Creating a fallback and switching of models

Models switch depending on the execution provider
Update requirements.txt
2025-08-02 02:56:20 +08:00 · 2025-07-09 17:19:26 +08:00 · 2025-07-02 18:37:10 +08:00 · 2025-07-02 18:29:32 +08:00 · 2025-07-02 04:08:26 +08:00 · 2025-06-29 15:03:35 +08:00
48 changed files with 2941 additions and 462 deletions
@@ -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
+
@@ -6,17 +6,22 @@ __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
@@ -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.
@@ -1,163 +1,328 @@
-![demo-gif](demo.gif)
+<h1 align="center">Deep-Live-Cam</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.
+
+## Exclusive v2.1 Quick Start - Pre-built (Windows/Mac Silicon)
+
+  <a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
+
+##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU or Mac Silicon, And you'll receive special priority support.
+ 
+###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually. 
+
+## 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 (3.11 recommended)
 -   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_fp16.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 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.pth)
+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 3.10
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
 ```
+
+**For macOS:**
+
+Apple Silicon (M1/M2/M3) requires specific setup:
+
+```bash
+# Install Python 3.11 (specific version is important)
+brew install python@3.11
+
+# Install tkinter package (required for the GUI)
+brew install python-tk@3.10
+
+# Create and activate virtual environment with Python 3.11
+python3.11 -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
+```
+
+**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/M2/M3) specific installation:

-```
+1. Make sure you've completed the macOS setup above using Python 3.10.
+2. Install dependencies:
+
+```bash
 pip uninstall onnxruntime onnxruntime-silicon
 pip install onnxruntime-silicon==1.13.1
-
 ```

-2.  Usage in case the provider is available:
-
-```
-python run.py --execution-provider coreml
+3. Usage (important: specify Python 3.10):

+```bash
+python3.10 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:**
+- You **must** use Python 3.10, not newer versions like 3.11 or 3.13
+- Always run with `python3.10` 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@3.10`
+- 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
+  
+  # Uninstall conflicting versions if needed
+  brew uninstall --ignore-dependencies python@3.11 python@3.13
+  
+  # Keep only Python 3.11
+  brew cleanup
+  ```

-1.  Install dependencies:
+**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).
-
-
-Additional command line arguments are given below. To learn out what they do, check [this guide](https://github.com/s0md3v/roop/wiki/Advanced-Options).
+## 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, ...)
  --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
+  --live-resizable                                         the live camera frame is resizable
  --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
@@ -166,10 +331,51 @@ options:

 Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.

+## Press
+
+**We are always open to criticism and are ready to improve, that's why we didn't cherry-pick anything.**
+
+ - [*"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - Ars Technica
+ - [*"Thanks Deep Live Cam, shapeshifters are among us now"*](https://dataconomy.com/2024/08/15/what-is-deep-live-cam-github-deepfake/) - Dataconomy
+ - [*"This free AI tool lets you become anyone during video-calls"*](https://www.newsbytesapp.com/news/science/deep-live-cam-ai-impersonation-tool-goes-viral/story) - NewsBytes
+ - [*"OK, this viral AI live stream software is truly terrifying"*](https://www.creativebloq.com/ai/ok-this-viral-ai-live-stream-software-is-truly-terrifying) - Creative Bloq
+ - [*"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*](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/) - PetaPixel
+ - [*"Deep-Live-Cam Uses AI to Transform Your Face in Real-Time, Celebrities Included"*](https://www.techeblog.com/deep-live-cam-ai-transform-face/) - TechEBlog
+ - [*"An AI tool that "makes you look like anyone" during a video call is going viral online"*](https://telegrafi.com/en/a-tool-that-makes-you-look-like-anyone-during-a-video-call-is-going-viral-on-the-Internet/) - Telegrafi
+ - [*"This Deepfake Tool Turning Images Into Livestreams is Topping the GitHub Charts"*](https://decrypt.co/244565/this-deepfake-tool-turning-images-into-livestreams-is-topping-the-github-charts) - Emerge
+ - [*"New Real-Time Face-Swapping AI Allows Anyone to Mimic Famous Faces"*](https://www.digitalmusicnews.com/2024/08/15/face-swapping-ai-real-time-mimic/) - Digital Music News
+ - [*"This real-time webcam deepfake tool raises alarms about the future of identity theft"*](https://www.diyphotography.net/this-real-time-webcam-deepfake-tool-raises-alarms-about-the-future-of-identity-theft/) - DIYPhotography
+ - [*"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - SomeOrdinaryGamers
+ - [*"Alright look look look, now look chat, we can do any face we want to look like chat"*](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - IShowSpeed
+ - [*"They do a pretty good job matching poses, expression and even the lighting"*](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - TechLinked (LTT)
+
+
 ## 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
+-   [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
+-   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>
@@ -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."
+}
@@ -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."
+}
@@ -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."
+}
@@ -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។"
+}
@@ -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."
+}
@@ -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."
+}
@@ -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."
+}
@@ -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"
+}
@@ -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。"
+}
@@ -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
@@ -0,0 +1,18 @@
+import os 
+import cv2
+import numpy as np
+
+# Utility function to support unicode characters in file paths for reading
+def imread_unicode(path, flags=cv2.IMREAD_COLOR):
+    return cv2.imdecode(np.fromfile(path, dtype=np.uint8), flags)
+
+# Utility function to support unicode characters in file paths for writing
+def imwrite_unicode(path, img, params=None):
+    root, ext = os.path.splitext(path)
+    if not ext:
+        ext = ".png"
+        result, encoded_img = cv2.imencode(ext, img, params if params else [])
+        result, encoded_img = cv2.imencode(f".{ext}", img, params if params is not None else [])
+        encoded_img.tofile(path)
+        return True
+    return False
@@ -1,16 +1,28 @@
 from typing import Any
 import cv2
+import modules.globals  # Import the globals to check the color correction toggle


 def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
    capture = cv2.VideoCapture(video_path)
+
+    # Set MJPEG format to ensure correct color space handling
+    capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
+    
+    # Only force RGB conversion if color correction is enabled
+    if modules.globals.color_correction:
+        capture.set(cv2.CAP_PROP_CONVERT_RGB, 1)
+    
    frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
    capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
    has_frame, frame = capture.read()
+
+    if has_frame and modules.globals.color_correction:
+        # Convert the frame color if necessary
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
    capture.release()
-    if has_frame:
-        return frame
-    return None
+    return frame if has_frame else None


 def get_video_frame_total(video_path: str) -> int:
@@ -0,0 +1,32 @@
+import numpy as np
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+from typing import Any
+
+
+def find_cluster_centroids(embeddings, max_k=10) -> Any:
+    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
@@ -39,8 +39,14 @@ def parse_args() -> None:
    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=['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())
@@ -63,19 +69,23 @@ def parse_args() -> None:
    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
+    modules.globals.live_resizable = args.live_resizable
    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.lang = args.lang

    #for ENHANCER tumbler:
    if 'face_enhancer' in args.frame_processor:
        modules.globals.fp_ui['face_enhancer'] = True
    else:
        modules.globals.fp_ui['face_enhancer'] = False
-    
-    modules.globals.nsfw = False

    # translate deprecated args
    if args.source_path_deprecated:
@@ -165,18 +175,19 @@ def update_status(message: str, scope: str = 'DLC.CORE') -> None:
    if not modules.globals.headless:
        ui.update_status(message)

-
 def start() -> None:
    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
        if not frame_processor.pre_start():
            return
+    update_status('Processing...')
    # process image to image
    if has_image_extension(modules.globals.target_path):
-        if modules.globals.nsfw == False:
-            from modules.predicter import predict_image
-            if predict_image(modules.globals.target_path):
-                destroy()
-        shutil.copy2(modules.globals.target_path, modules.globals.output_path)
+        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)
@@ -187,14 +198,15 @@ def start() -> None:
            update_status('Processing to image failed!')
        return
    # process image to videos
-    if modules.globals.nsfw == False:
-        from modules.predicter import predict_video
-        if predict_video(modules.globals.target_path):
-            destroy()
-    update_status('Creating temp resources...')
-    create_temp(modules.globals.target_path)
-    update_status('Extracting frames...')
-    extract_frames(modules.globals.target_path)
+    if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
+        return
+
+    if not modules.globals.map_faces:
+        update_status('Creating temp 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('Progressing...', frame_processor.NAME)
@@ -226,10 +238,10 @@ def start() -> None:
        update_status('Processing to video failed!')


-def destroy() -> None:
+def destroy(to_quit=True) -> None:
    if modules.globals.target_path:
        clean_temp(modules.globals.target_path)
-    quit()
+    if to_quit: quit()


 def run() -> None:
@@ -243,5 +255,5 @@ def run() -> None:
    if modules.globals.headless:
        start()
    else:
-        window = ui.init(start, destroy)
+        window = ui.init(start, destroy, modules.globals.lang)
        window.mainloop()
@@ -1,8 +1,16 @@
+import os
+import shutil
 from typing import Any
 import insightface

+import cv2
+import numpy as np
 import modules.globals
+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 = None

@@ -29,3 +37,153 @@ def get_many_faces(frame: Frame) -> Any:
        return get_face_analyser().get(frame)
    except IndexError:
        return None
+
+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 = cv2.imread(modules.globals.target_path)
+        many_faces = get_many_faces(target_frame)
+        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 = cv2.imread(temp_frame_path)
+            many_faces = get_many_faces(temp_frame)
+
+            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
+
+        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 = cv2.imread(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 = cv2.imread(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:
+                        cv2.imwrite(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
@@ -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)
@@ -1,30 +1,43 @@
 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_target_map = []
+simple_map = {}
+
 source_path = None
 target_path = None
 output_path = None
 frame_processors: List[str] = []
-keep_fps = None
-keep_audio = None
-keep_frames = None
-many_faces = None
+keep_fps = True
+keep_audio = True
+keep_frames = False
+many_faces = False
+map_faces = False
+color_correction = False  # New global variable for color correction toggle
+nsfw_filter = False
 video_encoder = None
 video_quality = None
+live_mirror = False
+live_resizable = True
 max_memory = None
 execution_providers: List[str] = []
 execution_threads = None
 headless = None
-log_level = 'error'
-fp_ui: Dict[str, bool] = {}
-nsfw = None
+log_level = "error"
+fp_ui: Dict[str, bool] = {"face_enhancer": False}
 camera_input_combobox = None
-webcam_preview_running = False
+webcam_preview_running = False
+show_fps = False
+mouth_mask = False
+show_mouth_mask_box = False
+mask_feather_ratio = 8
+mask_down_size = 0.50
+mask_size = 1
@@ -1,3 +1,3 @@
-name = 'Deep Live Cam'
-version = '1.3.0'
-edition = 'Portable'
+name = 'Deep-Live-Cam'
+version = '1.8.1'
+edition = 'GitHub Edition'
@@ -1,16 +1,27 @@
 import numpy
 import opennsfw2
 from PIL import Image
+import cv2  # Add OpenCV import
+import modules.globals  # Import globals to access the color correction toggle

 from modules.typing import Frame

 MAX_PROBABILITY = 0.85

+# Preload the model once for efficiency
+model = None

 def predict_frame(target_frame: Frame) -> bool:
+    # Convert the frame to RGB before processing if color correction is enabled
+    if modules.globals.color_correction:
+        target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
+        
    image = Image.fromarray(target_frame)
    image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
-    model = opennsfw2.make_open_nsfw_model()
+    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
@@ -42,18 +42,29 @@ def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType

 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 == True and frame_processor not in frame_processors:
-            frame_processor_module = load_frame_processor_module(frame_processor)
-            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
-            modules.globals.frame_processors.append(frame_processor)
-        if state == False:
+        if state == True and frame_processor not in current_processor_names:
            try:
                frame_processor_module = load_frame_processor_module(frame_processor)
-                FRAME_PROCESSORS_MODULES.remove(frame_processor_module)
-                modules.globals.frame_processors.remove(frame_processor)
-            except:
-                pass
+                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 state == False 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:
@@ -9,47 +9,89 @@ import modules.processors.frame.core
 from modules.core import update_status
 from modules.face_analyser import get_one_face
 from modules.typing import Frame, Face
-from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
+import platform
+import torch
+from modules.utilities import (
+    conditional_download,
+    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"
+)


 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'])
+    download_directory_path = models_dir
+    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)
+    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


+TENSORRT_AVAILABLE = False
+try:
+    import torch_tensorrt
+    TENSORRT_AVAILABLE = True
+except ImportError as im:
+    print(f"TensorRT is not available: {im}")
+    pass
+except Exception as e:
+    print(f"TensorRT is not available: {e}")
+    pass
+
 def get_face_enhancer() -> Any:
    global FACE_ENHANCER

    with THREAD_LOCK:
        if FACE_ENHANCER is None:
-            if os.name == 'nt':
-                model_path = resolve_relative_path('..\models\GFPGANv1.4.pth')
-                # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399
-            else:
-                model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
-            FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
+            model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
+            
+            selected_device = None
+            device_priority = []
+
+            if TENSORRT_AVAILABLE and torch.cuda.is_available():
+                selected_device = torch.device("cuda")
+                device_priority.append("TensorRT+CUDA")
+            elif torch.cuda.is_available():
+                selected_device = torch.device("cuda")
+                device_priority.append("CUDA")
+            elif torch.backends.mps.is_available() and platform.system() == "Darwin":
+                selected_device = torch.device("mps")
+                device_priority.append("MPS")
+            elif not torch.cuda.is_available():
+                selected_device = torch.device("cpu")
+                device_priority.append("CPU")
+            
+            FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=selected_device)
+
+            # for debug:
+            print(f"Selected device: {selected_device} and device priority: {device_priority}")
    return FACE_ENHANCER


 def enhance_face(temp_frame: Frame) -> Frame:
    with THREAD_SEMAPHORE:
-        _, _, temp_frame = get_face_enhancer().enhance(
-            temp_frame,
-            paste_back=True
-        )
+        _, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
    return temp_frame


@@ -60,7 +102,9 @@ def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
    return temp_frame


-def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
+def process_frames(
+    source_path: str, temp_frame_paths: List[str], progress: Any = None
+) -> None:
    for temp_frame_path in temp_frame_paths:
        temp_frame = cv2.imread(temp_frame_path)
        result = process_frame(None, temp_frame)
@@ -77,3 +121,10 @@ def process_image(source_path: str, target_path: str, output_path: str) -> None:

 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_frame_v2(temp_frame: Frame) -> Frame:
+    target_face = get_one_face(temp_frame)
+    if target_face:
+        temp_frame = enhance_face(temp_frame)
+    return temp_frame
@@ -2,34 +2,57 @@ from typing import Any, List
 import cv2
 import insightface
 import threading
-
+import numpy as np
 import modules.globals
+import logging
 import modules.processors.frame.core
 from modules.core import update_status
-from modules.face_analyser import get_one_face, get_many_faces
+from modules.face_analyser import get_one_face, get_many_faces, default_source_face
 from modules.typing import Face, Frame
-from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
+from modules.utilities import (
+    conditional_download,
+    is_image,
+    is_video,
+)
+from modules.cluster_analysis import find_closest_centroid
+import os

 FACE_SWAPPER = None
 THREAD_LOCK = threading.Lock()
-NAME = 'DLC.FACE-SWAPPER'
+NAME = "DLC.FACE-SWAPPER"
+
+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:
-    download_directory_path = resolve_relative_path('../models')
-    conditional_download(download_directory_path, ['https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx'])
+    download_directory_path = models_dir
+    model_url = "https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128.onnx"
+    if "CUDAExecutionProvider" in modules.globals.execution_providers:
+        model_url = "https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx"
+
+    conditional_download(
+        download_directory_path,
+        [model_url],
+    )
    return True


 def pre_start() -> bool:
-    if not is_image(modules.globals.source_path):
-        update_status('Select an image for source path.', NAME)
+    if not modules.globals.map_faces and 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 in source path detected.', NAME)
+    elif not modules.globals.map_faces and not get_one_face(
+        cv2.imread(modules.globals.source_path)
+    ):
+        update_status("No face in source path detected.", 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)
+    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

@@ -39,48 +62,566 @@ def get_face_swapper() -> Any:

    with THREAD_LOCK:
        if FACE_SWAPPER is None:
-            model_path = resolve_relative_path('../models/inswapper_128_fp16.onnx')
-            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=modules.globals.execution_providers)
+            model_name = "inswapper_128.onnx"
+            if "CUDAExecutionProvider" in modules.globals.execution_providers:
+                model_name = "inswapper_128_fp16.onnx"
+            model_path = os.path.join(models_dir, model_name)
+            FACE_SWAPPER = insightface.model_zoo.get_model(
+                model_path, providers=modules.globals.execution_providers
+            )
    return FACE_SWAPPER


 def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
+    face_swapper = get_face_swapper()
+
+    # Apply the face swap
+    swapped_frame = face_swapper.get(
+        temp_frame, target_face, source_face, paste_back=True
+    )
+
+    if modules.globals.mouth_mask:
+        # Create a mask for the target face
+        face_mask = create_face_mask(target_face, temp_frame)
+
+        # Create the mouth mask
+        mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
+            create_lower_mouth_mask(target_face, temp_frame)
+        )
+
+        # Apply the mouth area
+        swapped_frame = apply_mouth_area(
+            swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
+        )
+
+        if modules.globals.show_mouth_mask_box:
+            mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
+            swapped_frame = draw_mouth_mask_visualization(
+                swapped_frame, target_face, mouth_mask_data
+            )
+
+    return swapped_frame


 def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
+    if modules.globals.color_correction:
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+
    if modules.globals.many_faces:
        many_faces = get_many_faces(temp_frame)
        if many_faces:
            for target_face in many_faces:
-                temp_frame = swap_face(source_face, target_face, temp_frame)
+                if source_face and target_face:
+                    temp_frame = swap_face(source_face, target_face, temp_frame)
+                else:
+                    print("Face detection failed for target/source.")
    else:
        target_face = get_one_face(temp_frame)
-        if target_face:
+        if target_face and source_face:
            temp_frame = swap_face(source_face, target_face, temp_frame)
+        else:
+            logging.error("Face detection failed for target or source.")
    return temp_frame


-def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    for temp_frame_path in temp_frame_paths:
-        temp_frame = cv2.imread(temp_frame_path)
-        try:
-            result = process_frame(source_face, temp_frame)
-            cv2.imwrite(temp_frame_path, result)
-        except Exception as exception:
-            print(exception)
-            pass
-        if progress:
-            progress.update(1)
+
+def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
+    if is_image(modules.globals.target_path):
+        if modules.globals.many_faces:
+            source_face = default_source_face()
+            for map in modules.globals.source_target_map:
+                target_face = map["target"]["face"]
+                temp_frame = swap_face(source_face, target_face, temp_frame)
+
+        elif not modules.globals.many_faces:
+            for map in modules.globals.source_target_map:
+                if "source" in map:
+                    source_face = map["source"]["face"]
+                    target_face = map["target"]["face"]
+                    temp_frame = swap_face(source_face, target_face, temp_frame)
+
+    elif is_video(modules.globals.target_path):
+        if modules.globals.many_faces:
+            source_face = default_source_face()
+            for map in modules.globals.source_target_map:
+                target_frame = [
+                    f
+                    for f in map["target_faces_in_frame"]
+                    if f["location"] == temp_frame_path
+                ]
+
+                for frame in target_frame:
+                    for target_face in frame["faces"]:
+                        temp_frame = swap_face(source_face, target_face, temp_frame)
+
+        elif not modules.globals.many_faces:
+            for map in modules.globals.source_target_map:
+                if "source" in map:
+                    target_frame = [
+                        f
+                        for f in map["target_faces_in_frame"]
+                        if f["location"] == temp_frame_path
+                    ]
+                    source_face = map["source"]["face"]
+
+                    for frame in target_frame:
+                        for target_face in frame["faces"]:
+                            temp_frame = swap_face(source_face, target_face, temp_frame)
+
+    else:
+        detected_faces = get_many_faces(temp_frame)
+        if modules.globals.many_faces:
+            if detected_faces:
+                source_face = default_source_face()
+                for target_face in detected_faces:
+                    temp_frame = swap_face(source_face, target_face, temp_frame)
+
+        elif not modules.globals.many_faces:
+            if detected_faces:
+                if len(detected_faces) <= len(
+                    modules.globals.simple_map["target_embeddings"]
+                ):
+                    for detected_face in detected_faces:
+                        closest_centroid_index, _ = find_closest_centroid(
+                            modules.globals.simple_map["target_embeddings"],
+                            detected_face.normed_embedding,
+                        )
+
+                        temp_frame = swap_face(
+                            modules.globals.simple_map["source_faces"][
+                                closest_centroid_index
+                            ],
+                            detected_face,
+                            temp_frame,
+                        )
+                else:
+                    detected_faces_centroids = []
+                    for face in detected_faces:
+                        detected_faces_centroids.append(face.normed_embedding)
+                    i = 0
+                    for target_embedding in modules.globals.simple_map[
+                        "target_embeddings"
+                    ]:
+                        closest_centroid_index, _ = find_closest_centroid(
+                            detected_faces_centroids, target_embedding
+                        )
+
+                        temp_frame = swap_face(
+                            modules.globals.simple_map["source_faces"][i],
+                            detected_faces[closest_centroid_index],
+                            temp_frame,
+                        )
+                        i += 1
+    return temp_frame
+
+
+def process_frames(
+    source_path: str, temp_frame_paths: List[str], progress: Any = None
+) -> None:
+    if not modules.globals.map_faces:
+        source_face = get_one_face(cv2.imread(source_path))
+        for temp_frame_path in temp_frame_paths:
+            temp_frame = cv2.imread(temp_frame_path)
+            try:
+                result = process_frame(source_face, temp_frame)
+                cv2.imwrite(temp_frame_path, result)
+            except Exception as exception:
+                print(exception)
+                pass
+            if progress:
+                progress.update(1)
+    else:
+        for temp_frame_path in temp_frame_paths:
+            temp_frame = cv2.imread(temp_frame_path)
+            try:
+                result = process_frame_v2(temp_frame, temp_frame_path)
+                cv2.imwrite(temp_frame_path, result)
+            except Exception as exception:
+                print(exception)
+                pass
+            if progress:
+                progress.update(1)


 def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    target_frame = cv2.imread(target_path)
-    result = process_frame(source_face, target_frame)
-    cv2.imwrite(output_path, result)
+    if not modules.globals.map_faces:
+        source_face = get_one_face(cv2.imread(source_path))
+        target_frame = cv2.imread(target_path)
+        result = process_frame(source_face, target_frame)
+        cv2.imwrite(output_path, result)
+    else:
+        if modules.globals.many_faces:
+            update_status(
+                "Many faces enabled. Using first source image. Progressing...", NAME
+            )
+        target_frame = cv2.imread(output_path)
+        result = process_frame_v2(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(source_path, temp_frame_paths, process_frames)
+    if modules.globals.map_faces and modules.globals.many_faces:
+        update_status(
+            "Many faces enabled. Using first source image. Progressing...", NAME
+        )
+    modules.processors.frame.core.process_video(
+        source_path, temp_frame_paths, process_frames
+    )
+
+
+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
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        #                  0  1  2  3  4  5  6  7  8  9  10 11 12 13 14 15 16 17 18 19 20
+        lower_lip_order = [
+            65,
+            66,
+            62,
+            70,
+            69,
+            18,
+            19,
+            20,
+            21,
+            22,
+            23,
+            24,
+            0,
+            8,
+            7,
+            6,
+            5,
+            4,
+            3,
+            2,
+            65,
+        ]
+        lower_lip_landmarks = landmarks[lower_lip_order].astype(
+            np.float32
+        )  # Use float for precise calculations
+
+        # Calculate the center of the landmarks
+        center = np.mean(lower_lip_landmarks, axis=0)
+
+        # Expand the landmarks outward
+        expansion_factor = (
+            1 + modules.globals.mask_down_size
+        )  # Adjust this for more or less expansion
+        expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
+
+        # Extend the top lip part
+        toplip_indices = [
+            20,
+            0,
+            1,
+            2,
+            3,
+            4,
+            5,
+        ]  # Indices for landmarks 2, 65, 66, 62, 70, 69, 18
+        toplip_extension = (
+            modules.globals.mask_size * 0.5
+        )  # Adjust this factor to control the extension
+        for idx in toplip_indices:
+            direction = expanded_landmarks[idx] - center
+            direction = direction / np.linalg.norm(direction)
+            expanded_landmarks[idx] += direction * toplip_extension
+
+        # Extend the bottom part (chin area)
+        chin_indices = [
+            11,
+            12,
+            13,
+            14,
+            15,
+            16,
+        ]  # Indices for landmarks 21, 22, 23, 24, 0, 8
+        chin_extension = 2 * 0.2  # Adjust this factor to control the extension
+        for idx in chin_indices:
+            expanded_landmarks[idx][1] += (
+                expanded_landmarks[idx][1] - center[1]
+            ) * chin_extension
+
+        # 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)
+        cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
+
+        # Apply Gaussian blur to soften the mask edges
+        mask_roi = cv2.GaussianBlur(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
+
+    return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
+
+
+def draw_mouth_mask_visualization(
+    frame: Frame, face: Face, mouth_mask_data: tuple
+) -> Frame:
+    landmarks = face.landmark_2d_106
+    if landmarks is not None and mouth_mask_data is not None:
+        mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = (
+            mouth_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)
+
+        # Adjust mask to match the region size
+        mask_region = mask[0 : max_y - min_y, 0 : max_x - min_x]
+
+        # Remove the color mask overlay
+        # color_mask = cv2.applyColorMap((mask_region * 255).astype(np.uint8), cv2.COLORMAP_JET)
+
+        # Ensure shapes match before blending
+        vis_region = vis_frame[min_y:max_y, min_x:max_x]
+        # Remove blending with color_mask
+        # if vis_region.shape[:2] == color_mask.shape[:2]:
+        #     blended = cv2.addWeighted(vis_region, 0.7, color_mask, 0.3, 0)
+        #     vis_frame[min_y:max_y, min_x:max_x] = blended
+
+        # Draw the lower lip polygon
+        cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
+
+        # Remove the red box
+        # cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 2)
+
+        # Visualize the feathered mask
+        feather_amount = max(
+            1,
+            min(
+                30,
+                (max_x - min_x) // modules.globals.mask_feather_ratio,
+                (max_y - min_y) // modules.globals.mask_feather_ratio,
+            ),
+        )
+        # Ensure kernel size is odd
+        kernel_size = 2 * feather_amount + 1
+        feathered_mask = cv2.GaussianBlur(
+            mask_region.astype(float), (kernel_size, kernel_size), 0
+        )
+        feathered_mask = (feathered_mask / feathered_mask.max() * 255).astype(np.uint8)
+        # Remove the feathered mask color overlay
+        # color_feathered_mask = cv2.applyColorMap(feathered_mask, cv2.COLORMAP_VIRIDIS)
+
+        # Ensure shapes match before blending feathered mask
+        # if vis_region.shape == color_feathered_mask.shape:
+        #     blended_feathered = cv2.addWeighted(vis_region, 0.7, color_feathered_mask, 0.3, 0)
+        #     vis_frame[min_y:max_y, min_x:max_x] = blended_feathered
+
+        # Add labels
+        cv2.putText(
+            vis_frame,
+            "Lower Mouth Mask",
+            (min_x, min_y - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (255, 255, 255),
+            1,
+        )
+        cv2.putText(
+            vis_frame,
+            "Feathered Mask",
+            (min_x, max_y + 20),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (255, 255, 255),
+            1,
+        )
+
+        return vis_frame
+    return frame
+
+
+def apply_mouth_area(
+    frame: np.ndarray,
+    mouth_cutout: np.ndarray,
+    mouth_box: tuple,
+    face_mask: np.ndarray,
+    mouth_polygon: np.ndarray,
+) -> np.ndarray:
+    min_x, min_y, max_x, max_y = mouth_box
+    box_width = max_x - min_x
+    box_height = max_y - min_y
+
+    if (
+        mouth_cutout is None
+        or box_width is None
+        or box_height is None
+        or face_mask is None
+        or mouth_polygon is None
+    ):
+        return frame
+
+    try:
+        resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
+        roi = frame[min_y:max_y, min_x:max_x]
+
+        if roi.shape != resized_mouth_cutout.shape:
+            resized_mouth_cutout = cv2.resize(
+                resized_mouth_cutout, (roi.shape[1], roi.shape[0])
+            )
+
+        color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
+
+        # Use the provided mouth polygon to create the mask
+        polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
+        adjusted_polygon = mouth_polygon - [min_x, min_y]
+        cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
+
+        # Apply feathering to the polygon mask
+        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(float), (0, 0), feather_amount
+        )
+        feathered_mask = feathered_mask / feathered_mask.max()
+
+        face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
+        combined_mask = feathered_mask * (face_mask_roi / 255.0)
+
+        combined_mask = combined_mask[:, :, np.newaxis]
+        blended = (
+            color_corrected_mouth * combined_mask + roi * (1 - combined_mask)
+        ).astype(np.uint8)
+
+        # Apply face mask to blended result
+        face_mask_3channel = (
+            np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
+        )
+        final_blend = blended * face_mask_3channel + roi * (1 - 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 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 forehead extension
+        right_eyebrow_top = np.min(right_eye_brow[:, 1])
+        left_eyebrow_top = np.min(left_eye_brow[:, 1])
+        eyebrow_top = min(right_eyebrow_top, left_eyebrow_top)
+
+        face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
+        forehead_height = face_top - eyebrow_top
+        extended_forehead_height = int(forehead_height * 5.0)  # Extend by 50%
+
+        # Create forehead points
+        forehead_left = right_side_face[0].copy()
+        forehead_right = left_side_face[-1].copy()
+        forehead_left[1] -= extended_forehead_height
+        forehead_right[1] -= extended_forehead_height
+
+        # Combine all points to create the face outline
+        face_outline = np.vstack(
+            [
+                [forehead_left],
+                right_side_face,
+                left_side_face[
+                    ::-1
+                ],  # Reverse left side to create a continuous outline
+                [forehead_right],
+            ]
+        )
+
+        # 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
+        hull = cv2.convexHull(face_outline)
+        hull_padded = []
+        for point in hull:
+            x, y = point[0]
+            center = np.mean(face_outline, axis=0)
+            direction = np.array([x, y]) - center
+            direction = direction / np.linalg.norm(direction)
+            padded_point = np.array([x, y]) + direction * padding
+            hull_padded.append(padded_point)
+
+        hull_padded = np.array(hull_padded, dtype=np.int32)
+
+        # Fill the padded convex hull
+        cv2.fillConvexPoly(mask, hull_padded, 255)
+
+        # Smooth the mask edges
+        mask = cv2.GaussianBlur(mask, (5, 5), 3)
+
+    return mask
+
+
+def apply_color_transfer(source, target):
+    """
+    Apply color transfer from target to source image
+    """
+    source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
+    target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
+
+    source_mean, source_std = cv2.meanStdDev(source)
+    target_mean, target_std = cv2.meanStdDev(target)
+
+    # Reshape mean and std to be broadcastable
+    source_mean = source_mean.reshape(1, 1, 3)
+    source_std = source_std.reshape(1, 1, 3)
+    target_mean = target_mean.reshape(1, 1, 3)
+    target_std = target_std.reshape(1, 1, 3)
+
+    # Perform the color transfer
+    source = (source - source_mean) * (target_std / source_std) + target_mean
+
+    return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
@@ -12,16 +12,23 @@ 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 mac
-if platform.system().lower() == 'darwin':
+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]
+    commands = [
+        "ffmpeg",
+        "-hide_banner",
+        "-hwaccel",
+        "auto",
+        "-loglevel",
+        modules.globals.log_level,
+    ]
    commands.extend(args)
    try:
        subprocess.check_output(commands, stderr=subprocess.STDOUT)
@@ -32,8 +39,19 @@ def run_ffmpeg(args: List[str]) -> bool:


 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]
-    output = subprocess.check_output(command).decode().strip().split('/')
+    command = [
+        "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:
        numerator, denominator = map(int, output)
        return numerator / denominator
@@ -44,25 +62,65 @@ def detect_fps(target_path: str) -> float:

 def extract_frames(target_path: str) -> None:
    temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
+    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:
    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])
+    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,
+        ]
+    )


 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:
@@ -81,7 +139,9 @@ def normalize_output_path(source_path: str, target_path: str, output_path: str)
        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 os.path.join(
+                output_path, source_name + "-" + target_name + target_extension
+            )
    return output_path


@@ -108,20 +168,20 @@ def clean_temp(target_path: str) -> None:


 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 bool(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 bool(mimetype and mimetype.startswith('video/'))
+        return bool(mimetype and mimetype.startswith("video/"))
    return False


@@ -129,12 +189,20 @@ def conditional_download(download_directory_path: str, urls: List[str]) -> None:
    if not os.path.exists(download_directory_path):
        os.makedirs(download_directory_path)
    for url in urls:
-        download_file_path = os.path.join(download_directory_path, os.path.basename(url))
+        download_file_path = os.path.join(
+            download_directory_path, os.path.basename(url)
+        )
        if not os.path.exists(download_file_path):
-            request = urllib.request.urlopen(url) # type: ignore[attr-defined]
-            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)) # type: ignore[attr-defined]
+            request = urllib.request.urlopen(url)  # type: ignore[attr-defined]
+            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))  # type: ignore[attr-defined]


 def resolve_relative_path(path: str) -> str:
@@ -0,0 +1,94 @@
+import cv2
+import numpy as np
+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
+
+        # 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":
+                # Windows-specific capture methods
+                capture_methods = [
+                    (self.device_index, cv2.CAP_DSHOW),  # Try DirectShow first
+                    (self.device_index, cv2.CAP_ANY),  # Then try default backend
+                    (-1, cv2.CAP_ANY),  # Try -1 as fallback
+                    (0, cv2.CAP_ANY),  # Finally try 0 without specific backend
+                ]
+
+                for dev_id, backend in capture_methods:
+                    try:
+                        self.cap = cv2.VideoCapture(dev_id, backend)
+                        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")
+
+            # Configure format
+            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)
+
+            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 set_frame_callback(self, callback: Callable[[np.ndarray], None]) -> None:
+        """Set callback for frame processing"""
+        self.frame_callback = callback
@@ -1,23 +1,21 @@
--extra-index-url https://download.pytorch.org/whl/cu118
+--extra-index-url https://download.pytorch.org/whl/cu128

-numpy==1.23.5
-opencv-python==4.8.1.78
-onnx==1.16.0
+numpy>=1.23.5,<2
+typing-extensions>=4.8.0
+opencv-python==4.10.0.84
+cv2_enumerate_cameras==1.1.15
+onnx==1.18.0
 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'
+pillow==11.1.0
+torch; sys_platform != 'darwin'
+torch==2.5.1; sys_platform == 'darwin'
+torchvision; sys_platform != 'darwin'
+torchvision==0.20.1; sys_platform == 'darwin'
 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'
+onnxruntime-gpu==1.22.0; sys_platform != 'darwin'
+tensorflow; sys_platform != 'darwin'
 opennsfw2==0.10.2
-protobuf==4.23.2
-tqdm==4.66.4
-gfpgan==1.3.8
+protobuf==4.25.1
@@ -1 +1 @@
-python run.py --execution-provider cuda --execution-threads 60 --max-memory 60
+python run.py --execution-provider cuda
@@ -0,0 +1 @@
+python run.py --execution-provider dml
@@ -1 +0,0 @@
-python run.py --execution-provider dml
@@ -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
@@ -1,122 +0,0 @@
-@echo off
-setlocal EnableDelayedExpansion
-
-:: 1. Setup your platform
-echo Setting up your platform...
-
-:: Python
-where python >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo Python is not installed. Please install Python 3.10 or later.
-    pause
-    exit /b
-)
-
-:: Pip
-where pip >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo Pip is not installed. Please install Pip.
-    pause
-    exit /b
-)
-
-:: Git
-where git >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo Git is not installed. Installing Git...
-    winget install --id Git.Git -e --source winget
-)
-
-:: FFMPEG
-where ffmpeg >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo FFMPEG is not installed. Installing 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
-if exist Deep-Live-Cam (
-    echo Deep-Live-Cam directory already exists.
-    set /p overwrite="Do you want to overwrite? (Y/N): "
-    if /i "%overwrite%"=="Y" (
-        rmdir /s /q Deep-Live-Cam
-        git clone https://github.com/hacksider/Deep-Live-Cam.git
-    ) else (
-        echo Skipping clone, using existing directory.
-    )
-) else (
-    git clone https://github.com/hacksider/Deep-Live-Cam.git
-)
-cd Deep-Live-Cam
-
-:: 3. Download Models
-echo Downloading models...
-mkdir models
-curl -L -o models/GFPGANv1.4.pth https://path.to.model/GFPGANv1.4.pth
-curl -L -o models/inswapper_128_fp16.onnx https://path.to.model/inswapper_128_fp16.onnx
-
-:: 4. Install dependencies
-echo Creating a virtual environment...
-python -m venv venv
-call venv\Scripts\activate
-
-echo Installing required Python packages...
-pip install --upgrade pip
-pip install -r requirements.txt
-
-echo Setup complete. You can now run the application.
-
-:: 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): "
-
-if "%choice%"=="1" (
-    echo Installing CUDA dependencies...
-    pip uninstall -y onnxruntime onnxruntime-gpu
-    pip install onnxruntime-gpu==1.16.3
-    set exec_provider="cuda"
-) else if "%choice%"=="2" (
-    echo Installing CoreML (Apple Silicon) dependencies...
-    pip uninstall -y onnxruntime onnxruntime-silicon
-    pip install onnxruntime-silicon==1.13.1
-    set exec_provider="coreml"
-) else if "%choice%"=="3" (
-    echo Installing CoreML (Apple Legacy) dependencies...
-    pip uninstall -y onnxruntime onnxruntime-coreml
-    pip install onnxruntime-coreml==1.13.1
-    set exec_provider="coreml"
-) else if "%choice%"=="4" (
-    echo Installing DirectML dependencies...
-    pip uninstall -y onnxruntime onnxruntime-directml
-    pip install onnxruntime-directml==1.15.1
-    set exec_provider="directml"
-) else if "%choice%"=="5" (
-    echo Installing OpenVINO dependencies...
-    pip uninstall -y onnxruntime onnxruntime-openvino
-    pip install onnxruntime-openvino==1.15.0
-    set exec_provider="openvino"
-) else (
-    echo Skipping GPU acceleration setup.
-)
-
-:: 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
-)
-
-pause