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
38 changed files with 1093 additions and 3016 deletions
@@ -1,38 +0,0 @@
---
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
---
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Desktop (please complete the following information):**
- - OS: [e.g. iOS]
- - Browser [e.g. chrome, safari]
- - Version [e.g. 22]
-
-**Smartphone (please complete the following information):**
- - Device: [e.g. iPhone6]
- - OS: [e.g. iOS8.1]
- - Browser [e.g. stock browser, safari]
- - Version [e.g. 22]
-
-**Additional context**
-Add any other context about the problem here.
@@ -6,21 +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/
@@ -0,0 +1 @@
+3.10.14
@@ -1 +0,0 @@
-Please always push on the experimental to ensure we don't mess with the main branch. All the test will be done on the experimental and will be pushed to the main branch after few days of testing.
@@ -1,213 +1,163 @@
-<h1 align="center">Deep-Live-Cam</h1>
+![demo-gif](demo.gif)

-<p align="center">
-  Real-time face swap and video deepfake with a single click and only a single image.
-</p>
-
-<p align="center">
-  <img src="media/demo.gif" alt="Demo GIF">
-  <img src="media/avgpcperformancedemo.gif" alt="Performance Demo GIF">
-</p>

 ## Disclaimer
+This software is meant to be a productive contribution to the rapidly growing AI-generated media industry. It will help artists with tasks such as animating a custom character or using the character as a model for clothing etc.

-This software is intended as a productive contribution to the AI-generated media industry. It aims to assist artists with tasks like animating custom characters or using them as models for clothing, etc.
+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.

-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 law and ethics. We may shut down the project or add watermarks if legally required.
+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.

-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.
+## How do I install it?


-## Quick Start (Windows / Nvidia)
+### 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) 
+-   [visual studio 2022 runtimes (windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
+#### 2. Clone Repository
+    https://github.com/hacksider/Deep-Live-Cam.git

-[![Download](media/download.png)](https://hacksider.gumroad.com/l/vccdmm) 
+#### 3. Download Models

-[Download latest pre-built version with CUDA support](https://hacksider.gumroad.com/l/vccdmm) - No Manual Installation/Downloading required.
+ 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)

-## Installation (Manual)
-**Please be aware that the installation needs technical skills and is NOT for beginners, consider downloading the prebuilt. Please do NOT open platform and installation related issues on GitHub before discussing it on the discord server.**
-### Basic Installation (CPU)
+Then put those 2 files on the "**models**" folder

-This is more likely to work on your computer but will be slower as it utilizes the CPU.
-
-**1. Setup Your Platform**
-
- Python (3.10 recommended)
- pip
- git
- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) 
- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
-
-**2. Clone Repository**
-
-```bash
-https://github.com/hacksider/Deep-Live-Cam.git
+#### 4. Install dependency
+We highly recommend to work with a  `venv`  to avoid issues.
 ```
-
-**3. Download 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.onnx) (Note: Use this [replacement version](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) if you encounter issues)
-
-Place these files in the "**models**" folder.
-
-**4. Install Dependencies**
-
-We highly recommend using a `venv` to avoid issues.
-
-```bash
 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:** Install or upgrade the `python-tk` package:
+### *Proceed if you want to use GPU Acceleration
+### CUDA Execution Provider (Nvidia)*
+
+1.  Install  [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive)
+    
+2.  Install dependencies:
+    

-```bash
-brew install python-tk@3.10
 ```
-
-**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 (Optional)
-
-<details>
-<summary>Click to see the details</summary>
-
-**CUDA Execution Provider (Nvidia)**
-
-1. Install [CUDA Toolkit 11.8](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 in case the provider is available:
+
 ```
-3. Usage:
-```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)

-1. Install dependencies:
-```bash
+1.  Install dependencies:
+
+```
 pip uninstall onnxruntime onnxruntime-silicon
 pip install onnxruntime-silicon==1.13.1
+
+```
+
+2.  Usage in case the provider is available:
+
 ```
-2. Usage:
-```bash
 python run.py --execution-provider coreml
+
 ```

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

-1. Install dependencies:
-```bash
+1.  Install dependencies:
+
+```
 pip uninstall onnxruntime onnxruntime-coreml
 pip install onnxruntime-coreml==1.13.1
+
+```
+
+2.  Usage in case the provider is available:
+
 ```
-2. Usage:
-```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:
-```bash
+1.  Install dependencies:
+
+```
 pip uninstall onnxruntime onnxruntime-directml
 pip install onnxruntime-directml==1.15.1
+
+```
+
+2.  Usage in case the provider is available:
+
 ```
-2. Usage:
-```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:
-```bash
+1.  Install dependencies:
+
+```
 pip uninstall onnxruntime onnxruntime-openvino
 pip install onnxruntime-openvino==1.15.0
+
+```
+
+2.  Usage in case the provider is available:
+
 ```
-2. Usage:
-```bash
 python run.py --execution-provider openvino
 ```

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

+Executing `python run.py` command will launch this window:
+![gui-demo](instruction.png)

-## Usage
+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.

-**1. Image/Video 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`. 
- 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.
+![demo-gif](demo.gif)

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

- 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.
+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.

-![demo-gif](media/demo.gif)
+Additional command line arguments are given below. To learn out what they do, check [this guide](https://github.com/s0md3v/roop/wiki/Advanced-Options).

-## Features
-
-### Resizable Preview Window
-
-Dynamically improve performance using the `--live-resizable` parameter.
-
-![resizable-gif](media/resizable.gif)
-
-### Face Mapping
-
-Track and change faces on the fly.
-
-![face_mapping_source](media/face_mapping_source.gif)
-
-**Source Video:**
-
-![face-mapping](media/face_mapping.png)
-
-**Enable Face Mapping:**
-
-![face-mapping2](media/face_mapping2.png)
-
-**Map the Faces:**
-
-![face_mapping_result](media/face_mapping_result.gif)
-
-**See the Magic!**
-
-![movie](media/movie.gif)
-
-**Watch movies in realtime:**
-
-It's as simple as opening a movie on the screen, and selecting OBS as your camera!
-![image](media/movie_img.png)
-
-
-## Command Line Arguments

 ```
 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
-  --nsfw-filter                                            filter the NSFW image or video
  --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
@@ -215,198 +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.

+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.

-## Webcam Mode on WSL2 Ubuntu (Optional)
-
-<details>
-<summary>Click to see the details</summary>
-
-If you want to use WSL2 on Windows 11 you will notice, that Ubuntu WSL2 doesn't come with USB-Webcam support in the Kernel. You need to do two things: Compile the Kernel with the right modules integrated and forward your USB Webcam from Windows to Ubuntu with the usbipd app. Here are detailed Steps:
-
-This tutorial will guide you through the process of setting up WSL2 Ubuntu with USB webcam support, rebuilding the kernel, and preparing the environment for the Deep-Live-Cam project.  
-  
-**1. Install WSL2 Ubuntu**  
-  
-Install WSL2 Ubuntu from the Microsoft Store or using PowerShell:  
-  
-**2. Enable USB Support in WSL2**  
-  
-1. Install the USB/IP tool for Windows:  
-[https://learn.microsoft.com/en-us/windows/wsl/connect-usb](https://learn.microsoft.com/en-us/windows/wsl/connect-usb)  
-  
-2. In Windows PowerShell (as Administrator), connect your webcam to WSL:  
-  
-```powershell  
-usbipd list  
-usbipd bind --busid x-x # Replace x-x with your webcam's bus ID  
-usbipd attach --wsl --busid x-x # Replace x-x with your webcam's bus ID  
-```  
- You need to redo the above every time you reboot wsl or re-connect your webcam/usb device.
- 
-**3. Rebuild WSL2 Ubuntu Kernel with USB and Webcam Modules**  
-  
-Follow these steps to rebuild the kernel:  
-  
-1. Start with this guide: [https://github.com/PINTO0309/wsl2_linux_kernel_usbcam_enable_conf](https://github.com/PINTO0309/wsl2_linux_kernel_usbcam_enable_conf)  
-  
-2. When you reach the `sudo wget [github.com](http://github.com/)...PINTO0309` step, which won't work for newer kernel versions, follow this video instead or alternatively follow the video tutorial from the beginning: 
-[https://www.youtube.com/watch?v=t_YnACEPmrM](https://www.youtube.com/watch?v=t_YnACEPmrM)  
-  
-Additional info: [https://askubuntu.com/questions/1413377/camera-not-working-in-cheese-in-wsl2](https://askubuntu.com/questions/1413377/camera-not-working-in-cheese-in-wsl2)  
-  
-3. After rebuilding, restart WSL with the new kernel. 
-  
-**4. Set Up Deep-Live-Cam Project**  
- Within Ubuntu:
-1. Clone the repository:  
-  
-```bash  
-git clone [https://github.com/hacksider/Deep-Live-Cam](https://github.com/hacksider/Deep-Live-Cam)  
-```  
-  
-2. Follow the installation instructions in the repository, including cuda toolkit 11.8, make 100% sure it's not cuda toolkit 12.x.  
-   
-**5. Verify and Load Kernel Modules**  
-  
-1. Check if USB and webcam modules are built into the kernel:  
-  
-```bash  
-zcat /proc/config.gz | grep -i "CONFIG_USB_VIDEO_CLASS"  
-```  
-  
-2. If modules are loadable (m), not built-in (y), check if the file exists:  
-  
-```bash  
-ls /lib/modules/$(uname -r)/kernel/drivers/media/usb/uvc/  
-```  
-  
-3. Load the module and check for errors (optional if built-in):  
-  
-```bash  
-sudo modprobe uvcvideo  
-dmesg | tail  
-```  
-  
-4. Verify video devices:  
-  
-```bash  
-sudo ls -al /dev/video*  
-```  
-  
-**6. Set Up Permissions**  
-  
-1. Add user to video group and set permissions:  
-  
-```bash  
-sudo usermod -a -G video $USER  
-sudo chgrp video /dev/video0 /dev/video1  
-sudo chmod 660 /dev/video0 /dev/video1  
-```  
-  
-2. Create a udev rule for permanent permissions:  
-  
-```bash  
-sudo nano /etc/udev/rules.d/81-webcam.rules  
-```  
-  
-Add this content:  
-  
-```  
-KERNEL=="video[0-9]*", GROUP="video", MODE="0660"  
-```  
-  
-3. Reload udev rules:  
-  
-```bash  
-sudo udevadm control --reload-rules && sudo udevadm trigger  
-```  
-  
-4. Log out and log back into your WSL session.  
-  
-5. Start Deep-Live-Cam with `python run.py --execution-provider cuda --max-memory 8` where 8 can be changed to the number of GB VRAM of your GPU has, minus 1-2GB. If you have a RTX3080 with 10GB I suggest adding 8GB. Leave some left for Windows.
-
-**Final Notes**  
-  
- Steps 6 and 7 may be optional if the modules are built into the kernel and permissions are already set correctly.  
- Always ensure you're using compatible versions of CUDA, ONNX, and other dependencies.  
- If issues persist, consider checking the Deep-Live-Cam project's specific requirements and troubleshooting steps.  
-  
-By following these steps, you should have a WSL2 Ubuntu environment with USB webcam support ready for the Deep-Live-Cam project. If you encounter any issues, refer back to the specific error messages and troubleshooting steps provided.
-
-**Troubleshooting CUDA Issues** 
-
-If you encounter this error:  
-  
-```  
-[ONNXRuntimeError] : 1 : FAIL : Failed to load library [libonnxruntime_providers_cuda.so](http://libonnxruntime_providers_cuda.so/) with error: libcufft.so.10: cannot open shared object file: No such file or directory  
-```  
-  
-Follow these steps:  
-  
-1. Install CUDA Toolkit 11.8 (ONNX 1.16.3 requires CUDA 11.x, not 12.x):  
-[https://developer.nvidia.com/cuda-11-8-0-download-archive](https://developer.nvidia.com/cuda-11-8-0-download-archive)  
-  select: Linux, x86_64, WSL-Ubuntu, 2.0, deb (local)
-2. Check CUDA version:  
-  
-```bash  
-/usr/local/cuda/bin/nvcc --version  
-```  
-  
-3. If the wrong version is installed, remove it completely:  
-[https://askubuntu.com/questions/530043/removing-nvidia-cuda-toolkit-and-installing-new-one](https://askubuntu.com/questions/530043/removing-nvidia-cuda-toolkit-and-installing-new-one)  
-  
-4. Install CUDA Toolkit 11.8 again [https://developer.nvidia.com/cuda-11-8-0-download-archive](https://developer.nvidia.com/cuda-11-8-0-download-archive), select: Linux, x86_64, WSL-Ubuntu, 2.0, deb (local)
-  
-```bash  
-sudo apt-get -y install cuda-toolkit-11-8  
 ```
-</details>
-
-
-## Future Updates & Roadmap
-
-For the latest experimental builds and features, see the [experimental branch](https://github.com/hacksider/Deep-Live-Cam/tree/experimental).
-
-**TODO:**
-
- [ ] Develop a version for web app/service
- [ ] Speed up model loading
- [ ] Speed up real-time face swapping
- [x] Support multiple faces 
- [x] UI/UX enhancements for desktop app
-
-This is an open-source project developed in our free time. Updates may be delayed.
-
-**Tips and Links:**
- [How to make the most of Deep-Live-Cam](https://hacksider.gumroad.com/p/how-to-make-the-most-on-deep-live-cam)
- Face enhancer is good, but still very slow for any live streaming purpose.
-

 ## Credits
-
+- [henryruhs](https://github.com/henryruhs): for being an irreplaceable contributor to the project
 - [ffmpeg](https://ffmpeg.org/): for making video related operations easy
- [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. 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).
+- [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) : for open version of roop
- [pereiraroland26](https://github.com/pereiraroland26) : Multiple faces support
- [vic4key](https://github.com/vic4key) : For supporting/contributing on this project
- [KRSHH](https://github.com/KRSHH) : For updating the UI
- and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
- Foot Note: [This is originally roop-cam, see the full history of the code here.](https://github.com/hacksider/roop-cam) Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
-
-## Contributions
-![Alt](https://repobeats.axiom.co/api/embed/fec8e29c45dfdb9c5916f3a7830e1249308d20e1.svg "Repobeats analytics image")
-## Star History
-
-<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>
+- [GosuDRM](https://github.com/GosuDRM/nsfw-roop) : for uncensoring roop
+- and all developers behind libraries used in this project.
@@ -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
@@ -1,16 +1,17 @@
 import os
 import sys
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-# reduce tensorflow log level
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 import warnings
-from typing import List
 import platform
 import signal
 import shutil
 import argparse
+from typing import List
+
+# Set environment variables for CUDA performance and TensorFlow logging
+if any(arg.startswith('--execution-provider') for arg in sys.argv):
+    os.environ['OMP_NUM_THREADS'] = '1'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
 import torch
 import onnxruntime
 import tensorflow
@@ -19,38 +20,73 @@ import modules.globals
 import modules.metadata
 import modules.ui as ui
 from modules.processors.frame.core import get_frame_processors_modules
-from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
-
-if 'ROCMExecutionProvider' in modules.globals.execution_providers:
-    del torch
+from modules.utilities import (
+    has_image_extension,
+    is_image,
+    is_video,
+    detect_fps,
+    create_video,
+    extract_frames,
+    get_temp_frame_paths,
+    restore_audio,
+    create_temp,
+    move_temp,
+    clean_temp,
+    normalize_output_path
+)

+# Filter warnings
 warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
 warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')

+# Cross-platform resource management
+if platform.system() == 'Darwin' and 'ROCMExecutionProvider' in modules.globals.execution_providers:
+    del torch
+

 def parse_args() -> None:
    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
    program = argparse.ArgumentParser()
-    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
-    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+')
-    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
-    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
-    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
-    program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
-    program.add_argument('--map-faces', help='map source target faces', dest='map_faces', action='store_true', default=False)
-    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
-    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
-    program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
-    program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
-    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
-    program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
-    program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')
+    program.add_argument('-s', '--source', help='Select a source image', dest='source_path')
+    program.add_argument('-t', '--target', help='Select a target image or video', dest='target_path')
+    program.add_argument('-o', '--output', help='Select output file or directory', dest='output_path')
+    program.add_argument('--frame-processor', help='Pipeline of frame processors', dest='frame_processor',
+                         default=['face_swapper'], choices=['face_swapper', 'face_enhancer', 'super_resolution'],
+                         nargs='+')
+    program.add_argument('--keep-fps', help='Keep original fps', dest='keep_fps', action='store_true', default=False)
+    program.add_argument('--keep-audio', help='Keep original audio', dest='keep_audio', action='store_true',
+                         default=True)
+    program.add_argument('--keep-frames', help='Keep temporary frames', dest='keep_frames', action='store_true',
+                         default=False)
+    program.add_argument('--many-faces', help='Process every face', dest='many_faces', action='store_true',
+                         default=False)
+    program.add_argument('--video-encoder', help='Adjust output video encoder', dest='video_encoder', default='libx264',
+                         choices=['libx264', 'libx265', 'libvpx-vp9'])
+    program.add_argument('--video-quality', help='Adjust output video quality', dest='video_quality', type=int,
+                         default=18,
+                         choices=range(52), metavar='[0-51]')
+    program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame',
+                         dest='live_mirror', action='store_true', default=False)
+    program.add_argument('--live-resizable', help='The live camera frame is resizable',
+                         dest='live_resizable', action='store_true', default=False)
+    program.add_argument('--max-memory', help='Maximum amount of RAM in GB', dest='max_memory', type=int,
+                         default=suggest_max_memory())
+    program.add_argument('--execution-provider', help='Execution provider', dest='execution_provider', default=['cpu'],
+                         choices=suggest_execution_providers(), nargs='+')
+    program.add_argument('--execution-threads', help='Number of execution threads', dest='execution_threads', type=int,
+                         default=suggest_execution_threads())
+    program.add_argument('--headless', help='Run in headless mode', dest='headless', default=False, action='store_true')
+    program.add_argument('--enhancer-upscale-factor',
+                         help='Sets the upscale factor for the enhancer. Only applies if `face_enhancer` is set as a frame-processor',
+                         dest='enhancer_upscale_factor', type=int, default=1)
+    program.add_argument('--source-image-scaling-factor', help='Set the upscale factor for source images',
+                         dest='source_image_scaling_factor', default=2, type=int)
+    program.add_argument('-r', '--super-resolution-scale-factor', dest='super_resolution_scale_factor',
+                         help='Set the upscale factor for super resolution', default=4, choices=[2, 3, 4], type=int)
+    program.add_argument('-v', '--version', action='version',
+                         version=f'{modules.metadata.name} {modules.metadata.version}')

-    # register deprecated args
+    # Register deprecated args
    program.add_argument('-f', '--face', help=argparse.SUPPRESS, dest='source_path_deprecated')
    program.add_argument('--cpu-cores', help=argparse.SUPPRESS, dest='cpu_cores_deprecated', type=int)
    program.add_argument('--gpu-vendor', help=argparse.SUPPRESS, dest='gpu_vendor_deprecated')
@@ -60,15 +96,14 @@ def parse_args() -> None:

    modules.globals.source_path = args.source_path
    modules.globals.target_path = args.target_path
-    modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path)
+    modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path,
+                                                        args.output_path)
    modules.globals.frame_processors = args.frame_processor
    modules.globals.headless = args.source_path or args.target_path or args.output_path
    modules.globals.keep_fps = args.keep_fps
    modules.globals.keep_audio = args.keep_audio
    modules.globals.keep_frames = args.keep_frames
    modules.globals.many_faces = args.many_faces
-    modules.globals.nsfw_filter = args.nsfw_filter
-    modules.globals.map_faces = args.map_faces
    modules.globals.video_encoder = args.video_encoder
    modules.globals.video_quality = args.video_quality
    modules.globals.live_mirror = args.live_mirror
@@ -76,18 +111,26 @@ def parse_args() -> None:
    modules.globals.max_memory = args.max_memory
    modules.globals.execution_providers = decode_execution_providers(args.execution_provider)
    modules.globals.execution_threads = args.execution_threads
+    modules.globals.headless = args.headless
+    modules.globals.enhancer_upscale_factor = args.enhancer_upscale_factor
+    modules.globals.source_image_scaling_factor = args.source_image_scaling_factor
+    modules.globals.sr_scale_factor = args.super_resolution_scale_factor
+    # Handle face enhancer tumbler
+    modules.globals.fp_ui['face_enhancer'] = 'face_enhancer' in args.frame_processor

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

-    # translate deprecated args
+    # Handle deprecated arguments
+    handle_deprecated_args(args)
+
+
+def handle_deprecated_args(args) -> None:
+    """Handle deprecated arguments by translating them to the new format."""
    if args.source_path_deprecated:
        print('\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\033[0m')
        modules.globals.source_path = args.source_path_deprecated
-        modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path, args.output_path)
+        modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path,
+                                                            args.output_path)
    if args.cpu_cores_deprecated:
        print('\033[33mArgument --cpu-cores is deprecated. Use --execution-threads instead.\033[0m')
        modules.globals.execution_threads = args.cpu_cores_deprecated
@@ -98,7 +141,7 @@ def parse_args() -> None:
        print('\033[33mArgument --gpu-vendor nvidia is deprecated. Use --execution-provider cuda instead.\033[0m')
        modules.globals.execution_providers = decode_execution_providers(['cuda'])
    if args.gpu_vendor_deprecated == 'amd':
-        print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider cuda instead.\033[0m')
+        print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider rocm instead.\033[0m')
        modules.globals.execution_providers = decode_execution_providers(['rocm'])
    if args.gpu_threads_deprecated:
        print('\033[33mArgument --gpu-threads is deprecated. Use --execution-threads instead.\033[0m')
@@ -106,18 +149,22 @@ def parse_args() -> None:


 def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
+    return [provider.replace('ExecutionProvider', '').lower() for provider in execution_providers]


 def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
+    available_providers = onnxruntime.get_available_providers()
+    encoded_providers = encode_execution_providers(available_providers)
+
+    selected_providers = [available_providers[encoded_providers.index(req)] for req in execution_providers
+                          if req in encoded_providers]
+
+    # Default to CPU if no suitable providers are found
+    return selected_providers if selected_providers else ['CPUExecutionProvider']


 def suggest_max_memory() -> int:
-    if platform.system().lower() == 'darwin':
-        return 4
-    return 16
+    return 4 if platform.system().lower() == 'darwin' else 16


 def suggest_execution_providers() -> List[str]:
@@ -125,34 +172,43 @@ def suggest_execution_providers() -> List[str]:


 def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in modules.globals.execution_providers:
+    if 'dml' in modules.globals.execution_providers:
        return 1
-    if 'ROCMExecutionProvider' in modules.globals.execution_providers:
+    if 'rocm' in modules.globals.execution_providers:
        return 1
    return 8


 def limit_resources() -> None:
-    # prevent tensorflow memory leak
+    # Prevent TensorFlow memory leak
    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
    for gpu in gpus:
        tensorflow.config.experimental.set_memory_growth(gpu, True)
-    # limit memory usage
+
+    # Limit memory usage
    if modules.globals.max_memory:
        memory = modules.globals.max_memory * 1024 ** 3
        if platform.system().lower() == 'darwin':
-            memory = modules.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
+            memory = modules.globals.max_memory * 1024 ** 3
+        elif platform.system().lower() == 'windows':
            import ctypes
            kernel32 = ctypes.windll.kernel32
            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
        else:
            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
+            try:
+                soft, hard = resource.getrlimit(resource.RLIMIT_DATA)
+                if memory > hard:
+                    print(
+                        f"Warning: Requested memory limit {memory / (1024 ** 3)} GB exceeds system's hard limit. Setting to maximum allowed {hard / (1024 ** 3)} GB.")
+                    memory = hard
+                resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
+            except ValueError as e:
+                print(f"Warning: Could not set memory limit: {e}. Continuing with default limits.")


 def release_resources() -> None:
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+    if 'cuda' in modules.globals.execution_providers:
        torch.cuda.empty_cache()


@@ -163,52 +219,86 @@ def pre_check() -> bool:
    if not shutil.which('ffmpeg'):
        update_status('ffmpeg is not installed.')
        return False
+    if 'cuda' in modules.globals.execution_providers and not torch.cuda.is_available():
+        update_status('CUDA is not available. Please check your GPU or CUDA installation.')
+        return False
    return True


 def update_status(message: str, scope: str = 'DLC.CORE') -> None:
    print(f'[{scope}] {message}')
-    if not modules.globals.headless:
+    if not modules.globals.headless and ui.status_label:
        ui.update_status(message)

+
 def start() -> None:
    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
        if not frame_processor.pre_start():
            return
-    update_status('Processing...')
-    # process image to image
+
+    # Process image to image
    if has_image_extension(modules.globals.target_path):
-        if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
+        process_image_to_image()
+        return
+
+    # Process image to video
+    process_image_to_video()
+
+
+def process_image_to_image() -> None:
+    if modules.globals.nsfw:
+        from modules.predicter import predict_image
+        if predict_image(modules.globals.target_path):
+            destroy(to_quit=False)
+            update_status('Processing to image ignored!')
            return
-        try:
-            shutil.copy2(modules.globals.target_path, modules.globals.output_path)
-        except Exception as e:
-            print("Error copying file:", str(e))
-        for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
-            update_status('Progressing...', frame_processor.NAME)
-            frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
-            release_resources()
-        if is_image(modules.globals.target_path):
-            update_status('Processing to image succeed!')
-        else:
-            update_status('Processing to image failed!')
-        return
-    # process image to videos
-    if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
-        return

-    if not modules.globals.map_faces:
-        update_status('Creating temp resources...')
-        create_temp(modules.globals.target_path)
-        update_status('Extracting frames...')
-        extract_frames(modules.globals.target_path)
+    try:
+        shutil.copy2(modules.globals.target_path, modules.globals.output_path)
+    except Exception as e:
+        print("Error copying file:", str(e))

+    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
+        update_status('Processing...', frame_processor.NAME)
+        frame_processor.process_image(modules.globals.source_path, modules.globals.output_path,
+                                      modules.globals.output_path)
+        release_resources()
+
+    if is_image(modules.globals.target_path):
+        update_status('Processing to image succeeded!')
+    else:
+        update_status('Processing to image failed!')
+
+
+def process_image_to_video() -> None:
+    if modules.globals.nsfw:
+        from modules.predicter import predict_video
+        if predict_video(modules.globals.target_path):
+            destroy(to_quit=False)
+            update_status('Processing to video ignored!')
+            return
+
+    update_status('Creating temporary resources...')
+    create_temp(modules.globals.target_path)
+    update_status('Extracting frames...')
+    extract_frames(modules.globals.target_path)
    temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
-        update_status('Progressing...', frame_processor.NAME)
+        update_status('Processing...', frame_processor.NAME)
        frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
        release_resources()
-    # handles fps
+
+    handle_video_fps()
+    handle_video_audio()
+    clean_temp(modules.globals.target_path)
+
+    if is_video(modules.globals.target_path):
+        update_status('Processing to video succeeded!')
+    else:
+        update_status('Processing to video failed!')
+
+
+def handle_video_fps() -> None:
    if modules.globals.keep_fps:
        update_status('Detecting fps...')
        fps = detect_fps(modules.globals.target_path)
@@ -217,7 +307,9 @@ def start() -> None:
    else:
        update_status('Creating video with 30.0 fps...')
        create_video(modules.globals.target_path)
-    # handle audio
+
+
+def handle_video_audio() -> None:
    if modules.globals.keep_audio:
        if modules.globals.keep_fps:
            update_status('Restoring audio...')
@@ -226,12 +318,6 @@ def start() -> None:
        restore_audio(modules.globals.target_path, modules.globals.output_path)
    else:
        move_temp(modules.globals.target_path, modules.globals.output_path)
-    # clean and validate
-    clean_temp(modules.globals.target_path)
-    if is_video(modules.globals.target_path):
-        update_status('Processing to video succeed!')
-    else:
-        update_status('Processing to video failed!')


 def destroy(to_quit=True) -> None:
@@ -241,15 +327,20 @@ def destroy(to_quit=True) -> None:


 def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
-        if not frame_processor.pre_check():
+    try:
+        parse_args()
+        if not pre_check():
            return
-    limit_resources()
-    if modules.globals.headless:
-        start()
-    else:
-        window = ui.init(start, destroy)
-        window.mainloop()
+        for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
+            if not frame_processor.pre_check():
+                return
+        limit_resources()
+        if modules.globals.headless:
+            start()
+        else:
+            window = ui.init(start, destroy)
+            window.mainloop()
+    except Exception as e:
+        print(f"UI initialization failed: {str(e)}")
+        update_status(f"UI initialization failed: {str(e)}")
+        destroy()  # Ensure any resources are cleaned up on failure
@@ -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.souce_target_map:
-        if "source" in map and "target" in map:
-            return True
-    return False
-
-def default_source_face() -> Any:
-    for map in modules.globals.souce_target_map:
-        if "source" in map:
-            return map['source']['face']
-    return None
-
-def simplify_maps() -> Any:
-    centroids = []
-    faces = []
-    for map in modules.globals.souce_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.souce_target_map) > 0:
-            max_id = max(modules.globals.souce_target_map, key=lambda x: x['id'])['id']
-
-        modules.globals.souce_target_map.append({
-                'id' : max_id + 1
-                })
-    except ValueError:
-        return None
-    
-def get_unique_faces_from_target_image() -> Any:
-    try:
-        modules.globals.souce_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.souce_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.souce_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.souce_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.souce_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.souce_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
@@ -1,46 +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'))
 ]

-souce_target_map = []
-simple_map = {}
-
 source_path = None
 target_path = None
 output_path = None
 frame_processors: List[str] = []
-keep_fps = True  # Initialize with default value
-keep_audio = True  # Initialize with default value
-keep_frames = False  # Initialize with default value
-many_faces = False  # Initialize with default value
-map_faces = False  # Initialize with default value
-color_correction = False  # Initialize with default value
-nsfw_filter = False  # Initialize with default value
+keep_fps = None
+keep_audio = None
+keep_frames = None
+many_faces = None
 video_encoder = None
 video_quality = None
-live_mirror = False  # Initialize with default value
-live_resizable = False  # Initialize with default value
+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}  # Initialize with default value
+log_level = 'error'
+fp_ui: Dict[str, bool] = {}
+nsfw = None
 camera_input_combobox = None
 webcam_preview_running = False
-show_fps = False  # Initialize with default value
-mouth_mask = False
-show_mouth_mask_box = False
-mask_down_size = 0.5
-mask_size = 1.0
-mask_feather_ratio = 8
-opacity_switch = False
-face_opacity = 100
-selected_camera = None
+enhancer_upscale_factor = 1
+source_image_scaling_factor = 2
+sr_scale_factor = 4
@@ -1,3 +1,3 @@
 name = 'Deep Live Cam'
-version = '1.6.0'
+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)
@@ -8,7 +8,7 @@ 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
+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
@@ -16,34 +16,29 @@ THREAD_SEMAPHORE = threading.Semaphore()
 THREAD_LOCK = threading.Lock()
 NAME = 'DLC.FACE-ENHANCER'

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

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

-
 def get_face_enhancer() -> Any:
    global FACE_ENHANCER

    with THREAD_LOCK:
        if FACE_ENHANCER is None:
-            if os.name == 'nt':
-                model_path = resolve_relative_path('..\models\GFPGANv1.4.pth')
-                # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399
-            else:
-                model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
-            FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
+            model_path = 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(
@@ -52,14 +47,12 @@ def enhance_face(temp_frame: Frame) -> Frame:
        )
    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:
    for temp_frame_path in temp_frame_paths:
        temp_frame = cv2.imread(temp_frame_path)
@@ -68,12 +61,10 @@ def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any
        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)
@@ -2,101 +2,68 @@ from typing import Any, List
 import cv2
 import insightface
 import threading
-import numpy as np
+import os
+
 import modules.globals
 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,
-    resolve_relative_path,
-    is_image,
-    is_video,
-)
-from modules.cluster_analysis import find_closest_centroid
+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"
-
+NAME = 'DLC.FACE-SWAPPER'

 def pre_check() -> bool:
-    download_directory_path = resolve_relative_path("../models")
-    conditional_download(
-        download_directory_path,
-        [
-            "https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
-        ],
-    )
+    download_directory_path = 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 = resolve_relative_path("../models/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:
-    swapped_frame = get_face_swapper().get(
-        temp_frame, target_face, source_face, paste_back=True
-    )
-
-    # Apply opacity if enabled
-    if modules.globals.opacity_switch:
-        opacity = modules.globals.face_opacity / 100
-        swapped_frame = cv2.addWeighted(
-            swapped_frame, opacity, temp_frame, 1 - opacity, 0
-        )
-
-    # Apply mouth mask if enabled
-    if modules.globals.mouth_mask:
-        face_mask = create_face_mask(target_face, temp_frame)
-        mouth_mask_data = create_lower_mouth_mask(target_face, temp_frame)
-        mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = mouth_mask_data
-
-        if mouth_box is not None:
-            swapped_frame = apply_mouth_area(
-                swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
-            )
-
-            if modules.globals.show_mouth_mask_box:
-                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:
-    # Ensure the frame is in RGB format if color correction is enabled
-    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:
@@ -108,386 +75,30 @@ def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
            temp_frame = swap_face(source_face, target_face, temp_frame)
    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.souce_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.souce_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.souce_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.souce_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_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:
-        landmarks = landmarks.astype(np.int32)
-
-        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]
-
-        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)
-
-        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
-
-        face_outline = np.vstack(
-            [
-                [forehead_left],
-                right_side_face,
-                left_side_face[::-1],
-                [forehead_right],
-            ]
-        )
-
-        padding = int(np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05)
-
-        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)
-        cv2.fillConvexPoly(mask, hull_padded, 255)
-        mask = cv2.GaussianBlur(mask, (5, 5), 3)
-
-    return mask
-
-
-def create_lower_mouth_mask(face: Face, frame: Frame) -> tuple:
-    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
-    mouth_cutout = None
-    landmarks = face.landmark_2d_106
-    if landmarks is not None:
-        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)
-
-        center = np.mean(lower_lip_landmarks, axis=0)
-        expansion_factor = 1 + modules.globals.mask_down_size
-        expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
-
-        toplip_indices = [20, 0, 1, 2, 3, 4, 5]
-        toplip_extension = modules.globals.mask_size * 0.5
-        for idx in toplip_indices:
-            direction = expanded_landmarks[idx] - center
-            direction = direction / np.linalg.norm(direction)
-            expanded_landmarks[idx] += direction * toplip_extension
-
-        chin_indices = [11, 12, 13, 14, 15, 16]
-        chin_extension = 2 * 0.2
-        for idx in chin_indices:
-            expanded_landmarks[idx][1] += (
-                expanded_landmarks[idx][1] - center[1]
-            ) * chin_extension
-
-        expanded_landmarks = expanded_landmarks.astype(np.int32)
-
-        min_x, min_y = np.min(expanded_landmarks, axis=0)
-        max_x, max_y = np.max(expanded_landmarks, axis=0)
-
-        padding = int((max_x - min_x) * 0.1)
-        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)
-
-        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
-
-        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)
-        mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
-        mask[min_y:max_y, min_x:max_x] = mask_roi
-        mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
-
-        return mask, mouth_cutout, (min_x, min_y, max_x, max_y), expanded_landmarks
-
-    return mask, mouth_cutout, None, None
-
-
-def apply_mouth_area(
-    frame: Frame,
-    mouth_cutout: np.ndarray,
-    mouth_box: tuple,
-    face_mask: np.ndarray,
-    mouth_polygon: np.ndarray,
-) -> Frame:
-    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)
-
-        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)
-
-        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)
-
-        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 apply_color_transfer(source: np.ndarray, target: np.ndarray) -> np.ndarray:
-    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)
-
-    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)
-
-    source = (source - source_mean) * (target_std / source_std) + target_mean
-
-    return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
-
-
-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()
-
-        # Draw the lower lip polygon
-        cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
-
-        # Add labels
-        cv2.putText(
-            vis_frame,
-            "Mouth Mask",
-            (min_x, min_y - 10),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.5,
-            (255, 255, 255),
-            1,
-        )
-
-        return vis_frame
-    return frame
+    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"]
  }
 }
@@ -5,7 +5,7 @@ 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
@@ -15,127 +15,123 @@ import modules.globals
 TEMP_FILE = 'temp.mp4'
 TEMP_DIRECTORY = 'temp'

-# monkey patch ssl for mac
+# 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.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]
-    output = subprocess.check_output(command).decode().strip().split('/')
+    command = [
+        'ffprobe', '-v', 'error', '-select_streams', 'v:0', 
+        '-show_entries', 'stream=r_frame_rate', 
+        '-of', 'default=noprint_wrappers=1:nokey=1', target_path
+    ]
    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)
+    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'))

-
 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)
@@ -1,23 +1,27 @@
 --extra-index-url https://download.pytorch.org/whl/cu118

-numpy>=1.23.5,<2
-opencv-python==4.8.1.78
-cv2_enumerate_cameras==1.1.15
+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==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==1.16.3; sys_platform != 'darwin'
+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
-customtkinter==5.2.2
+pyobjc==9.1; sys_platform == 'darwin'
+pygrabber==0.2
+pyvirtualcam==0.12.0
+pyobjc-framework-AVFoundation==10.3.1; sys_platform == 'darwin'
@@ -3,73 +3,35 @@ setlocal EnableDelayedExpansion

 :: 1. Setup your platform
 echo Setting up your platform...
-
-:: Python
-where python >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo Python is not installed. Please install Python 3.10 or later.
-    pause
-    exit /b
-)
-
-:: Pip
-where pip >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo Pip is not installed. Please install Pip.
-    pause
-    exit /b
-)
-
-:: Git
-where git >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo Git is not installed. Installing Git...
-    winget install --id Git.Git -e --source winget
-)
-
-:: FFMPEG
-where ffmpeg >nul 2>&1
-if %ERRORLEVEL% neq 0 (
-    echo FFMPEG is not installed. Installing FFMPEG...
-    winget install --id Gyan.FFmpeg -e --source winget
-)
+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
-if exist Deep-Live-Cam (
-    echo Deep-Live-Cam directory already exists.
-    set /p overwrite="Do you want to overwrite? (Y/N): "
-    if /i "%overwrite%"=="Y" (
-        rmdir /s /q Deep-Live-Cam
-        git clone https://github.com/hacksider/Deep-Live-Cam.git
-    ) else (
-        echo Skipping clone, using existing directory.
-    )
-) else (
-    git clone https://github.com/hacksider/Deep-Live-Cam.git
-)
-cd Deep-Live-Cam
+call :clone_repository "https://github.com/iVideoGameBoss/iRoopDeepFaceCam.git" "iRoopDeepFaceCam"

 :: 3. Download Models
 echo Downloading models...
-mkdir models
-curl -L -o models/GFPGANv1.4.pth https://path.to.model/GFPGANv1.4.pth
-curl -L -o models/inswapper_128_fp16.onnx https://path.to.model/inswapper_128_fp16.onnx
+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
+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:
@@ -81,42 +43,83 @@ echo 5. OpenVINO (Intel)
 echo 6. None
 set /p choice="Enter your choice (1-6): "

-if "%choice%"=="1" (
-    echo Installing CUDA dependencies...
-    pip uninstall -y onnxruntime onnxruntime-gpu
-    pip install onnxruntime-gpu==1.16.3
-    set exec_provider="cuda"
-) else if "%choice%"=="2" (
-    echo Installing CoreML (Apple Silicon) dependencies...
-    pip uninstall -y onnxruntime onnxruntime-silicon
-    pip install onnxruntime-silicon==1.13.1
-    set exec_provider="coreml"
-) else if "%choice%"=="3" (
-    echo Installing CoreML (Apple Legacy) dependencies...
-    pip uninstall -y onnxruntime onnxruntime-coreml
-    pip install onnxruntime-coreml==1.13.1
-    set exec_provider="coreml"
-) else if "%choice%"=="4" (
-    echo Installing DirectML dependencies...
-    pip uninstall -y onnxruntime onnxruntime-directml
-    pip install onnxruntime-directml==1.15.1
-    set exec_provider="directml"
-) else if "%choice%"=="5" (
-    echo Installing OpenVINO dependencies...
-    pip uninstall -y onnxruntime onnxruntime-openvino
-    pip install onnxruntime-openvino==1.15.0
-    set exec_provider="openvino"
-) else (
-    echo Skipping GPU acceleration setup.
-)
+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%
+    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
				`@@ -1 +0,0 @@`
				`Please always push on the experimental to ensure we don't mess with the main branch. All the test will be done on the experimental and will be pushed to the main branch after few days of testing.`