Revert "Merge pull request #566 from pereiraroland26/main"

This reverts commit 5d450b4352.
Merge pull request #566 from pereiraroland26/main
2024-10-04 15:57:48 +08:00 · 2024-10-04 15:55:31 +08:00 · 2024-09-23 11:59:23 +08:00 · 2024-09-23 02:49:48 +01:00 · 2024-09-23 02:48:59 +01:00 · 2024-09-23 02:25:14 +01:00
42 changed files with 1203 additions and 3543 deletions
@@ -1,26 +0,0 @@
-***[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
-
@@ -6,23 +6,17 @@ __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
-venv.rar
@@ -0,0 +1 @@
+3.10.14
@@ -1,38 +0,0 @@
-# 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.
@@ -1,326 +1,163 @@
-<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>
-
-<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>
-
-<p align="center">
-  <img src="media/demo.gif" alt="Demo GIF" width="800">
-</p>
-
-##  Disclaimer
-
-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.
-
- 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.
+![demo-gif](demo.gif)


-## Quick Start - Pre-built (Windows / Nvidia)
+## 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.

-  <a href="https://hacksider.gumroad.com/l/vccdmm"> <img src="https://github.com/user-attachments/assets/7d993b32-e3e8-4cd3-bbfb-a549152ebdd5" width="285" height="77" />
+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.

-##### This is the fastest build you can get if you have a discrete NVIDIA GPU.
- 
-###### 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.
+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.

-## 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!
+## How do I install it?

-## 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 prebuilt 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 (3.10 recommended)
+### 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)
 -   pip
 -   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/)
+-   [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

-**2. Clone the Repository**
+#### 3. Download Models

-```bash
-git clone https://github.com/hacksider/Deep-Live-Cam.git
-cd Deep-Live-Cam
+ 1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
+ 2. [inswapper_128_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128.onnx)
+
+Then put those 2 files on the "**models**" folder
+
+#### 4. Install dependency
+We highly recommend to work with a  `venv`  to avoid issues.
 ```
-
-**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.

-**For macOS:**
+### *Proceed if you want to use GPU Acceleration
+### CUDA Execution Provider (Nvidia)*

-Apple Silicon (M1/M2/M3) requires specific setup:
+1.  Install  [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive)
+    
+2.  Install dependencies:
+    

-```bash
-# Install Python 3.10 (specific version is important)
-brew install python@3.10
-
-# Install tkinter package (required for the GUI)
-brew install python-tk@3.10
-
-# Create and activate virtual environment with Python 3.10
-python3.10 -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 11.8.0](https://developer.nvidia.com/cuda-11-8-0-download-archive)
-2. Install dependencies:
-
-```bash
 pip uninstall onnxruntime onnxruntime-gpu
 pip install onnxruntime-gpu==1.16.3
+
 ```

-3. Usage:
+3.  Usage in case the provider is available:

-```bash
+```
 python run.py --execution-provider cuda
+
 ```

-**CoreML Execution Provider (Apple Silicon)**
+### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#coreml-execution-provider-apple-silicon)CoreML Execution Provider (Apple Silicon)

-Apple Silicon (M1/M2/M3) specific installation:
+1.  Install dependencies:

-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
+
 ```

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

-```bash
-python3.10 run.py --execution-provider coreml
 ```

-**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 3.10
-  brew cleanup
-  ```
+### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#coreml-execution-provider-apple-legacy)CoreML Execution Provider (Apple Legacy)

-**CoreML Execution Provider (Apple Legacy)**
+1.  Install dependencies:

-1. Install dependencies:
-
-```bash
+```
 pip uninstall onnxruntime onnxruntime-coreml
 pip install onnxruntime-coreml==1.13.1
+
 ```

-2. Usage:
+2.  Usage in case the provider is available:

-```bash
+```
 python run.py --execution-provider coreml
+
 ```

-**DirectML Execution Provider (Windows)**
+### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#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
+
 ```

-2. Usage:
+2.  Usage in case the provider is available:

-```bash
+```
 python run.py --execution-provider directml
+
 ```

-**OpenVINO™ Execution Provider (Intel)**
+### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#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
+
 ```

-2. Usage:
+2.  Usage in case the provider is available:

-```bash
+```
 python run.py --execution-provider openvino
 ```
-</details>

-## Usage
+## How do I use it?
+> Note: When you run this program for the first time, it will download some models ~300MB in size.

-**1. Image/Video Mode**
+Executing `python run.py` command will launch this window:
+![gui-demo](instruction.png)

-   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.
+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.

-**2. Webcam Mode**
+## 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)

-   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.
+![demo-gif](demo.gif)

-## Tips and Tricks
+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).

-Check out these helpful guides to get the most out of Deep-Live-Cam:
+You can now use the virtual camera output (uses pyvirtualcam) by turning on the `Virtual Cam Output (OBS)` toggle which should output to the OBS Virtual Camera. Note: this may not work on macOS. You will get a preview as before, but now you will also have a virtual camera output which can be used in applications like Zoom.

- [Unlocking the Secrets to the Perfect Deepfake Image](https://deeplivecam.net/index.php/blog/tips-and-tricks/unlocking-the-secrets-to-the-perfect-deepfake-image) - Learn how to create the best deepfake with full head coverage
- [Video Call with DeepLiveCam](https://deeplivecam.net/index.php/blog/tips-and-tricks/video-call-with-deeplivecam) - Make your meetings livelier by using DeepLiveCam with OBS and meeting software
- [Have a Special Guest!](https://deeplivecam.net/index.php/blog/tips-and-tricks/have-a-special-guest) - Tutorial on how to use face mapping to add special guests to your stream
- [Watch Deepfake Movies in Realtime](https://deeplivecam.net/index.php/blog/tips-and-tricks/watch-deepfake-movies-in-realtime) - See yourself star in any video without processing the video
- [Better Quality without Sacrificing Speed](https://deeplivecam.net/index.php/blog/tips-and-tricks/better-quality-without-sacrificing-speed) - Tips for achieving better results without impacting performance
- [Instant Vtuber!](https://deeplivecam.net/index.php/blog/tips-and-tricks/instant-vtuber) - Create a new persona/vtuber easily using Metahuman Creator
+Additional command line arguments are given below. To learn out what they do, check [this guide](https://github.com/s0md3v/roop/wiki/Advanced-Options).

-Visit our [official blog](https://deeplivecam.net/index.php/blog/tips-and-tricks) for more tips and tutorials.
-
-## Command Line Arguments (Unmaintained)

 ```
 options:
  -h, --help                                               show this help message and exit
-  -s SOURCE_PATH, --source SOURCE_PATH                     select a source image
-  -t TARGET_PATH, --target TARGET_PATH                     select a target image or video
+  -s SOURCE_PATH, --source SOURCE_PATH                     select an source image
+  -t TARGET_PATH, --target TARGET_PATH                     select an 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, ...)
+  --frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...]  frame processors (choices: face_swapper, face_enhancer, super_resolution...)
  --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
@@ -328,54 +165,24 @@ options:
  --max-memory MAX_MEMORY                                  maximum amount of RAM in GB
  --execution-provider {cpu} [{cpu} ...]                   available execution provider (choices: cpu, ...)
  --execution-threads EXECUTION_THREADS                    number of execution threads
+  --headless                                               run in headless mode
+  --enhancer-upscale-factor                                Sets the upscale factor for the enhancer. Only applies if `face_enhancer` is set as a frame-processor
+  --source-image-scaling-factor                            Set the upscale factor for source images. Only applies if `face_swapper` is set as a frame-processor
+  -r SCALE, --super-resolution-scale-factor SCALE          Super resolution scale factor, choices are 2, 3, 4
  -v, --version                                            show program's version number and exit
 ```

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

-## Press
+To improve the video quality, you can use the `super_resolution` frame processor after swapping the faces. It will enhance the video quality by 2x, 3x or 4x. You can set the upscale factor using the `-r` or `--super-resolution-scale-factor` argument. 
+Processing time will increase with the upscale factor, but it's quite quick.

-**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
+```

 ## Credits
-
-   [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>
+- [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.
@@ -1,46 +0,0 @@
-{
-    "Source x Target Mapper": "Source x Target Mapper",
-    "select an source image": "选择一个源图像",
-    "Preview": "预览",
-    "select an 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 an 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",
-    "Atleast 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。"
-}
@@ -1,4 +1 @@
-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
+just put the models in this folder
@@ -1,32 +1,38 @@
-from typing import Any
+from typing import Any, Optional
 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:
+def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Any]:
+    """Retrieve a specific frame from a video."""
    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))
+    if not capture.isOpened():
+        print(f"Error: Cannot open video file {video_path}")
+        return None
+
+    frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # Ensure frame_number is within the valid range
+    frame_number = max(0, min(frame_number, frame_total - 1))
+
+    capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
    has_frame, frame = capture.read()
-
-    if has_frame and modules.globals.color_correction:
-        # Convert the frame color if necessary
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
    capture.release()
-    return frame if has_frame else None

+    if not has_frame:
+        print(f"Error: Cannot read frame {frame_number} from {video_path}")
+        return None
+    
+    return frame

 def get_video_frame_total(video_path: str) -> int:
+    """Get the total number of frames in a video."""
    capture = cv2.VideoCapture(video_path)
-    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    if not capture.isOpened():
+        print(f"Error: Cannot open video file {video_path}")
+        return 0
+    
+    frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
    capture.release()
-    return video_frame_total
+
+    return frame_total
@@ -1,32 +0,0 @@
-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
@@ -1,189 +1,27 @@
-import os
-import shutil
-from typing import Any
+from typing import Any, Optional
 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
+FACE_ANALYSER: Optional[insightface.app.FaceAnalysis] = None

-
-def get_face_analyser() -> Any:
+def get_face_analyser() -> insightface.app.FaceAnalysis:
    global FACE_ANALYSER

    if FACE_ANALYSER is None:
-        FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
+        FACE_ANALYSER = insightface.app.FaceAnalysis(
+            name='buffalo_l', 
+            providers=modules.globals.execution_providers
+        )
        FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+    
    return FACE_ANALYSER

+def get_one_face(frame: Frame) -> Optional[Any]:
+    faces = get_face_analyser().get(frame)
+    return min(faces, key=lambda x: x.bbox[0], default=None)

-def get_one_face(frame: Frame) -> Any:
-    face = get_face_analyser().get(frame)
-    try:
-        return min(face, key=lambda x: x.bbox[0])
-    except ValueError:
-        return None
-
-
-def get_many_faces(frame: Frame) -> Any:
-    try:
-        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
+def get_many_faces(frame: Frame) -> Optional[Any]:
+    faces = get_face_analyser().get(frame)
+    return faces if faces else None
@@ -1,26 +0,0 @@
-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)
@@ -1,43 +1,35 @@
 import os
-from typing import List, Dict, Any
+from typing import List, Dict

 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 = 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
+keep_fps = None
+keep_audio = None
+keep_frames = None
+many_faces = None
 video_encoder = None
 video_quality = None
-live_mirror = False
-live_resizable = True
+live_mirror = None
+live_resizable = None
 max_memory = None
 execution_providers: List[str] = []
 execution_threads = None
 headless = None
-log_level = "error"
-fp_ui: Dict[str, bool] = {"face_enhancer": False}
+log_level = 'error'
+fp_ui: Dict[str, bool] = {}
+nsfw = None
 camera_input_combobox = None
 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
+enhancer_upscale_factor = 1
+source_image_scaling_factor = 2
+sr_scale_factor = 4
@@ -1,3 +1,3 @@
-name = 'Deep-Live-Cam'
-version = '1.8'
-edition = 'GitHub Edition'
+name = 'Deep Live Cam'
+version = '1.3.0'
+edition = 'Portable'
@@ -1,9 +1,6 @@
-import numpy
+import numpy as np
 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
@@ -12,24 +9,17 @@ MAX_PROBABILITY = 0.85
 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)
-        
+    global model
+    if model is None: model = opennsfw2.make_open_nsfw_model()
    image = Image.fromarray(target_frame)
    image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
-    global model
-    if model is None: 
-        model = opennsfw2.make_open_nsfw_model()
-        
-    views = numpy.expand_dims(image, axis=0)
+    views = np.expand_dims(image, axis=0)
    _, probability = model.predict(views)[0]
    return probability > MAX_PROBABILITY

-
 def predict_image(target_path: str) -> bool:
-    return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
-
+    probability = opennsfw2.predict_image(target_path)
+    return probability > MAX_PROBABILITY

 def predict_video(target_path: str) -> bool:
    _, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100)
@@ -17,57 +17,56 @@ FRAME_PROCESSORS_INTERFACE = [
    'process_video'
 ]

-
-def load_frame_processor_module(frame_processor: str) -> Any:
+def load_frame_processor_module(frame_processor: str) -> ModuleType:
    try:
        frame_processor_module = importlib.import_module(f'modules.processors.frame.{frame_processor}')
+        # Ensure all required methods are present
        for method_name in FRAME_PROCESSORS_INTERFACE:
            if not hasattr(frame_processor_module, method_name):
-                sys.exit()
+                raise AttributeError(f"Missing required method {method_name} in {frame_processor} module.")
    except ImportError:
-        print(f"Frame processor {frame_processor} not found")
-        sys.exit()
+        print(f"Error: Frame processor '{frame_processor}' not found.")
+        sys.exit(1)
+    except AttributeError as e:
+        print(e)
+        sys.exit(1)
+    
    return frame_processor_module

-
 def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
    global FRAME_PROCESSORS_MODULES

    if not FRAME_PROCESSORS_MODULES:
-        for frame_processor in frame_processors:
-            frame_processor_module = load_frame_processor_module(frame_processor)
-            FRAME_PROCESSORS_MODULES.append(frame_processor_module)
+        FRAME_PROCESSORS_MODULES = [load_frame_processor_module(fp) for fp in frame_processors]
+    
    set_frame_processors_modules_from_ui(frame_processors)
    return FRAME_PROCESSORS_MODULES

 def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
    global FRAME_PROCESSORS_MODULES
    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)
+        if state and frame_processor not in frame_processors:
+            module = load_frame_processor_module(frame_processor)
+            FRAME_PROCESSORS_MODULES.append(module)
            modules.globals.frame_processors.append(frame_processor)
-        if state == False:
-            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
+        elif not state and frame_processor in frame_processors:
+            module = load_frame_processor_module(frame_processor)
+            FRAME_PROCESSORS_MODULES.remove(module)
+            modules.globals.frame_processors.remove(frame_processor)

 def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
    with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
-        futures = []
-        for path in temp_frame_paths:
-            future = executor.submit(process_frames, source_path, [path], progress)
-            futures.append(future)
+        futures = [executor.submit(process_frames, source_path, [path], progress) for path in temp_frame_paths]
        for future in futures:
            future.result()

-
-def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
+def process_video(source_path: str, frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
    progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
    total = len(frame_paths)
    with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
-        progress.set_postfix({'execution_providers': modules.globals.execution_providers, 'execution_threads': modules.globals.execution_threads, 'max_memory': modules.globals.max_memory})
+        progress.set_postfix({
+            'execution_providers': modules.globals.execution_providers, 
+            'execution_threads': modules.globals.execution_threads, 
+            'max_memory': modules.globals.max_memory
+        })
        multi_process_frame(source_path, frame_paths, process_frames, progress)
@@ -8,82 +8,52 @@ import modules.globals
 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
-import platform
-import torch
-from modules.utilities import (
-    conditional_download,
-    is_image,
-    is_video,
-)
+from modules.typing import Frame, Face  # Ensure these are imported
+from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video

 FACE_ENHANCER = None
 THREAD_SEMAPHORE = threading.Semaphore()
 THREAD_LOCK = threading.Lock()
-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"
-)
-
+NAME = 'DLC.FACE-ENHANCER'

 def pre_check() -> bool:
-    download_directory_path = models_dir
-    conditional_download(
-        download_directory_path,
-        [
-            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
-        ],
-    )
+    download_directory_path = resolve_relative_path('..\models')
+    conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth'])
    return True

-
 def pre_start() -> bool:
-    if not is_image(modules.globals.target_path) and not is_video(
-        modules.globals.target_path
-    ):
-        update_status("Select an image or video for target path.", NAME)
+    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
+        update_status('Select an image or video for target path.', NAME)
        return False
    return True

-
 def get_face_enhancer() -> Any:
    global FACE_ENHANCER

    with THREAD_LOCK:
        if FACE_ENHANCER is None:
-            model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
-            
-            match platform.system():
-                case "Darwin":  # Mac OS
-                    if torch.backends.mps.is_available():
-                        mps_device = torch.device("mps")
-                        FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=mps_device)  # type: ignore[attr-defined]
-                    else:
-                        FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1)  # type: ignore[attr-defined]
-                case _:  # Other OS
-                    FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1)  # type: ignore[attr-defined]
-
+            model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
+            FACE_ENHANCER = gfpgan.GFPGANer(
+                model_path=model_path,
+                upscale=modules.globals.enhancer_upscale_factor
+            )  # type: ignore[attr-defined]
    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

-
 def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
    target_face = get_one_face(temp_frame)
    if target_face:
        temp_frame = enhance_face(temp_frame)
    return temp_frame

-
-def process_frames(
-    source_path: str, temp_frame_paths: List[str], progress: Any = None
-) -> None:
+def process_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)
@@ -91,19 +61,10 @@ def process_frames(
        if progress:
            progress.update(1)

-
 def process_image(source_path: str, target_path: str, output_path: str) -> None:
    target_frame = cv2.imread(target_path)
    result = process_frame(None, target_frame)
    cv2.imwrite(output_path, result)

-
 def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
    modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
-
-
-def process_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
@@ -2,621 +2,103 @@ from typing import Any, List
 import cv2
 import insightface
 import threading
-import numpy as np
+import os
+
 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, default_source_face
+from modules.face_analyser import get_one_face, get_many_faces
 from modules.typing import Face, Frame
-from modules.utilities import (
-    conditional_download,
-    is_image,
-    is_video,
-)
-from modules.cluster_analysis import find_closest_centroid
-import os
+from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
+import numpy as np

 FACE_SWAPPER = None
 THREAD_LOCK = threading.Lock()
-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"
-)
-
+NAME = 'DLC.FACE-SWAPPER'

 def pre_check() -> bool:
-    download_directory_path = abs_dir
-    conditional_download(
-        download_directory_path,
-        [
-            "https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
-        ],
-    )
+    download_directory_path = resolve_relative_path('../models')
+    conditional_download(download_directory_path, [
+        'https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128.onnx'
+    ])
    return True

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

-
 def get_face_swapper() -> Any:
    global FACE_SWAPPER

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

+def upscale_image(image: np.ndarray, scaling_factor: int = modules.globals.source_image_scaling_factor) -> np.ndarray:
+    """
+    Upscales the given image by the specified scaling factor.
+
+    Args:
+        image (np.ndarray): The input image to upscale.
+        scaling_factor (int): The factor by which to upscale the image.
+
+    Returns:
+        np.ndarray: The upscaled image.
+    """
+    height, width = image.shape[:2]
+    new_size = (width * scaling_factor, height * scaling_factor)
+    upscaled_image = cv2.resize(image, new_size, interpolation=cv2.INTER_CUBIC)
+    return upscaled_image

 def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    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
-
+    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)

 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:
-                if source_face and target_face:
-                    temp_frame = swap_face(source_face, target_face, temp_frame)
-                else:
-                    print("Face detection failed for target/source.")
+                temp_frame = swap_face(source_face, target_face, temp_frame)
    else:
        target_face = get_one_face(temp_frame)
-        if target_face and source_face:
+        if target_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_image = cv2.imread(source_path)
+    if source_image is None:
+        print(f"Failed to load source image from {source_path}")
+        return
+    # Upscale the source image for better quality
+    source_image_upscaled = upscale_image(source_image, scaling_factor=2)
+    source_face = get_one_face(source_image_upscaled)

-
-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)
-
+    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(f"Error processing frame {temp_frame_path}: {exception}")
+        if progress:
+            progress.update(1)

 def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    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)
-
+    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)

 def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    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)
+    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
@@ -0,0 +1,197 @@
+import threading
+import traceback
+from typing import Any, List
+import cv2
+
+import os
+
+import modules.globals
+import modules.processors.frame.core
+from modules.core import update_status
+from modules.face_analyser import get_one_face
+from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
+import numpy as np
+
+NAME = 'DLC.SUPER-RESOLUTION'
+THREAD_SEMAPHORE = threading.Semaphore()
+
+# Singleton class for Super-Resolution
+class SuperResolutionModel:
+    _instance = None
+    _lock = threading.Lock()
+
+    def __init__(self, sr_model_path: str = f'ESPCN_x{modules.globals.sr_scale_factor}.pb'):
+        if SuperResolutionModel._instance is not None:
+            raise Exception("This class is a singleton!")
+        self.sr = cv2.dnn_superres.DnnSuperResImpl_create()
+        self.model_path = os.path.join(resolve_relative_path('../models'), sr_model_path)
+        if not os.path.exists(self.model_path):
+            raise FileNotFoundError(f"Super-resolution model not found at {self.model_path}")
+        try:
+            self.sr.readModel(self.model_path)
+            self.sr.setModel("espcn", modules.globals.sr_scale_factor)  # Using ESPCN with 2,3 or 4x upscaling
+        except Exception as e:
+            print(f"Error during super-resolution model initialization: {e}")
+            raise e
+
+    @classmethod
+    def get_instance(cls, sr_model_path: str = f'ESPCN_x{modules.globals.sr_scale_factor}.pb'):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    try:
+                        cls._instance = cls(sr_model_path)
+                    except Exception as e:
+                        raise RuntimeError(f"Failed to initialize SuperResolution: {str(e)}")
+        return cls._instance
+
+
+def pre_check() -> bool:
+    """
+    Checks and downloads necessary models before starting the face swapper.
+    """
+    download_directory_path = resolve_relative_path('../models')
+    # Download the super-resolution model as well
+    conditional_download(download_directory_path, [
+        f'https://huggingface.co/spaces/PabloGabrielSch/AI_Resolution_Upscaler_And_Resizer/resolve/bcd13b766a9499196e8becbe453c4a848673b3b6/models/ESPCN_x{modules.globals.sr_scale_factor}.pb'
+    ])
+    return True
+
+def pre_start() -> bool:
+    if not is_image(modules.globals.source_path):
+        update_status('Select an image for source path.', NAME)
+        return False
+    elif not get_one_face(cv2.imread(modules.globals.source_path)):
+        update_status('No face detected in the source path.', NAME)
+        return False
+    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
+        update_status('Select an image or video for target path.', NAME)
+        return False
+    return True
+
+
+def apply_super_resolution(image: np.ndarray) -> np.ndarray:
+    """
+    Applies super-resolution to the given image using the provided super-resolver.
+
+    Args:
+        image (np.ndarray): The input image to enhance.
+        sr_model_path (str): ESPCN model path for super-resolution.
+
+    Returns:
+        np.ndarray: The super-resolved image.
+    """
+    with THREAD_SEMAPHORE:
+        sr_model = SuperResolutionModel.get_instance()
+
+        if sr_model is None:
+            print("Super-resolution model is not initialized.")
+            return image
+        try:
+            upscaled_image = sr_model.sr.upsample(image)
+            return upscaled_image
+        except Exception as e:
+            print(f"Error during super-resolution: {e}")
+            return image
+
+
+def process_frame(frame: np.ndarray) -> np.ndarray:
+    """
+    Processes a single frame by swapping the source face into detected target faces.
+
+    Args:
+
+        frame (np.ndarray): The target frame image.
+
+    Returns:
+        np.ndarray: The processed frame with swapped faces.
+    """
+
+    # Apply super-resolution to the entire frame
+    frame = apply_super_resolution(frame)
+
+    return frame
+
+def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
+    """
+    Processes multiple frames by swapping the source face into each target frame.
+
+    Args:
+        source_path (str): Path to the source image.
+        temp_frame_paths (List[str]): List of paths to target frame images.
+        progress (Any, optional): Progress tracker. Defaults to None.
+    """
+    for idx, temp_frame_path in enumerate(temp_frame_paths):
+        frame = cv2.imread(temp_frame_path)
+        if frame is None:
+            print(f"Failed to load frame from {temp_frame_path}")
+            continue
+        try:
+            result = process_frame(frame)
+            cv2.imwrite(temp_frame_path, result)
+        except Exception as exception:
+            traceback.print_exc()
+            print(f"Error processing frame {temp_frame_path}: {exception}")
+        if progress:
+            progress.update(1)
+
+def upscale_image(image: np.ndarray, scaling_factor: int = 2) -> np.ndarray:
+    """
+    Upscales the given image by the specified scaling factor.
+
+    Args:
+        image (np.ndarray): The input image to upscale.
+        scaling_factor (int): The factor by which to upscale the image.
+
+    Returns:
+        np.ndarray: The upscaled image.
+    """
+    height, width = image.shape[:2]
+    new_size = (width * scaling_factor, height * scaling_factor)
+    upscaled_image = cv2.resize(image, new_size, interpolation=cv2.INTER_CUBIC)
+    return upscaled_image
+
+def process_image(source_path: str, target_path: str, output_path: str) -> None:
+    """
+    Processes a single image by swapping the source face into the target image.
+
+    Args:
+        source_path (str): Path to the source image.
+        target_path (str): Path to the target image.
+        output_path (str): Path to save the output image.
+    """
+    source_image = cv2.imread(source_path)
+    if source_image is None:
+        print(f"Failed to load source image from {source_path}")
+        return
+
+    # Upscale the source image for better quality before face detection
+    source_image_upscaled = upscale_image(source_image, scaling_factor=2)
+
+    # Detect source face from the upscaled image
+    source_face = get_one_face(source_image_upscaled)
+    if source_face is None:
+        print("No source face detected.")
+        return
+
+    target_frame = cv2.imread(target_path)
+    if target_frame is None:
+        print(f"Failed to load target image from {target_path}")
+        return
+
+    # Process the frame
+    result = process_frame(target_frame)
+
+    # Save the processed frame
+    cv2.imwrite(output_path, result)
+
+
+def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
+    """
+    Processes a video by swapping the source face into each frame.
+
+    Args:
+        source_path (str): Path to the source image.
+        temp_frame_paths (List[str]): List of paths to video frame images.
+    """
+    modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
@@ -1,76 +1,57 @@
 {
  "CTk": {
-    "fg_color": ["gray95", "gray10"]
+    "fg_color": ["#FFFFFF", "#2D2D2D"]
  },
  "CTkToplevel": {
-    "fg_color": ["gray95", "gray10"]
+    "fg_color": ["#FFFFFF", "#2D2D2D"]
  },
  "CTkFrame": {
    "corner_radius": 0,
    "border_width": 0,
-    "fg_color": ["gray90", "gray13"],
-    "top_fg_color": ["gray85", "gray16"],
-    "border_color": ["gray65", "gray28"]
+    "fg_color": ["#F0F0F0", "#3C3C3C"],
+    "top_fg_color": ["#E0E0E0", "#4B4B4B"],
+    "border_color": ["#B0B0B0", "#5A5A5A"]
  },
  "CTkButton": {
    "corner_radius": 0,
    "border_width": 0,
-    "fg_color": ["#2aa666", "#1f538d"],
-    "hover_color": ["#3cb666", "#14375e"],
-    "border_color": ["#3e4a40", "#949A9F"],
-    "text_color": ["#f3faf6", "#f3faf6"],
+    "fg_color": ["#007ACC", "#007ACC"],
+    "hover_color": ["#005EA3", "#005EA3"],
+    "border_color": ["#004C8A", "#004C8A"],
+    "text_color": ["#FFFFFF", "#FFFFFF"],
    "text_color_disabled": ["gray74", "gray60"]
  },
  "CTkLabel": {
    "corner_radius": 0,
    "fg_color": "transparent",
-    "text_color": ["gray14", "gray84"]
+    "text_color": ["#000000", "#FFFFFF"]
  },
  "CTkEntry": {
    "corner_radius": 0,
    "border_width": 2,
-    "fg_color": ["#F9F9FA", "#343638"],
-    "border_color": ["#979DA2", "#565B5E"],
-    "text_color": ["gray14", "gray84"],
+    "fg_color": ["#FFFFFF", "#333333"],
+    "border_color": ["#A0A0A0", "#5A5A5A"],
+    "text_color": ["#000000", "#FFFFFF"],
    "placeholder_text_color": ["gray52", "gray62"]
  },
-  "CTkCheckbox": {
-    "corner_radius": 0,
-    "border_width": 3,
-    "fg_color": ["#2aa666", "#1f538d"],
-    "border_color": ["#3e4a40", "#949A9F"],
-    "hover_color": ["#3cb666", "#14375e"],
-    "checkmark_color": ["#f3faf6", "gray90"],
-    "text_color": ["gray14", "gray84"],
-    "text_color_disabled": ["gray60", "gray45"]
-  },
  "CTkSwitch": {
    "corner_radius": 1000,
    "border_width": 3,
    "button_length": 0,
    "fg_color": ["#939BA2", "#4A4D50"],
    "progress_color": ["#2aa666", "#1f538d"],
-    "button_color": ["gray36", "#D5D9DE"],
-    "button_hover_color": ["gray20", "gray100"],
-    "text_color": ["gray14", "gray84"],
+    "button_color": ["#444444", "#D5D9DE"],
+    "button_hover_color": ["#333333", "#FFFFFF"],
+    "text_color": ["#000000", "#FFFFFF"],
    "text_color_disabled": ["gray60", "gray45"]
  },
-  "CTkRadiobutton": {
-    "corner_radius": 1000,
-    "border_width_checked": 6,
-    "border_width_unchecked": 3,
+  "CTkOptionMenu": {
+    "corner_radius": 0,
    "fg_color": ["#2aa666", "#1f538d"],
-    "border_color": ["#3e4a40", "#949A9F"],
-    "hover_color": ["#3cb666", "#14375e"],
-    "text_color": ["gray14", "gray84"],
-    "text_color_disabled": ["gray60", "gray45"]
-  },
-  "CTkProgressBar": {
-    "corner_radius": 1000,
-    "border_width": 0,
-    "fg_color": ["#939BA2", "#4A4D50"],
-    "progress_color": ["#2aa666", "#1f538d"],
-    "border_color": ["gray", "gray"]
+    "button_color": ["#3cb666", "#14375e"],
+    "button_hover_color": ["#234567", "#1e2c40"],
+    "text_color": ["#FFFFFF", "#FFFFFF"],
+    "text_color_disabled": ["gray74", "gray60"]
  },
  "CTkSlider": {
    "corner_radius": 1000,
@@ -82,59 +63,6 @@
    "button_color": ["#2aa666", "#1f538d"],
    "button_hover_color": ["#3cb666", "#14375e"]
  },
-  "CTkOptionMenu": {
-    "corner_radius": 0,
-    "fg_color": ["#2aa666", "#1f538d"],
-    "button_color": ["#3cb666", "#14375e"],
-    "button_hover_color": ["#234567", "#1e2c40"],
-    "text_color": ["#f3faf6", "#f3faf6"],
-    "text_color_disabled": ["gray74", "gray60"]
-  },
-  "CTkComboBox": {
-    "corner_radius": 0,
-    "border_width": 2,
-    "fg_color": ["#F9F9FA", "#343638"],
-    "border_color": ["#979DA2", "#565B5E"],
-    "button_color": ["#979DA2", "#565B5E"],
-    "button_hover_color": ["#6E7174", "#7A848D"],
-    "text_color": ["gray14", "gray84"],
-    "text_color_disabled": ["gray50", "gray45"]
-  },
-  "CTkScrollbar": {
-    "corner_radius": 1000,
-    "border_spacing": 4,
-    "fg_color": "transparent",
-    "button_color": ["gray55", "gray41"],
-    "button_hover_color": ["gray40", "gray53"]
-  },
-  "CTkSegmentedButton": {
-    "corner_radius": 0,
-    "border_width": 2,
-    "fg_color": ["#979DA2", "gray29"],
-    "selected_color": ["#2aa666", "#1f538d"],
-    "selected_hover_color": ["#3cb666", "#14375e"],
-    "unselected_color": ["#979DA2", "gray29"],
-    "unselected_hover_color": ["gray70", "gray41"],
-    "text_color": ["#f3faf6", "#f3faf6"],
-    "text_color_disabled": ["gray74", "gray60"]
-  },
-  "CTkTextbox": {
-    "corner_radius": 0,
-    "border_width": 0,
-    "fg_color": ["gray100", "gray20"],
-    "border_color": ["#979DA2", "#565B5E"],
-    "text_color": ["gray14", "gray84"],
-    "scrollbar_button_color": ["gray55", "gray41"],
-    "scrollbar_button_hover_color": ["gray40", "gray53"]
-  },
-  "CTkScrollableFrame": {
-    "label_fg_color": ["gray80", "gray21"]
-  },
-  "DropdownMenu": {
-    "fg_color": ["gray90", "gray20"],
-    "hover_color": ["gray75", "gray28"],
-    "text_color": ["gray14", "gray84"]
-  },
  "CTkFont": {
    "macOS": {
      "family": "Avenir",
@@ -152,7 +80,12 @@
      "weight": "normal"
    }
  },
+  "DropdownMenu": {
+    "fg_color": ["#FFFFFF", "#2D2D2D"],
+    "hover_color": ["#E0E0E0", "#4B4B4B"],
+    "text_color": ["#000000", "#FFFFFF"]
+  },
  "URL": {
-    "text_color": ["gray74", "gray60"]
+    "text_color": ["#007ACC", "#1E90FF"]
  }
 }
@@ -5,205 +5,133 @@ import platform
 import shutil
 import ssl
 import subprocess
-import urllib
+import urllib.request
 from pathlib import Path
 from typing import List, Any
 from tqdm import tqdm

 import modules.globals

-TEMP_FILE = "temp.mp4"
-TEMP_DIRECTORY = "temp"
+TEMP_FILE = 'temp.mp4'
+TEMP_DIRECTORY = 'temp'

-# monkey patch ssl for mac
-if platform.system().lower() == "darwin":
+# Monkey patch SSL for macOS to handle issues with some HTTPS requests
+if platform.system().lower() == 'darwin':
    ssl._create_default_https_context = ssl._create_unverified_context

-
 def run_ffmpeg(args: List[str]) -> bool:
-    commands = [
-        "ffmpeg",
-        "-hide_banner",
-        "-hwaccel",
-        "auto",
-        "-loglevel",
-        modules.globals.log_level,
-    ]
+    commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-loglevel', modules.globals.log_level]
    commands.extend(args)
    try:
        subprocess.check_output(commands, stderr=subprocess.STDOUT)
        return True
-    except Exception:
-        pass
+    except subprocess.CalledProcessError as e:
+        print(f"FFmpeg error: {e.output.decode()}")
    return False

-
 def detect_fps(target_path: str) -> float:
    command = [
-        "ffprobe",
-        "-v",
-        "error",
-        "-select_streams",
-        "v:0",
-        "-show_entries",
-        "stream=r_frame_rate",
-        "-of",
-        "default=noprint_wrappers=1:nokey=1",
-        target_path,
+        'ffprobe', '-v', 'error', '-select_streams', 'v:0', 
+        '-show_entries', 'stream=r_frame_rate', 
+        '-of', 'default=noprint_wrappers=1:nokey=1', target_path
    ]
-    output = subprocess.check_output(command).decode().strip().split("/")
    try:
+        output = subprocess.check_output(command).decode().strip().split('/')
        numerator, denominator = map(int, output)
        return numerator / denominator
-    except Exception:
-        pass
+    except (subprocess.CalledProcessError, ValueError):
+        print("Failed to detect FPS, defaulting to 30.0 FPS.")
    return 30.0

-
 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"),
-        ]
-    )
-
+    create_temp(target_path)
+    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])

 def create_video(target_path: str, fps: float = 30.0) -> None:
    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:
-    target_name, _ = os.path.splitext(os.path.basename(target_path))
-    target_directory_path = os.path.dirname(target_path)
-    return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
-
+    target_name = Path(target_path).stem
+    target_directory_path = Path(target_path).parent
+    return str(target_directory_path / TEMP_DIRECTORY / target_name)

 def get_temp_output_path(target_path: str) -> str:
    temp_directory_path = get_temp_directory_path(target_path)
-    return os.path.join(temp_directory_path, TEMP_FILE)
+    return str(Path(temp_directory_path) / TEMP_FILE)

-
-def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
-    if source_path and target_path:
-        source_name, _ = os.path.splitext(os.path.basename(source_path))
-        target_name, target_extension = os.path.splitext(os.path.basename(target_path))
-        if os.path.isdir(output_path):
-            return os.path.join(
-                output_path, source_name + "-" + target_name + target_extension
-            )
+def normalize_output_path(source_path: str, target_path: str, output_path: str) -> str:
+    if source_path and target_path and os.path.isdir(output_path):
+        source_name = Path(source_path).stem
+        target_name = Path(target_path).stem
+        target_extension = Path(target_path).suffix
+        return str(Path(output_path) / f"{source_name}-{target_name}{target_extension}")
    return output_path

-
 def create_temp(target_path: str) -> None:
    temp_directory_path = get_temp_directory_path(target_path)
    Path(temp_directory_path).mkdir(parents=True, exist_ok=True)

-
 def move_temp(target_path: str, output_path: str) -> None:
    temp_output_path = get_temp_output_path(target_path)
    if os.path.isfile(temp_output_path):
-        if os.path.isfile(output_path):
-            os.remove(output_path)
        shutil.move(temp_output_path, output_path)

-
 def clean_temp(target_path: str) -> None:
    temp_directory_path = get_temp_directory_path(target_path)
-    parent_directory_path = os.path.dirname(temp_directory_path)
+    parent_directory_path = Path(temp_directory_path).parent
    if not modules.globals.keep_frames and os.path.isdir(temp_directory_path):
        shutil.rmtree(temp_directory_path)
-    if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
-        os.rmdir(parent_directory_path)
-
+    if parent_directory_path.exists() and not list(parent_directory_path.iterdir()):
+        parent_directory_path.rmdir()

 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 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 mimetype and mimetype.startswith('video/')
    return False

-
 def conditional_download(download_directory_path: str, urls: List[str]) -> None:
-    if not os.path.exists(download_directory_path):
-        os.makedirs(download_directory_path)
+    download_directory = Path(download_directory_path)
+    download_directory.mkdir(parents=True, exist_ok=True)
    for url in urls:
-        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]
-
+        download_file_path = download_directory / Path(url).name
+        if not download_file_path.exists():
+            with urllib.request.urlopen(url) as request:
+                total = int(request.headers.get('Content-Length', 0))
+                with tqdm(total=total, desc='Downloading', unit='B', unit_scale=True, unit_divisor=1024) as progress:
+                    urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size))

 def resolve_relative_path(path: str) -> str:
-    return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
+    return str(Path(__file__).parent / path)
@@ -1,94 +0,0 @@
-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
@@ -1,25 +1,27 @@
--extra-index-url https://download.pytorch.org/whl/nightly/cu128
+--extra-index-url https://download.pytorch.org/whl/cu118

-numpy>=1.23.5,<2
-typing-extensions>=4.8.0
-opencv-python==4.11.0.86
-onnx==1.17.0
-cv2_enumerate_cameras==1.1.18.3
+numpy==1.23.5
+opencv-contrib-python==4.10.0.84
+onnx==1.16.0
 insightface==0.7.3
 psutil==5.9.8
 tk==0.1.0
 customtkinter==5.2.2
-pillow==11.1.0
-torch; sys_platform != 'darwin'
-torch; sys_platform == 'darwin'
-torchvision; sys_platform != 'darwin'
-torchvision; sys_platform == 'darwin'
-onnxruntime-silicon==1.21; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.21; sys_platform != 'darwin'
-tensorflow; sys_platform != 'darwin'
+pillow==9.5.0
+torch==2.0.1+cu118; sys_platform != 'darwin'
+torch==2.0.1; sys_platform == 'darwin'
+torchvision==0.15.2+cu118; sys_platform != 'darwin'
+torchvision==0.15.2; sys_platform == 'darwin'
+onnxruntime==1.18.0; sys_platform == 'darwin' and platform_machine != 'arm64'
+onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
+onnxruntime-gpu==1.18.0; sys_platform != 'darwin'
+tensorflow==2.13.0rc1; sys_platform == 'darwin'
+tensorflow==2.12.1; sys_platform != 'darwin'
 opennsfw2==0.10.2
 protobuf==4.23.2
 tqdm==4.66.4
 gfpgan==1.3.8
-tkinterdnd2==0.4.2
+pyobjc==9.1; sys_platform == 'darwin'
 pygrabber==0.2
+pyvirtualcam==0.12.0
+pyobjc-framework-AVFoundation==10.3.1; sys_platform == 'darwin'
@@ -1 +1 @@
-python run.py --execution-provider cuda
+python run.py --execution-provider cuda --execution-threads 60 --max-memory 60
@@ -1 +0,0 @@
-python run.py --execution-provider dml
@@ -0,0 +1 @@
+python run.py --execution-provider dml
@@ -0,0 +1,13 @@
+@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
@@ -0,0 +1,125 @@
+@echo off
+setlocal EnableDelayedExpansion
+
+:: 1. Setup your platform
+echo Setting up your platform...
+call :check_installation python "Python 3.10 or later"
+call :check_installation pip "Pip"
+call :install_if_missing git "Git" "winget install --id Git.Git -e --source winget"
+call :install_if_missing ffmpeg "FFMPEG" "winget install --id Gyan.FFmpeg -e --source winget"
+
+:: Visual Studio 2022 Runtimes
+echo Installing Visual Studio 2022 Runtimes...
+winget install --id Microsoft.VC++2015-2022Redist-x64 -e --source winget
+
+:: 2. Clone Repository
+call :clone_repository "https://github.com/iVideoGameBoss/iRoopDeepFaceCam.git" "iRoopDeepFaceCam"
+
+:: 3. Download Models
+echo Downloading models...
+if not exist models mkdir models
+curl -L -o models\GFPGANv1.4.pth https://huggingface.co/ivideogameboss/iroopdeepfacecam/resolve/main/GFPGANv1.4.pth
+curl -L -o models\inswapper_128_fp16.onnx https://huggingface.co/ivideogameboss/iroopdeepfacecam/resolve/main/inswapper_128_fp16.onnx
+
+:: 4. Install dependencies
+echo Creating a virtual environment...
+python -m venv venv
+call venv\Scripts\activate.bat
+
+echo Installing required Python packages...
+pip install --upgrade pip
+pip install -r requirements.txt
+echo Setup complete. You can now run the application.
+
+:menu
+:: GPU Acceleration Options
+echo.
+echo Choose the GPU Acceleration Option if applicable:
+echo 1. CUDA (Nvidia)
+echo 2. CoreML (Apple Silicon)
+echo 3. CoreML (Apple Legacy)
+echo 4. DirectML (Windows)
+echo 5. OpenVINO (Intel)
+echo 6. None
+set /p choice="Enter your choice (1-6): "
+
+set "exec_provider="
+call :set_execution_provider %choice%
+
+:end_choice
+echo.
+echo GPU Acceleration setup complete.
+echo Selected provider: !exec_provider!
+echo.
+
+:: Run the application
+if defined exec_provider (
+    echo Running the application with !exec_provider! execution provider...
+    python run.py --execution-provider !exec_provider!
+) else (
+    echo Running the application...
+    python run.py
+)
+
+:: Deactivate the virtual environment
+call venv\Scripts\deactivate.bat
+
+echo.
+echo Script execution completed.
+pause
+exit /b
+
+:check_installation
+where %1 >nul 2>&1
+if %ERRORLEVEL% neq 0 (
+    echo %2 is not installed. Please install %2.
+    pause
+    exit /b
+)
+
+:install_if_missing
+where %1 >nul 2>&1
+if %ERRORLEVEL% neq 0 (
+    echo %2 is not installed. Installing %2...
+    %3
+)
+
+:clone_repository
+if exist %2 (
+    echo %2 directory already exists.
+    set /p overwrite="Do you want to overwrite? (Y/N): "
+    if /i "%overwrite%"=="Y" (
+        rmdir /s /q %2
+        git clone %1
+    ) else (
+        echo Skipping clone, using existing directory.
+    )
+) else (
+    git clone %1
+)
+
+:set_execution_provider
+if "%1"=="1" (
+    call :install_onnxruntime "onnxruntime-gpu" "1.16.3" "cuda"
+) else if "%1"=="2" (
+    call :install_onnxruntime "onnxruntime-silicon" "1.13.1" "coreml"
+) else if "%1"=="3" (
+    call :install_onnxruntime "onnxruntime-coreml" "1.13.1" "coreml"
+) else if "%1"=="4" (
+    call :install_onnxruntime "onnxruntime-directml" "1.15.1" "directml"
+) else if "%1"=="5" (
+    call :install_onnxruntime "onnxruntime-openvino" "1.15.0" "openvino"
+) else if "%1"=="6" (
+    echo Skipping GPU acceleration setup.
+    set "exec_provider=none"
+) else (
+    echo Invalid choice. Please try again.
+    goto menu
+)
+
+:install_onnxruntime
+echo Installing %1 dependencies...
+pip uninstall -y onnxruntime %1
+pip install %1==%2
+set "exec_provider=%3"
+goto end_choice
Author	SHA1	Message	Date
Kenneth Estanislao	61dae91439	Revert "Merge pull request #566 from pereiraroland26/main" This reverts commit `5d450b4352`.	2024-10-04 15:57:48 +08:00
Kenneth Estanislao	5d450b4352	Merge pull request #566 from pereiraroland26/main Added support for multiple faces	2024-10-04 15:55:31 +08:00
Kenneth Estanislao	a11ccf9c49	Merge pull request #621 from saleweaver/experimental Significantly improve video resolution/quality using ESPCN_x4 model	2024-09-23 11:59:23 +08:00
Michael	59cc742197	Add option to change the scale factor. Add readme information about super resolution.	2024-09-23 02:49:48 +01:00
Michael	2066560a95	Significantly improve video resolution/quality using ESPCN_x4 model	2024-09-23 02:48:59 +01:00
Michael	1af9abda2f	Significantly improve video resolution/quality using ESPCN_x4 model	2024-09-23 02:25:14 +01:00
Kenneth Estanislao	91884eebf7	Merge pull request #615 from saleweaver/experimental Adding headless parameter to arguments to run from the cli, reenabling macOS compatibility	2024-09-23 04:01:23 +08:00
Michael	4686716c59	add to README.md	2024-09-22 18:39:00 +01:00
Michael	f4028d3949	Fix underscore/hyphen	2024-09-22 18:33:02 +01:00
Michael	07c735e9d2	Allows to set the upscale factor for gfpgan face_enhancer	2024-09-22 18:31:06 +01:00
Michael	aa021b6aa0	better import condition	2024-09-22 18:11:02 +01:00
Michael	0e3805e200	added headless argument to readme	2024-09-22 17:57:46 +01:00
Michael	5cabbffda8	- removed unused import statements - added macOS specific required library to requirements.txt - conditional import of pygrabber, which is unavailable for macOS	2024-09-22 17:55:26 +01:00
Michael	0d4676591e	- removed unused import statements - added macOS specific required library to requirements.txt - conditional import of pygrabber, which is unavailable for macOS	2024-09-22 17:54:44 +01:00
Michael	c2cc885672	Adding headless parameter to arguments to run from the cli	2024-09-21 22:41:47 +01:00
Kenneth Estanislao	e36c746c81	Update setup_deep_live_cam.bat	2024-09-08 20:31:36 +08:00
barongello	14ab470dcc	Adding a swap faces button to easily swap source/target images	2024-08-27 12:44:47 +08:00
Kenneth Estanislao	4dc4746235	update inswapper	2024-08-21 14:40:15 +08:00
Kenneth Estanislao	ac8feff652	Merge pull request #329 from bit-wrangler/experimental Added virtual camera output and fetching of input camera devices with names using pygrabber on windows and linux	2024-08-16 00:58:50 +08:00
Aleksandr Spiridonov	a90c4facc5	added a note to README to document new virtual cam output feature	2024-08-15 12:27:52 -04:00
Aleksandr Spiridonov	575373beac	fixed variable names not matching after merge conflict resolution from upstream	2024-08-15 12:22:19 -04:00
Aleksandr Spiridonov	b8cdad5cce	Merge remote-tracking branch 'parent/experimental' into experimental	2024-08-15 12:15:53 -04:00
Kenneth Estanislao	137ac597ef	Merge pull request #293 from vic4key/experimental To fix bugs and support more options for the Live function (see details in Commits tab)	2024-08-15 13:44:53 +08:00
Aleksandr Spiridonov	f976885456	updated rely coords for the taller window	2024-08-15 01:36:24 -04:00
Aleksandr Spiridonov	cd2c3c2103	added virtual cam output	2024-08-15 01:31:10 -04:00
Aleksandr Spiridonov	3fbc1d0433	added virtual cam output toggle	2024-08-15 01:04:57 -04:00
Aleksandr Spiridonov	b4cf8854f8	refactored camera preview to use a loop function	2024-08-15 00:50:14 -04:00
Aleksandr Spiridonov	eb733ad8c5	started using pygrabber to get input cameras with names; fixed issue with webcam preview not stopping when the preview window is closed	2024-08-15 00:42:53 -04:00
Vic P	c6c41b8d0d	Support the following options: - The live camera display as you see it in the front-facing camera frame (like iPhone's Mirror Front Camera). - The live camera frame is resizable. Note: These options are turned off by default. Enabling both options may reduce performance by ~2%. Signed-off-by: Vic P <vic4key@gmail.com>	2024-08-15 02:25:29 +07:00
Vic P	55c8d8181c	Fix an issue that the Live function where the camera was not released when the user closed the live window. Signed-off-by: Vic P <vic4key@gmail.com>	2024-08-14 00:48:01 +07:00
Kenneth Estanislao	4ddcd60c49	Merge pull request #237 from vic4key/experimental Fix & Improve the NSFW function	2024-08-13 12:10:14 +08:00
Vic P	408b0f4cf7	## Fix & Improve the NSFW function - Fixed incorrect state usage. - Removed the redundant argument that caused exceptions. - Prevented the app from closing when an image is flagged as NSFW.	2024-08-13 04:16:34 +07:00
Kenneth Estanislao	78c808ef36	Merge pull request #166 from zoharbabin/experimental Refactor and Optimize Cross-Platform Support	2024-08-12 12:27:35 +08:00
Zohar Babin	6b0cc74957	Refactor and Optimize Cross-Platform Support, Error Handling, and UI Enhancements	2024-08-10 22:41:45 -04:00
Dmitry Samoylenko	8d3072d906	Enable to choose a camera device in UI Signed-off-by: samoylenkodmitry <samoylenkodmitry@gmail.com>	2024-08-10 14:08:29 +08:00