Just for your information, latest version of "richard roop" is actually called FastFaceSwap (FFS) and is on my github. It has more features, that you might like. It's also faster. Take a video and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training.
Also allow row render (original, faceswap, enchance face via codeformer or gfpgan):
1) install visual studio 2022 with desktop development C++ and python development (not sure about python development)
2) install python 3.10.x (any 3.10)
3) download the last version of roop
4) pip install virtualenv
5) virtualenv venv
6) start venv\scripts\activate.bat
7) pip install -r requirements.txt
that's for cpu (sometimes works for gpu for some reason)
if you want nvidia gpu to work:
don't go for cpu
1) install visual studio 2022 with desktop development C++ and python development (not sure about python development)
2) install cuda 11.7 (https://developer.nvidia.com/cuda-11-7-0-download-archive)
3) download cudnn 8.9.1 for cuda 11.x https://developer.nvidia.com/rdp/cudnn-archive
4) unpack cudnn over C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7 with replacement
5) install python 3.10.x (any 3.10)
6) download the last version of roop
7) pip install virtualenv
8) virtualenv venv
9) start venv\scripts\activate.bat
10) pip install torch torchvision torchaudio --force-reinstall --index-url https://download.pytorch.org/whl/cu117
11) pip install -r requirements.txt
and yes, don't forget to download ffmpeg https://ffmpeg.org/download.html
and inswrapper_128.onnx https://drive.google.com/file/d/1eu60OrRtn4WhKrzM4mQv4F3rIuyUXqfl/view?usp=drive_link
#usage is simple:
python run.py --gpu --gpu-threads %number_of_threads%
for number of threads I recommend to play, for nice approximation of first step is: amount of threads = (GPU VRAM - 1)/800
You can also apply face enchancement by codeformer or gfpgan (slow!!)
To do:
Or if you want just enhance your video:
To do:
Or if you want just enhance your video:
It's recommended to set settings in options/codeformer.json like:
{
"background_enhance": false,
"codeformer_fidelity": 0.5,
"face_upsample": true,
"skip_if_no_face": true,
"upscale": 1
}
Effective implementation of GFPGAN on ONNX (I gain 2.5x speedup)
To use:
./models/GFPGANv1.3.onnx
Allow you to selective swap faces on img/video, the same as in "refacer" project
To use:
https://github.com/janvarev/chain-img-processor licensed under MIT
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