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Preparation

Installation

We highly recommand that you use Anaconda for Installation

conda create -n simswap python=3.6
conda activate simswap
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
(option): pip install --ignore-installed imageio
pip install insightface==0.2.1 onnxruntime moviepy
(option): pip install onnxruntime-gpu  (If you want to reduce the inference time)(It will be diffcult to install onnxruntime-gpu , the specify version of onnxruntime-gpu may depends on your machine and cuda version.)
  • We have now updated the prepare document. The main change gpu version of onnx is supported now. If you have configured the environment before, now use pip install onnxruntime-gpu ,You can increase the computing speed.
  • We use the face detection and alignment methods from insightface for image preprocessing. Please download the relative files and unzip them to ./insightface_func/models from this link.
  • We use the face parsing from face-parsing.PyTorch for image postprocessing. Please download the relative file and place it in ./parsing_model/checkpoint from this link.
  • The pytorch and cuda versions above are most recommanded. They may vary.
  • Using insightface with different versions is not recommanded. Please use this specific version.
  • These settings are tested valid on both Windows and Ubuntu.

Pretrained model

There are two archive files in the drive: checkpoints.zip and arcface_checkpoint.tar

  • Copy the arcface_checkpoint.tar into ./arcface_model
  • Unzip checkpoints.zip, place it in the root dir ./

[Google Drive] [Baidu Drive] Password: jd2v

Simswap 512 (optional)

The checkpoint of Simswap 512 beta version has been uploaded in Github release.If you want to experience Simswap 512, feel free to try.

  • Unzip 512.zip, place it in the root dir ./checkpoints.

Note

We expect users to have GPU with at least 3G memory. For those who do not, we provide [Colab Notebook implementation].