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AlexanderGroshev avatar AlexanderGroshev commented on June 2, 2024

Hi, @nagibator-hero!

The first thing you need to do is to determine the version of the CUDA with the command nvcc --version.
Then install the correct version of mxnet with source 1 or source 2.
requirements.txt corresponds to CUDA 10.1.
To find the correct version of mxnet you can look at #56, #60 or try solution with docker #51.

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battlesaint avatar battlesaint commented on June 2, 2024

For Windows

  1. install anaconda, visual studio tools 2019, 2022 with c++ development checked

    Cuda v10.1 here

    Cudnn v8.0.5 here

    Extract cudnn, copy all and paste to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1

  2. open up anaconda prompt, conda create -n sber python=3.6

  3. conda activate sber

  4. cd F:\AI\ghost (where you want to download)

  5. git clone https://github.com/sberbank-ai/sber-swap.git
    cd sber-swap
    git submodule init
    git submodule update

    (one by one)

  6. pip install -r requirements.txt

    if error, change requests==2.18.4

  7. download models and move to the right directory
    here

  8. Open model_zoo.py from C:\Users(username)\anaconda3\envs\sber\Lib\site-packages\insightface\model_zoo and edit line 23

    def get_model(self):
    session = onnxruntime.InferenceSession(self.onnx_file, providers=none)

    to

    def get_model(self):
    session = onnxruntime.InferenceSession(self.onnx_file, providers=['TensorrtExecutionProvider', 'CUDAExecutionProvider'])

  9. test and enjoy
    python inference.py --source_paths {PATH_TO_IMAGE} --target_faces_paths {PATH_TO_IMAGE} --target_video {PATH_TO_VIDEO}

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