face
-from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
-import numpy as np
-
-NAME = 'DLC.SUPER-RESOLUTION'
-THREAD_SEMAPHORE = threading.Semaphore()
-
-# Singleton class for Super-Resolution
-class SuperResolutionModel:
-    _instance = None
-    _lock = threading.Lock()
-
-    def __init__(self, sr_model_path: str = f'ESPCN_x{modules.globals.sr_scale_factor}.pb'):
-        if SuperResolutionModel._instance is not None:
-            raise Exception("This class is a singleton!")
-        self.sr = cv2.dnn_superres.DnnSuperResImpl_create()
-        self.model_path = os.path.join(resolve_relative_path('../models'), sr_model_path)
-        if not os.path.exists(self.model_path):
-            raise FileNotFoundError(f"Super-resolution model not found at {self.model_path}")
-        try:
-            self.sr.readModel(self.model_path)
-            self.sr.setModel("espcn", modules.globals.sr_scale_factor)  # Using ESPCN with 2,3 or 4x upscaling
-        except Exception as e:
-            print(f"Error during super-resolution model initialization: {e}")
-            raise e
-
-    @classmethod
-    def get_instance(cls, sr_model_path: str = f'ESPCN_x{modules.globals.sr_scale_factor}.pb'):
-        if cls._instance is None:
-            with cls._lock:
-                if cls._instance is None:
-                    try:
-                        cls._instance = cls(sr_model_path)
-                    except Exception as e:
-                        raise RuntimeError(f"Failed to initialize SuperResolution: {str(e)}")
-        return cls._instance
-
-
-def pre_check() -> bool:
-    """
-    Checks and downloads necessary models before starting the face swapper.
-    """
-    download_directory_path = resolve_relative_path('../models')
-    # Download the super-resolution model as well
-    conditional_download(download_directory_path, [
-        f'https://huggingface.co/spaces/PabloGabrielSch/AI_Resolution_Upscaler_And_Resizer/resolve/bcd13b766a9499196e8becbe453c4a848673b3b6/models/ESPCN_x{modules.globals.sr_scale_factor}.pb'
-    ])
-    return True
-
-def pre_start() -> bool:
-    if not is_image(modules.globals.source_path):
-        update_status('Select an image for source path.', NAME)
-        return False
-    elif not get_one_face(cv2.imread(modules.globals.source_path)):
-        update_status('No face detected in the source path.', NAME)
-        return False
-    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
-        update_status('Select an image or video for target path.', NAME)
-        return False
-    return True
-
-
-def apply_super_resolution(image: np.ndarray) -> np.ndarray:
-    """
-    Applies super-resolution to the given image using the provided super-resolver.
-
-    Args:
-        image (np.ndarray): The input image to enhance.
-        sr_model_path (str): ESPCN model path for super-resolution.
-
-    Returns:
-        np.ndarray: The super-resolved image.
-    """
-    with THREAD_SEMAPHORE:
-        sr_model = SuperResolutionModel.get_instance()
-
-        if sr_model is None:
-            print("Super-resolution model is not initialized.")
-            return image
-        try:
-            upscaled_image = sr_model.sr.upsample(image)
-            return upscaled_image
-        except Exception as e:
-            print(f"Error during super-resolution: {e}")
-            return image
-
-
-def process_frame(frame: np.ndarray) -> np.ndarray:
-    """
-    Processes a single frame by swapping the source face into detected target faces.
-
-    Args:
-
-        frame (np.ndarray): The target frame image.
-
-    Returns:
-        np.ndarray: The processed frame with swapped faces.
-    """
-
-    # Apply super-resolution to the entire frame
-    frame = apply_super_resolution(frame)
-
-    return frame
-
-def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
-    """
-    Processes multiple frames by swapping the source face into each target frame.
-
-    Args:
-        source_path (str): Path to the source image.
-        temp_frame_paths (List[str]): List of paths to target frame images.
-        progress (Any, optional): Progress tracker. Defaults to None.
-    """
-    for idx, temp_frame_path in enumerate(temp_frame_paths):
-        frame = cv2.imread(temp_frame_path)
-        if frame is None:
-            print(f"Failed to load frame from {temp_frame_path}")
-            continue
-        try:
-            result = process_frame(frame)
-            cv2.imwrite(temp_frame_path, result)
-        except Exception as exception:
-            traceback.print_exc()
-            print(f"Error processing frame {temp_frame_path}: {exception}")
-        if progress:
-            progress.update(1)
-
-def upscale_image(image: np.ndarray, scaling_factor: int = 2) -> np.ndarray:
-    """
-    Upscales the given image by the specified scaling factor.
-
-    Args:
-        image (np.ndarray): The input image to upscale.
-        scaling_factor (int): The factor by which to upscale the image.
-
-    Returns:
-        np.ndarray: The upscaled image.
-    """
-    height, width = image.shape[:2]
-    new_size = (width * scaling_factor, height * scaling_factor)
-    upscaled_image = cv2.resize(image, new_size, interpolation=cv2.INTER_CUBIC)
-    return upscaled_image
-
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    """
-    Processes a single image by swapping the source face into the target image.
-
-    Args:
-        source_path (str): Path to the source image.
-        target_path (str): Path to the target image.
-        output_path (str): Path to save the output image.
-    """
-    source_image = cv2.imread(source_path)
-    if source_image is None:
-        print(f"Failed to load source image from {source_path}")
-        return
-
-    # Upscale the source image for better quality before face detection
-    source_image_upscaled = upscale_image(source_image, scaling_factor=2)
-
-    # Detect source face from the upscaled image
-    source_face = get_one_face(source_image_upscaled)
-    if source_face is None:
-        print("No source face detected.")
-        return
-
-    target_frame = cv2.imread(target_path)
-    if target_frame is None:
-        print(f"Failed to load target image from {target_path}")
-        return
-
-    # Process the frame
-    result = process_frame(target_frame)
-
-    # Save the processed frame
-    cv2.imwrite(output_path, result)
-
-
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    """
-    Processes a video by swapping the source face into each frame.
-
-    Args:
-        source_path (str): Path to the source image.
-        temp_frame_paths (List[str]): List of paths to video frame images.
-    """
-    modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)

modules/run.py

@@ -0,0 +1,9 @@
+#!/usr/bin/env python3
+
+# Import the tkinter fix to patch the ScreenChanged error (module patches Tk on import)
+import tkinter_fix  # noqa: F401
+
+import core
+
+if __name__ == '__main__':
+    core.run()

modules/tkinter_fix.py

@@ -0,0 +1,26 @@
+import tkinter
+
+# Only needs to be imported once at the beginning of the application
+def apply_patch():
+    # Create a monkey patch for the internal _tkinter module
+    original_init = tkinter.Tk.__init__
+    
+    def patched_init(self, *args, **kwargs):
+        # Call the original init
+        original_init(self, *args, **kwargs)
+        
+        # Define the missing ::tk::ScreenChanged procedure
+        self.tk.eval("""
+        if {[info commands ::tk::ScreenChanged] == ""} {
+            proc ::tk::ScreenChanged {args} {
+                # Do nothing
+                return
+            }
+        }
+        """)
+    
+    # Apply the monkey patch
+    tkinter.Tk.__init__ = patched_init
+
+# Apply the patch automatically when this module is imported
+apply_patch() 

modules/ui.json

@@ -1,57 +1,76 @@
 {
   "CTk": {
-    "fg_color": ["#FFFFFF", "#2D2D2D"]
+    "fg_color": ["gray95", "gray10"]
   },
   "CTkToplevel": {
-    "fg_color": ["#FFFFFF", "#2D2D2D"]
+    "fg_color": ["gray95", "gray10"]
   },
   "CTkFrame": {
     "corner_radius": 0,
     "border_width": 0,
-    "fg_color": ["#F0F0F0", "#3C3C3C"],
-    "top_fg_color": ["#E0E0E0", "#4B4B4B"],
-    "border_color": ["#B0B0B0", "#5A5A5A"]
+    "fg_color": ["gray90", "gray13"],
+    "top_fg_color": ["gray85", "gray16"],
+    "border_color": ["gray65", "gray28"]
   },
   "CTkButton": {
     "corner_radius": 0,
     "border_width": 0,
-    "fg_color": ["#007ACC", "#007ACC"],
-    "hover_color": ["#005EA3", "#005EA3"],
-    "border_color": ["#004C8A", "#004C8A"],
-    "text_color": ["#FFFFFF", "#FFFFFF"],
+    "fg_color": ["#2aa666", "#1f538d"],
+    "hover_color": ["#3cb666", "#14375e"],
+    "border_color": ["#3e4a40", "#949A9F"],
+    "text_color": ["#f3faf6", "#f3faf6"],
     "text_color_disabled": ["gray74", "gray60"]
   },
   "CTkLabel": {
     "corner_radius": 0,
     "fg_color": "transparent",
-    "text_color": ["#000000", "#FFFFFF"]
+    "text_color": ["gray14", "gray84"]
   },
   "CTkEntry": {
     "corner_radius": 0,
     "border_width": 2,
-    "fg_color": ["#FFFFFF", "#333333"],
-    "border_color": ["#A0A0A0", "#5A5A5A"],
-    "text_color": ["#000000", "#FFFFFF"],
+    "fg_color": ["#F9F9FA", "#343638"],
+    "border_color": ["#979DA2", "#565B5E"],
+    "text_color": ["gray14", "gray84"],
     "placeholder_text_color": ["gray52", "gray62"]
   },
+  "CTkCheckbox": {
+    "corner_radius": 0,
+    "border_width": 3,
+    "fg_color": ["#2aa666", "#1f538d"],
+    "border_color": ["#3e4a40", "#949A9F"],
+    "hover_color": ["#3cb666", "#14375e"],
+    "checkmark_color": ["#f3faf6", "gray90"],
+    "text_color": ["gray14", "gray84"],
+    "text_color_disabled": ["gray60", "gray45"]
+  },
   "CTkSwitch": {
     "corner_radius": 1000,
     "border_width": 3,
     "button_length": 0,
     "fg_color": ["#939BA2", "#4A4D50"],
     "progress_color": ["#2aa666", "#1f538d"],
-    "button_color": ["#444444", "#D5D9DE"],
-    "button_hover_color": ["#333333", "#FFFFFF"],
-    "text_color": ["#000000", "#FFFFFF"],
+    "button_color": ["gray36", "#D5D9DE"],
+    "button_hover_color": ["gray20", "gray100"],
+    "text_color": ["gray14", "gray84"],
     "text_color_disabled": ["gray60", "gray45"]
   },
-  "CTkOptionMenu": {
-    "corner_radius": 0,
+  "CTkRadiobutton": {
+    "corner_radius": 1000,
+    "border_width_checked": 6,
+    "border_width_unchecked": 3,
     "fg_color": ["#2aa666", "#1f538d"],
-    "button_color": ["#3cb666", "#14375e"],
-    "button_hover_color": ["#234567", "#1e2c40"],
-    "text_color": ["#FFFFFF", "#FFFFFF"],
-    "text_color_disabled": ["gray74", "gray60"]
+    "border_color": ["#3e4a40", "#949A9F"],
+    "hover_color": ["#3cb666", "#14375e"],
+    "text_color": ["gray14", "gray84"],
+    "text_color_disabled": ["gray60", "gray45"]
+  },
+  "CTkProgressBar": {
+    "corner_radius": 1000,
+    "border_width": 0,
+    "fg_color": ["#939BA2", "#4A4D50"],
+    "progress_color": ["#2aa666", "#1f538d"],
+    "border_color": ["gray", "gray"]
   },
   "CTkSlider": {
     "corner_radius": 1000,
@@ -63,6 +82,59 @@
     "button_color": ["#2aa666", "#1f538d"],
     "button_hover_color": ["#3cb666", "#14375e"]
   },
+  "CTkOptionMenu": {
+    "corner_radius": 0,
+    "fg_color": ["#2aa666", "#1f538d"],
+    "button_color": ["#3cb666", "#14375e"],
+    "button_hover_color": ["#234567", "#1e2c40"],
+    "text_color": ["#f3faf6", "#f3faf6"],
+    "text_color_disabled": ["gray74", "gray60"]
+  },
+  "CTkComboBox": {
+    "corner_radius": 0,
+    "border_width": 2,
+    "fg_color": ["#F9F9FA", "#343638"],
+    "border_color": ["#979DA2", "#565B5E"],
+    "button_color": ["#979DA2", "#565B5E"],
+    "button_hover_color": ["#6E7174", "#7A848D"],
+    "text_color": ["gray14", "gray84"],
+    "text_color_disabled": ["gray50", "gray45"]
+  },
+  "CTkScrollbar": {
+    "corner_radius": 1000,
+    "border_spacing": 4,
+    "fg_color": "transparent",
+    "button_color": ["gray55", "gray41"],
+    "button_hover_color": ["gray40", "gray53"]
+  },
+  "CTkSegmentedButton": {
+    "corner_radius": 0,
+    "border_width": 2,
+    "fg_color": ["#979DA2", "gray29"],
+    "selected_color": ["#2aa666", "#1f538d"],
+    "selected_hover_color": ["#3cb666", "#14375e"],
+    "unselected_color": ["#979DA2", "gray29"],
+    "unselected_hover_color": ["gray70", "gray41"],
+    "text_color": ["#f3faf6", "#f3faf6"],
+    "text_color_disabled": ["gray74", "gray60"]
+  },
+  "CTkTextbox": {
+    "corner_radius": 0,
+    "border_width": 0,
+    "fg_color": ["gray100", "gray20"],
+    "border_color": ["#979DA2", "#565B5E"],
+    "text_color": ["gray14", "gray84"],
+    "scrollbar_button_color": ["gray55", "gray41"],
+    "scrollbar_button_hover_color": ["gray40", "gray53"]
+  },
+  "CTkScrollableFrame": {
+    "label_fg_color": ["gray80", "gray21"]
+  },
+  "DropdownMenu": {
+    "fg_color": ["gray90", "gray20"],
+    "hover_color": ["gray75", "gray28"],
+    "text_color": ["gray14", "gray84"]
+  },
   "CTkFont": {
     "macOS": {
       "family": "Avenir",
@@ -80,12 +152,7 @@
       "weight": "normal"
     }
   },
-  "DropdownMenu": {
-    "fg_color": ["#FFFFFF", "#2D2D2D"],
-    "hover_color": ["#E0E0E0", "#4B4B4B"],
-    "text_color": ["#000000", "#FFFFFF"]
-  },
   "URL": {
-    "text_color": ["#007ACC", "#1E90FF"]
+    "text_color": ["gray74", "gray60"]
   }
 }

modules/ui_tooltip.py

@@ -0,0 +1,74 @@
+"""Lightweight hover tooltip for CustomTkinter widgets."""
+
+import customtkinter as ctk
+
+
+class ToolTip:
+    """Show a floating tooltip popup when the user hovers over a widget.
+
+    Usage:
+        ToolTip(my_button, "Helpful description text")
+    """
+
+    def __init__(self, widget: ctk.CTkBaseClass, text: str, delay: int = 500):
+        self._widget = widget
+        self._text = text
+        self._delay = delay
+        self._tooltip_window = None
+        self._after_id = None
+
+        widget.bind("<Enter>", self._schedule_show, add="+")
+        widget.bind("<Leave>", self._hide, add="+")
+
+    def _schedule_show(self, event=None):
+        self._cancel()
+        self._after_id = self._widget.after(self._delay, self._show)
+
+    def _show(self):
+        if self._tooltip_window is not None:
+            return
+
+        x = self._widget.winfo_rootx() + 20
+        y = self._widget.winfo_rooty() + self._widget.winfo_height() + 5
+
+        self._tooltip_window = tw = ctk.CTkToplevel(self._widget)
+        tw.withdraw()
+        tw.overrideredirect(True)
+
+        label = ctk.CTkLabel(
+            tw,
+            text=self._text,
+            fg_color="#333333",
+            text_color="#EEEEEE",
+            corner_radius=6,
+            padx=8,
+            pady=4,
+        )
+        label.pack()
+
+        tw.update_idletasks()
+
+        # Clamp to screen bounds
+        screen_w = tw.winfo_screenwidth()
+        screen_h = tw.winfo_screenheight()
+        tip_w = tw.winfo_reqwidth()
+        tip_h = tw.winfo_reqheight()
+
+        if x + tip_w > screen_w:
+            x = screen_w - tip_w - 5
+        if y + tip_h > screen_h:
+            y = self._widget.winfo_rooty() - tip_h - 5
+
+        tw.geometry(f"+{x}+{y}")
+        tw.deiconify()
+
+    def _hide(self, event=None):
+        self._cancel()
+        if self._tooltip_window is not None:
+            self._tooltip_window.destroy()
+            self._tooltip_window = None
+
+    def _cancel(self):
+        if self._after_id is not None:
+            self._widget.after_cancel(self._after_id)
+            self._after_id = None

modules/utilities.py

@@ -5,133 +5,344 @@ import platform
 import shutil
 import ssl
 import subprocess
-import urllib.request
+import urllib
 from pathlib import Path
 from typing import List, Any
 from tqdm import tqdm
 
 import modules.globals
 
-TEMP_FILE = 'temp.mp4'
-TEMP_DIRECTORY = 'temp'
+TEMP_FILE = "temp.mp4"
+TEMP_DIRECTORY = "temp"
 
-# Monkey patch SSL for macOS to handle issues with some HTTPS requests
-if platform.system().lower() == 'darwin':
-    ssl._create_default_https_context = ssl._create_unverified_context
 
 def run_ffmpeg(args: List[str]) -> bool:
-    commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-loglevel', modules.globals.log_level]
+    """Run ffmpeg with hardware acceleration and optimized settings."""
+    commands = [
+        "ffmpeg",
+        "-hide_banner",
+        "-hwaccel", "auto",  # Auto-detect hardware acceleration
+        "-hwaccel_output_format", "auto",  # Use hardware format when possible
+        "-threads", str(modules.globals.execution_threads or 0),  # 0 = auto-detect optimal thread count
+        "-loglevel", modules.globals.log_level,
+    ]
     commands.extend(args)
     try:
         subprocess.check_output(commands, stderr=subprocess.STDOUT)
         return True
-    except subprocess.CalledProcessError as e:
-        print(f"FFmpeg error: {e.output.decode()}")
+    except subprocess.CalledProcessError as error:
+        output = error.output.decode(errors="ignore").strip()
+        if output:
+            print(output)
+    except Exception as error:
+        print(f"ffmpeg execution failed: {error}")
     return False
 
+
 def detect_fps(target_path: str) -> float:
     command = [
-        'ffprobe', '-v', 'error', '-select_streams', 'v:0', 
-        '-show_entries', 'stream=r_frame_rate', 
-        '-of', 'default=noprint_wrappers=1:nokey=1', target_path
+        "ffprobe",
+        "-v",
+        "error",
+        "-select_streams",
+        "v:0",
+        "-show_entries",
+        "stream=r_frame_rate",
+        "-of",
+        "default=noprint_wrappers=1:nokey=1",
+        target_path,
     ]
+    output = subprocess.check_output(command).decode().strip().split("/")
     try:
-        output = subprocess.check_output(command).decode().strip().split('/')
         numerator, denominator = map(int, output)
         return numerator / denominator
-    except (subprocess.CalledProcessError, ValueError):
-        print("Failed to detect FPS, defaulting to 30.0 FPS.")
+    except Exception:
+        pass
     return 30.0
 
-def extract_frames(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    create_temp(target_path)
-    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
 
-def create_video(target_path: str, fps: float = 30.0) -> None:
+def extract_frames(target_path: str) -> None:
+    """Extract frames with hardware acceleration and optimized settings."""
+    temp_directory_path = get_temp_directory_path(target_path)
+    
+    # Write a contiguous image sequence so the later "%04d.png" input pattern
+    # used during encoding can consume every frame reliably.
+    run_ffmpeg(
+        [
+            "-i", target_path,
+            "-vf", "format=rgb24",  # Use video filter for format conversion (faster)
+            "-vsync", "0",  # Prevent frame duplication
+            os.path.join(temp_directory_path, "%04d.png"),
+        ]
+    )
+
+
+def create_video(target_path: str, fps: float = 30.0) -> bool:
+    """Create video with hardware-accelerated encoding and optimized settings."""
     temp_output_path = get_temp_output_path(target_path)
     temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg([
-        '-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.png'), 
-        '-c:v', modules.globals.video_encoder, 
-        '-crf', str(modules.globals.video_quality), 
-        '-pix_fmt', 'yuv420p', 
-        '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', 
-        '-y', temp_output_path
+    
+    # Determine optimal encoder based on available hardware
+    encoder = modules.globals.video_encoder
+    encoder_options = []
+    
+    # GPU-accelerated encoding options
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+        # NVIDIA GPU encoding
+        if encoder == 'libx264':
+            encoder = 'h264_nvenc'
+            encoder_options = [
+                "-preset", "p7",  # Highest quality preset for NVENC
+                "-tune", "hq",  # High quality tuning
+                "-rc", "vbr",  # Variable bitrate
+                "-cq", str(modules.globals.video_quality),  # Quality level
+                "-b:v", "0",  # Let CQ control bitrate
+                "-multipass", "fullres",  # Two-pass encoding for better quality
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_nvenc'
+            encoder_options = [
+                "-preset", "p7",
+                "-tune", "hq",
+                "-rc", "vbr",
+                "-cq", str(modules.globals.video_quality),
+                "-b:v", "0",
+            ]
+    elif 'DmlExecutionProvider' in modules.globals.execution_providers:
+        # AMD/Intel GPU encoding (DirectML on Windows)
+        if encoder == 'libx264':
+            # Try AMD AMF encoder
+            encoder = 'h264_amf'
+            encoder_options = [
+                "-quality", "quality",  # Quality mode
+                "-rc", "vbr_latency",
+                "-qp_i", str(modules.globals.video_quality),
+                "-qp_p", str(modules.globals.video_quality),
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_amf'
+            encoder_options = [
+                "-quality", "quality",
+                "-rc", "vbr_latency",
+                "-qp_i", str(modules.globals.video_quality),
+                "-qp_p", str(modules.globals.video_quality),
+            ]
+    else:
+        # CPU encoding with optimized settings
+        if encoder == 'libx264':
+            encoder_options = [
+                "-preset", "medium",  # Balance speed/quality
+                "-crf", str(modules.globals.video_quality),
+                "-tune", "film",  # Optimize for film content
+            ]
+        elif encoder == 'libx265':
+            encoder_options = [
+                "-preset", "medium",
+                "-crf", str(modules.globals.video_quality),
+                "-x265-params", "log-level=error",
+            ]
+        elif encoder == 'libvpx-vp9':
+            encoder_options = [
+                "-crf", str(modules.globals.video_quality),
+                "-b:v", "0",  # Constant quality mode
+                "-cpu-used", "2",  # Speed vs quality (0-5, lower=slower/better)
+            ]
+    
+    # Build ffmpeg command
+    ffmpeg_args = [
+        "-r", str(fps),
+        "-i", os.path.join(temp_directory_path, "%04d.png"),
+        "-c:v", encoder,
+    ]
+    
+    # Add encoder-specific options
+    ffmpeg_args.extend(encoder_options)
+    
+    # Add common options
+    ffmpeg_args.extend([
+        "-pix_fmt", "yuv420p",
+        "-movflags", "+faststart",  # Enable fast start for web playback
+        "-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
+        "-y",
+        temp_output_path,
     ])
+    
+    # Try with hardware encoder first, fallback to software if it fails
+    success = run_ffmpeg(ffmpeg_args)
+    
+    if not success and encoder in ['h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf']:
+        # Fallback to software encoding
+        print(f"Hardware encoding with {encoder} failed, falling back to software encoding...")
+        fallback_encoder = 'libx264' if 'h264' in encoder else 'libx265'
+        ffmpeg_args_fallback = [
+            "-r", str(fps),
+            "-i", os.path.join(temp_directory_path, "%04d.png"),
+            "-c:v", fallback_encoder,
+            "-preset", "medium",
+            "-crf", str(modules.globals.video_quality),
+            "-pix_fmt", "yuv420p",
+            "-movflags", "+faststart",
+            "-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
+            "-y",
+            temp_output_path,
+        ]
+        success = run_ffmpeg(ffmpeg_args_fallback)
+    return success and os.path.isfile(temp_output_path)
+
 
 def restore_audio(target_path: str, output_path: str) -> None:
     temp_output_path = get_temp_output_path(target_path)
-    done = run_ffmpeg([
-        '-i', temp_output_path, '-i', target_path, 
-        '-c:v', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-y', output_path
-    ])
+    done = run_ffmpeg(
+        [
+            "-i",
+            temp_output_path,
+            "-i",
+            target_path,
+            "-c:v",
+            "copy",
+            "-map",
+            "0:v:0",
+            "-map",
+            "1:a:0",
+            "-y",
+            output_path,
+        ]
+    )
     if not done:
         move_temp(target_path, output_path)
 
+
 def get_temp_frame_paths(target_path: str) -> List[str]:
     temp_directory_path = get_temp_directory_path(target_path)
-    return glob.glob(os.path.join(glob.escape(temp_directory_path), '*.png'))
+    return glob.glob((os.path.join(glob.escape(temp_directory_path), "*.png")))
+
 
 def get_temp_directory_path(target_path: str) -> str:
-    target_name = Path(target_path).stem
-    target_directory_path = Path(target_path).parent
-    return str(target_directory_path / TEMP_DIRECTORY / target_name)
+    target_name, _ = os.path.splitext(os.path.basename(target_path))
+    target_directory_path = os.path.dirname(target_path)
+    return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
+
 
 def get_temp_output_path(target_path: str) -> str:
     temp_directory_path = get_temp_directory_path(target_path)
-    return str(Path(temp_directory_path) / TEMP_FILE)
+    return os.path.join(temp_directory_path, TEMP_FILE)
 
-def normalize_output_path(source_path: str, target_path: str, output_path: str) -> str:
-    if source_path and target_path and os.path.isdir(output_path):
-        source_name = Path(source_path).stem
-        target_name = Path(target_path).stem
-        target_extension = Path(target_path).suffix
-        return str(Path(output_path) / f"{source_name}-{target_name}{target_extension}")
+
+def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
+    if source_path and target_path:
+        source_name, _ = os.path.splitext(os.path.basename(source_path))
+        target_name, target_extension = os.path.splitext(os.path.basename(target_path))
+        if os.path.isdir(output_path):
+            return os.path.join(
+                output_path, source_name + "-" + target_name + target_extension
+            )
     return output_path
 
+
 def create_temp(target_path: str) -> None:
     temp_directory_path = get_temp_directory_path(target_path)
     Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
 
+
 def move_temp(target_path: str, output_path: str) -> None:
     temp_output_path = get_temp_output_path(target_path)
     if os.path.isfile(temp_output_path):
+        if os.path.isfile(output_path):
+            os.remove(output_path)
         shutil.move(temp_output_path, output_path)
 
+
 def clean_temp(target_path: str) -> None:
     temp_directory_path = get_temp_directory_path(target_path)
-    parent_directory_path = Path(temp_directory_path).parent
+    parent_directory_path = os.path.dirname(temp_directory_path)
     if not modules.globals.keep_frames and os.path.isdir(temp_directory_path):
         shutil.rmtree(temp_directory_path)
-    if parent_directory_path.exists() and not list(parent_directory_path.iterdir()):
-        parent_directory_path.rmdir()
+    if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
+        os.rmdir(parent_directory_path)
+
 
 def has_image_extension(image_path: str) -> bool:
-    return image_path.lower().endswith(('png', 'jpg', 'jpeg'))
+    return image_path.lower().endswith(("png", "jpg", "jpeg"))
+
 
 def is_image(image_path: str) -> bool:
     if image_path and os.path.isfile(image_path):
         mimetype, _ = mimetypes.guess_type(image_path)
-        return mimetype and mimetype.startswith('image/')
+        return bool(mimetype and mimetype.startswith("image/"))
     return False
 
+
 def is_video(video_path: str) -> bool:
     if video_path and os.path.isfile(video_path):
         mimetype, _ = mimetypes.guess_type(video_path)
-        return mimetype and mimetype.startswith('video/')
+        return bool(mimetype and mimetype.startswith("video/"))
     return False
 
+
 def conditional_download(download_directory_path: str, urls: List[str]) -> None:
-    download_directory = Path(download_directory_path)
-    download_directory.mkdir(parents=True, exist_ok=True)
+    if not os.path.exists(download_directory_path):
+        os.makedirs(download_directory_path)
     for url in urls:
-        download_file_path = download_directory / Path(url).name
-        if not download_file_path.exists():
-            with urllib.request.urlopen(url) as request:
-                total = int(request.headers.get('Content-Length', 0))
-                with tqdm(total=total, desc='Downloading', unit='B', unit_scale=True, unit_divisor=1024) as progress:
-                    urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size))
+        download_file_path = os.path.join(
+            download_directory_path, os.path.basename(url)
+        )
+        if not os.path.exists(download_file_path):
+            request = urllib.request.Request(url)
+            
+            # Create a specific SSL context for macOS to avoid globally disabling verification
+            ctx = None
+            if platform.system().lower() == "darwin":
+                ctx = ssl._create_unverified_context()
+                
+            response = urllib.request.urlopen(request, context=ctx)
+            total = int(response.headers.get("Content-Length", 0))
+            with tqdm(
+                total=total,
+                desc="Downloading",
+                unit="B",
+                unit_scale=True,
+                unit_divisor=1024,
+            ) as progress:
+                with open(download_file_path, "wb") as f:
+                    while True:
+                        buffer = response.read(8192)
+                        if not buffer:
+                            break
+                        f.write(buffer)
+                        progress.update(len(buffer))
+
 
 def resolve_relative_path(path: str) -> str:
-    return str(Path(__file__).parent / path)
+    return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
+
+
+def get_video_dimensions(target_path: str) -> tuple:
+    """Get video width and height using ffprobe."""
+    command = [
+        "ffprobe", "-v", "error",
+        "-select_streams", "v:0",
+        "-show_entries", "stream=width,height",
+        "-of", "csv=p=0:s=x",
+        target_path,
+    ]
+    output = subprocess.check_output(command).decode().strip()
+    width, height = map(int, output.split("x"))
+    return width, height
+
+
+def estimate_frame_count(target_path: str, fps: float = None) -> int:
+    """Estimate total frame count from video duration and fps."""
+    if fps is None:
+        fps = detect_fps(target_path)
+    command = [
+        "ffprobe", "-v", "error",
+        "-show_entries", "format=duration",
+        "-of", "csv=p=0",
+        target_path,
+    ]
+    try:
+        output = subprocess.check_output(command).decode().strip()
+        duration = float(output)
+        return int(duration * fps)
+    except Exception:
+        return 0

modules/video_capture.py

@@ -0,0 +1,159 @@
+import cv2
+import numpy as np
+import time
+from typing import Optional, Tuple, Callable
+import platform
+import threading
+
+# Only import Windows-specific library if on Windows
+if platform.system() == "Windows":
+    from pygrabber.dshow_graph import FilterGraph
+
+
+class VideoCapturer:
+    def __init__(self, device_index: int):
+        self.device_index = device_index
+        self.frame_callback = None
+        self._current_frame = None
+        self._frame_ready = threading.Event()
+        self.is_running = False
+        self.cap = None
+        # Actual values reported by the camera after configuration
+        self.actual_width: int = 0
+        self.actual_height: int = 0
+        self.actual_fps: float = 0.0
+
+        # Initialize Windows-specific components if on Windows
+        if platform.system() == "Windows":
+            self.graph = FilterGraph()
+            # Verify device exists
+            devices = self.graph.get_input_devices()
+            if self.device_index >= len(devices):
+                raise ValueError(
+                    f"Invalid device index {device_index}. Available devices: {len(devices)}"
+                )
+
+    def start(self, width: int = 960, height: int = 540, fps: int = 60) -> bool:
+        """Initialize and start video capture"""
+        try:
+            if platform.system() == "Windows":
+                # device_index comes from pygrabber.FilterGraph (DirectShow
+                # enumeration), so open with DSHOW first to preserve mapping.
+                # MSMF and DirectShow enumerate cameras in different orders, so
+                # opening MSMF with a DSHOW index silently selects the wrong
+                # camera. MSMF/ANY remain as fallbacks for cameras DSHOW can't
+                # open.
+                #
+                # Pass codec + resolution + fps as construction params (OpenCV
+                # 4.6+). DSHOW locks the pixel format at open time and ignores
+                # later cap.set(CAP_PROP_FOURCC, ...) — without this, DSHOW
+                # falls back to uncompressed YUYV at 1080p, which is USB-
+                # bandwidth-limited to ~5 fps. Setting MJPG at construction
+                # negotiates compressed frames from the first read.
+                mjpg = cv2.VideoWriter_fourcc(*'MJPG')
+                open_params = [
+                    cv2.CAP_PROP_FOURCC, mjpg,
+                    cv2.CAP_PROP_FRAME_WIDTH, width,
+                    cv2.CAP_PROP_FRAME_HEIGHT, height,
+                    cv2.CAP_PROP_FPS, fps,
+                ]
+                capture_methods = [
+                    (self.device_index, cv2.CAP_DSHOW),
+                    (self.device_index, cv2.CAP_MSMF),
+                    (self.device_index, cv2.CAP_ANY),
+                ]
+
+                for dev_id, backend in capture_methods:
+                    try:
+                        self.cap = cv2.VideoCapture(dev_id, backend, open_params)
+                        if self.cap.isOpened():
+                            break
+                        self.cap.release()
+                    except Exception:
+                        continue
+            else:
+                # Unix-like systems (Linux/Mac) capture method
+                self.cap = cv2.VideoCapture(self.device_index)
+
+            if not self.cap or not self.cap.isOpened():
+                raise RuntimeError("Failed to open camera")
+
+            # Belt-and-braces: also set via cap.set() for backends that honor
+            # post-open changes (MSMF, V4L2). DSHOW ignores these, but the
+            # construction params above already handled it.
+            if platform.system() != "Windows":
+                self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
+                self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
+                self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
+                self.cap.set(cv2.CAP_PROP_FPS, fps)
+
+            # Read back resolution (usually reliable)
+            self.actual_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            self.actual_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+            # CAP_PROP_FPS is unreliable on DirectShow — often reports 30
+            # even when the camera delivers 60.  Measure empirically by
+            # timing a burst of frames.
+            reported_fps = self.cap.get(cv2.CAP_PROP_FPS)
+            self.actual_fps = self._measure_fps(warmup=10, sample=30,
+                                                fallback=reported_fps or fps)
+
+            print(f"[VideoCapturer] {self.actual_width}x{self.actual_height} "
+                  f"@ {self.actual_fps:.1f}fps (reported={reported_fps:.0f})",
+                  flush=True)
+
+            self.is_running = True
+            return True
+
+        except Exception as e:
+            print(f"Failed to start capture: {str(e)}")
+            if self.cap:
+                self.cap.release()
+            return False
+
+    def read(self) -> Tuple[bool, Optional[np.ndarray]]:
+        """Read a frame from the camera"""
+        if not self.is_running or self.cap is None:
+            return False, None
+
+        ret, frame = self.cap.read()
+        if ret:
+            self._current_frame = frame
+            if self.frame_callback:
+                self.frame_callback(frame)
+            return True, frame
+        return False, None
+
+    def release(self) -> None:
+        """Stop capture and release resources"""
+        if self.is_running and self.cap is not None:
+            self.cap.release()
+            self.is_running = False
+            self.cap = None
+
+    def _measure_fps(self, warmup: int = 10, sample: int = 30,
+                     fallback: float = 30.0) -> float:
+        """Read warmup+sample frames and return measured FPS.
+
+        This is more reliable than CAP_PROP_FPS which often lies on
+        DirectShow.  Takes ~0.5-1s at startup but gives a ground-truth
+        number for adaptive polling/detection intervals.
+        """
+        try:
+            for _ in range(warmup):
+                self.cap.read()
+            t0 = time.perf_counter()
+            for _ in range(sample):
+                ret, _ = self.cap.read()
+                if not ret:
+                    return fallback
+            elapsed = time.perf_counter() - t0
+            if elapsed <= 0:
+                return fallback
+            return sample / elapsed
+        except Exception:
+            return fallback
+
+    def set_frame_callback(self, callback: Callable[[np.ndarray], None]) -> None:
+        """Set callback for frame processing"""
+        self.frame_callback = callback

pyproject.toml

@@ -0,0 +1,9 @@
+[tool.ruff]
+target-version = "py310"
+
+[tool.ruff.lint]
+# Deterministic, low-risk rules enforced in CI. Other rules (F841, E402, F821)
+# surface real findings but require human judgement to fix safely, so they are
+# left out of the gate for now. Intentional side-effect imports should be
+# annotated with `# noqa: F401`.
+select = ["E701", "E711", "E712", "F401", "F541"]

requirements.txt

@@ -1,27 +1,18 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-
-numpy==1.23.5
-opencv-contrib-python==4.10.0.84
-onnx==1.16.0
+numpy>=2.0,<3
+typing-extensions>=4.15.0
+opencv-python==4.14.0.94
+opencv-python-headless==4.14.0.94
+cv2_enumerate_cameras==1.3.3
+onnx==1.22.0
 insightface==0.7.3
-psutil==5.9.8
-tk==0.1.0
-customtkinter==5.2.2
-pillow==9.5.0
-torch==2.0.1+cu118; sys_platform != 'darwin'
-torch==2.0.1; sys_platform == 'darwin'
-torchvision==0.15.2+cu118; sys_platform != 'darwin'
-torchvision==0.15.2; sys_platform == 'darwin'
-onnxruntime==1.18.0; sys_platform == 'darwin' and platform_machine != 'arm64'
-onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.18.0; sys_platform != 'darwin'
-tensorflow==2.13.0rc1; sys_platform == 'darwin'
-tensorflow==2.12.1; sys_platform != 'darwin'
-opennsfw2==0.10.2
-protobuf==4.23.2
-tqdm==4.66.4
-gfpgan==1.3.8
-pyobjc==9.1; sys_platform == 'darwin'
-pygrabber==0.2
-pyvirtualcam==0.12.0
-pyobjc-framework-AVFoundation==10.3.1; sys_platform == 'darwin'
+psutil==7.2.2
+PySide6>=6.7,<7
+pillow==12.3.0
+tqdm>=4.66.3
+onnxruntime==1.28.0; sys_platform == 'darwin' and platform_machine == 'arm64'
+onnxruntime==1.23.0; sys_platform == 'darwin' and platform_machine != 'arm64'
+onnxruntime-gpu==1.26.0; sys_platform != 'darwin'
+opennsfw2==0.18.0
+keras>=3.0.0
+protobuf>=6.33.5,<8
+pygrabber; sys_platform == 'win32'

run-cuda.bat

@@ -1 +1 @@
-python run.py --execution-provider cuda --execution-threads 60 --max-memory 60
+python run.py --execution-provider cuda

run-directml.bat

@@ -0,0 +1 @@
+python run.py --execution-provider dml

run-laptop-gpu.bat

@@ -1 +0,0 @@
-python run.py --execution-provider dml

run.py

@@ -1,5 +1,98 @@
 #!/usr/bin/env python3
 
+import os
+import sys
+
+# Add the project root to PATH so bundled ffmpeg/ffprobe are found
+project_root = os.path.dirname(os.path.abspath(__file__))
+os.environ["PATH"] = project_root + os.pathsep + os.environ.get("PATH", "")
+
+# On Windows, register NVIDIA CUDA DLL directories so onnxruntime-gpu can
+# find cuDNN/cublas. Python 3.8+ ignores PATH for extension-module native deps —
+# os.add_dll_directory() is required. Also keep PATH for child processes/ffmpeg.
+if sys.platform == "win32":
+    _site_packages = os.path.join(sys.prefix, "Lib", "site-packages")
+    _venv_site_packages = os.path.join(project_root, "venv", "Lib", "site-packages")
+    for _sp in (_site_packages, _venv_site_packages):
+        _candidate_dirs = []
+        _torch_lib = os.path.join(_sp, "torch", "lib")
+        if os.path.isdir(_torch_lib):
+            _candidate_dirs.append(_torch_lib)
+        _nvidia_dir = os.path.join(_sp, "nvidia")
+        if os.path.isdir(_nvidia_dir):
+            for _pkg in os.listdir(_nvidia_dir):
+                _bin_dir = os.path.join(_nvidia_dir, _pkg, "bin")
+                if os.path.isdir(_bin_dir):
+                    _candidate_dirs.append(_bin_dir)
+        for _d in _candidate_dirs:
+            os.environ["PATH"] = _d + os.pathsep + os.environ["PATH"]
+            try:
+                os.add_dll_directory(_d)
+            except (OSError, AttributeError):
+                pass
+
+    # On Windows, register OpenVINO DLL directories so onnxruntime's
+    # OpenVINOExecutionProvider can find openvino.dll.  This must happen
+    # before any ONNX InferenceSession is created.  Failure is non-fatal:
+    # OpenVINO simply isn't installed, and onnxruntime will fall back to CPU.
+    try:
+        from onnxruntime.tools.add_openvino_win_libs import (  # type: ignore[import-untyped]  # noqa: E501
+            add_openvino_libs_to_path,
+        )
+        add_openvino_libs_to_path()
+    except ImportError:
+        # onnxruntime build without the OpenVINO tooling module — no-op.
+        pass
+    except FileNotFoundError:
+        # OpenVINO site-packages dir absent — no-op.
+        pass
+    except SystemExit as exc:
+        # add_openvino_libs_to_path() calls sys.exit() when OpenVINO libs
+        # can't be located (e.g. OPENVINO_LIB_PATHS unset).  Log the message
+        # it raised with so the failure is visible, but keep startup alive.
+        print(
+            f"[startup] OpenVINO DLL registration skipped: {exc}",
+            flush=True,
+        )
+
+# On Linux, pre-load NVIDIA shared libraries (cuDNN, cuBLAS, nvrtc...) shipped
+# inside the venv via pip wheels (nvidia-cudnn-cu12, etc.). LD_LIBRARY_PATH
+# cannot be set after Python starts, so we use ctypes.CDLL with RTLD_GLOBAL
+# instead. This makes symbols available to onnxruntime when it dlopens its
+# CUDA provider.
+if sys.platform.startswith("linux"):
+    import ctypes
+    import glob
+    _py_lib = f"python{sys.version_info.major}.{sys.version_info.minor}"
+    _site_packages_candidates = [
+        os.path.join(project_root, "venv", "lib", _py_lib, "site-packages"),
+        os.path.join(sys.prefix, "lib", _py_lib, "site-packages"),
+    ]
+    for _sp in _site_packages_candidates:
+        _nvidia_dir = os.path.join(_sp, "nvidia")
+        if not os.path.isdir(_nvidia_dir):
+            continue
+        for _pkg in os.listdir(_nvidia_dir):
+            _lib_dir = os.path.join(_nvidia_dir, _pkg, "lib")
+            if not os.path.isdir(_lib_dir):
+                continue
+            # Also expose the directory to child processes, without
+            # duplicating an entry that is already present.
+            _ldp = os.environ.get("LD_LIBRARY_PATH", "")
+            if _lib_dir not in _ldp.split(os.pathsep):
+                os.environ["LD_LIBRARY_PATH"] = (
+                    _lib_dir + (os.pathsep + _ldp if _ldp else "")
+                )
+            for _so in sorted(glob.glob(os.path.join(_lib_dir, "lib*.so*"))):
+                try:
+                    ctypes.CDLL(_so, mode=ctypes.RTLD_GLOBAL)
+                except OSError:
+                    pass
+        break
+
+from modules import platform_info
+platform_info.print_banner()
+
 from modules import core
 
 if __name__ == '__main__':

run_with_chocolatey.bat

@@ -1,13 +0,0 @@
-@echo off
-	:: Installing Microsoft Visual C++ Runtime - all versions 1.0.1 if it's not already installed
-	choco install vcredist-all
-	:: Installing CUDA if it's not already installed
-	choco install cuda
-	:: Inatalling ffmpeg if it's not already installed
-	choco install ffmpeg
-	:: Installing Python if it's not already installed
-	choco install python -y
-	:: Assuming successful installation, we ensure pip is upgraded
-	python -m ensurepip --upgrade
-	:: Use pip to install the packages listed in 'requirements.txt'
-	pip install -r requirements.txt

setup_deep_live_cam.bat

@@ -1,125 +0,0 @@
-@echo off
-setlocal EnableDelayedExpansion
-
-:: 1. Setup your platform
-echo Setting up your platform...
-call :check_installation python "Python 3.10 or later"
-call :check_installation pip "Pip"
-call :install_if_missing git "Git" "winget install --id Git.Git -e --source winget"
-call :install_if_missing ffmpeg "FFMPEG" "winget install --id Gyan.FFmpeg -e --source winget"
-
-:: Visual Studio 2022 Runtimes
-echo Installing Visual Studio 2022 Runtimes...
-winget install --id Microsoft.VC++2015-2022Redist-x64 -e --source winget
-
-:: 2. Clone Repository
-call :clone_repository "https://github.com/iVideoGameBoss/iRoopDeepFaceCam.git" "iRoopDeepFaceCam"
-
-:: 3. Download Models
-echo Downloading models...
-if not exist models mkdir models
-curl -L -o models\GFPGANv1.4.pth https://huggingface.co/ivideogameboss/iroopdeepfacecam/resolve/main/GFPGANv1.4.pth
-curl -L -o models\inswapper_128_fp16.onnx https://huggingface.co/ivideogameboss/iroopdeepfacecam/resolve/main/inswapper_128_fp16.onnx
-
-:: 4. Install dependencies
-echo Creating a virtual environment...
-python -m venv venv
-call venv\Scripts\activate.bat
-
-echo Installing required Python packages...
-pip install --upgrade pip
-pip install -r requirements.txt
-echo Setup complete. You can now run the application.
-
-:menu
-:: GPU Acceleration Options
-echo.
-echo Choose the GPU Acceleration Option if applicable:
-echo 1. CUDA (Nvidia)
-echo 2. CoreML (Apple Silicon)
-echo 3. CoreML (Apple Legacy)
-echo 4. DirectML (Windows)
-echo 5. OpenVINO (Intel)
-echo 6. None
-set /p choice="Enter your choice (1-6): "
-
-set "exec_provider="
-call :set_execution_provider %choice%
-
-:end_choice
-echo.
-echo GPU Acceleration setup complete.
-echo Selected provider: !exec_provider!
-echo.
-
-:: Run the application
-if defined exec_provider (
-    echo Running the application with !exec_provider! execution provider...
-    python run.py --execution-provider !exec_provider!
-) else (
-    echo Running the application...
-    python run.py
-)
-
-:: Deactivate the virtual environment
-call venv\Scripts\deactivate.bat
-
-echo.
-echo Script execution completed.
-pause
-exit /b
-
-:check_installation
-where %1 >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo %2 is not installed. Please install %2.
-    pause
-    exit /b
-)
-
-:install_if_missing
-where %1 >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo %2 is not installed. Installing %2...
-    %3
-)
-
-:clone_repository
-if exist %2 (
-    echo %2 directory already exists.
-    set /p overwrite="Do you want to overwrite? (Y/N): "
-    if /i "%overwrite%"=="Y" (
-        rmdir /s /q %2
-        git clone %1
-    ) else (
-        echo Skipping clone, using existing directory.
-    )
-) else (
-    git clone %1
-)
-
-:set_execution_provider
-if "%1"=="1" (
-    call :install_onnxruntime "onnxruntime-gpu" "1.16.3" "cuda"
-) else if "%1"=="2" (
-    call :install_onnxruntime "onnxruntime-silicon" "1.13.1" "coreml"
-) else if "%1"=="3" (
-    call :install_onnxruntime "onnxruntime-coreml" "1.13.1" "coreml"
-) else if "%1"=="4" (
-    call :install_onnxruntime "onnxruntime-directml" "1.15.1" "directml"
-) else if "%1"=="5" (
-    call :install_onnxruntime "onnxruntime-openvino" "1.15.0" "openvino"
-) else if "%1"=="6" (
-    echo Skipping GPU acceleration setup.
-    set "exec_provider=none"
-) else (
-    echo Invalid choice. Please try again.
-    goto menu
-)
-
-:install_onnxruntime
-echo Installing %1 dependencies...
-pip uninstall -y onnxruntime %1
-pip install %1==%2
-set "exec_provider=%3"
-goto end_choice

tests/test_face_analyser_get_one_face.py

@@ -0,0 +1,97 @@
+import importlib
+import sys
+import types
+import unittest
+from unittest.mock import patch
+
+
+def _install_import_stubs():
+    sys.modules.setdefault(
+        "insightface",
+        types.SimpleNamespace(app=types.SimpleNamespace(FaceAnalysis=object)),
+    )
+    sys.modules.setdefault(
+        "cv2",
+        types.SimpleNamespace(
+            IMREAD_COLOR=1,
+            imread=lambda *_args, **_kwargs: None,
+            imdecode=lambda *_args, **_kwargs: None,
+            imencode=lambda *_args, **_kwargs: (
+                True,
+                types.SimpleNamespace(tofile=lambda *_a, **_k: None),
+            ),
+        ),
+    )
+    sys.modules.setdefault(
+        "numpy",
+        types.SimpleNamespace(uint8=object, fromfile=lambda *_args, **_kwargs: b""),
+    )
+    sys.modules.setdefault(
+        "tqdm",
+        types.SimpleNamespace(tqdm=lambda iterable, **_kwargs: iterable),
+    )
+    sys.modules["modules.typing"] = types.SimpleNamespace(Frame=object)
+    sys.modules["modules.cluster_analysis"] = types.SimpleNamespace(
+        find_cluster_centroids=lambda *args, **kwargs: [],
+        find_closest_centroid=lambda *args, **kwargs: (0, None),
+    )
+    sys.modules["modules.utilities"] = types.SimpleNamespace(
+        get_temp_directory_path=lambda path: path,
+        create_temp=lambda path: None,
+        extract_frames=lambda path: None,
+        clean_temp=lambda path: None,
+        get_temp_frame_paths=lambda path: [],
+    )
+
+
+def _load_face_analyser():
+    _install_import_stubs()
+    sys.modules.pop("modules.face_analyser", None)
+    return importlib.import_module("modules.face_analyser")
+
+
+class Face:
+    def __init__(self, left):
+        self.bbox = [left, 0, 10, 10]
+
+
+class GetOneFaceTests(unittest.TestCase):
+    def test_uses_supplied_detected_faces_without_reanalysing_frame(self):
+        face_analyser = _load_face_analyser()
+        right = Face(20)
+        left = Face(5)
+
+        with patch.object(
+            face_analyser,
+            "_analyse_faces",
+            side_effect=AssertionError("should not analyse"),
+        ):
+            self.assertIs(face_analyser.get_one_face("frame", [right, left]), left)
+
+    def test_supplied_empty_detected_faces_returns_none(self):
+        face_analyser = _load_face_analyser()
+
+        with patch.object(
+            face_analyser,
+            "_analyse_faces",
+            side_effect=AssertionError("should not analyse"),
+        ):
+            self.assertIsNone(face_analyser.get_one_face("frame", []))
+
+    def test_without_supplied_faces_preserves_existing_detection_path(self):
+        face_analyser = _load_face_analyser()
+        right = Face(30)
+        left = Face(3)
+
+        with patch.object(face_analyser, "_is_dml", return_value=False), patch.object(
+            face_analyser,
+            "_analyse_faces",
+            return_value=[right, left],
+        ) as analyse:
+            self.assertIs(face_analyser.get_one_face("frame"), left)
+
+        analyse.assert_called_once_with("frame")
+
+
+if __name__ == "__main__":
+    unittest.main()

tkinter_fix.py

@@ -0,0 +1,29 @@
+import os
+os.environ.setdefault('TK_SILENCE_DEPRECATION', '1')
+
+import tkinter
+
+# Only needs to be imported once at the beginning of the application
+def apply_patch():
+    # Create a monkey patch for the internal _tkinter module
+    original_init = tkinter.Tk.__init__
+    
+    def patched_init(self, *args, **kwargs):
+        # Call the original init
+        original_init(self, *args, **kwargs)
+        
+        # Define the missing ::tk::ScreenChanged procedure
+        self.tk.eval("""
+        if {[info commands ::tk::ScreenChanged] == ""} {
+            proc ::tk::ScreenChanged {args} {
+                # Do nothing
+                return
+            }
+        }
+        """)
+    
+    # Apply the monkey patch
+    tkinter.Tk.__init__ = patched_init
+
+# Apply the patch automatically when this module is imported
+apply_patch()