Twitter: @NorowaretaGemu License: MIT
- Python 3.10.0rc2 (or 3.10.x)
- CUDA Toolkit (must be compatible with your GPU and TensorFlow)
- cuDNN (extract into your CUDA installation path)
To verify that CUDA is installed correctly, run the following in Command Prompt or PowerShell:
nvcc --version This should return the version of the CUDA compiler. If it says 'nvcc' is not recognized, make sure the CUDA bin directory is added to your system PATH (e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.0\bin).
For cuDNN, ensure the following cuDNN files are copied into your CUDA folder:
bin\cudnn*.dll → into CUDA_PATH\bin
include\cudnn*.h → into CUDA_PATH\include
lib\x64\cudnn*.lib → into CUDA_PATH\lib\x64
Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned & "C:\Users\YourUsername\AppData\Local\Programs\Python\Python310\python.exe" -m venv onnx-env . .\onnx-env\Scripts\Activate.ps1
pip install -r requirements.txt
Or run:
-
install_requirements.batpip install torch torchvision
. .\onnx-env\Scripts\Activate.ps1 onnx-env\Scripts\activate.bat source onnx-env/Scripts/activate
Using Python directly:
python run.py
Using provided scripts:
Windows:
.\run.bator.\run.ps1
Unix-like systems (Linux/macOS):
-
.\run.sh
--extra-index-url https://download.pytorch.org/whl/cu118 numpy==1.24.3 opencv-python==4.8.0.74 onnx==1.14.0 insightface==0.7.3 psutil==5.9.5 tk==0.1.0 customtkinter==5.2.0 tkinterdnd2==0.3.0; sys_platform != 'darwin' and platform_machine != 'arm64' tkinterdnd2-universal==1.7.3; sys_platform == 'darwin' and platform_machine == 'arm64' pillow==10.0.0 onnxruntime==1.15.1; python_version != '3.9' and sys_platform == 'darwin' and platform_machine != 'arm64' onnxruntime-coreml==1.13.1; python_version == '3.9' and sys_platform == 'darwin' and platform_machine != 'arm64' onnxruntime-silicon==1.13.1; sys_platform == 'darwin' and platform_machine == 'arm64' onnxruntime-gpu==1.15.1; sys_platform != 'darwin' tensorflow==2.13.0 opennsfw2==0.10.2 protobuf==4.23.4 tqdm==4.65.0 gfpgan==1.3.8
Basic - It is more likely to work on your computer, but will be quite slow
Acceleration - Unleash the full potential of your CPU and GPU
Start the program with arguments:
python run.py [options]
-h, --help show this help message and exit
-s SOURCE_PATH, --source SOURCE_PATH select an source image
-t TARGET_PATH, --target TARGET_PATH select an target image or video
-o OUTPUT_PATH, --output OUTPUT_PATH select output file or directory
--frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...] frame processors (choices: face_swapper, face_enhancer, ...)
--keep-fps keep target fps
--keep-frames keep temporary frames
--skip-audio skip target audio
--many-faces process every face
--reference-face-position REFERENCE_FACE_POSITION position of the reference face
--reference-frame-number REFERENCE_FRAME_NUMBER number of the reference frame
--similar-face-distance SIMILAR_FACE_DISTANCE face distance used for recognition
--temp-frame-format {jpg,png} image format used for frame extraction
--temp-frame-quality [0-100] image quality used for frame extraction
--output-video-encoder {libx264,libx265,libvpx-vp9,h264_nvenc,hevc_nvenc} encoder used for the output video
--output-video-quality [0-100] quality used for the output video
--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
-v, --version show program's version number and exit
Using the -s/--source, -t/--target and -o/--output argument will run the program in headless mode.
© Cursed Entertainment