1. Clone this repository

git clone https://github.com/Danyache/sber-swap.git
cd sber-swap
git submodule init
git submodule update

2. Install dependent packages

pip install -r requirements.txt

3. Download weights

sh download_models.sh

1. Colab Demo
2. Face Swap On Video

python inference.py

3. Face Swap On Image

python inference.py --target_path examples/images/beckham.jpg --image_to_image True

We also provide the training code for face swap model as follows:

1. Download VGGFace2 Dataset.
2. Crop and align faces with out detection model.

python preprocess_vgg.py --path_to_dataset ./VggFace2/VGG-Face2/data/preprocess_train --save_path ./VggFace2-crop

3. Start training.

python train.py

1. For first epochs we suggest not to use eye detection loss
2. In case of finetuning model you can variate losses coefficients to make result look more like source identity, or vice versa, save features and attributes of target face