SimSwap-train

Reimplement of SimSwap training code

Instructions

1.Environment Preparation

（1）Refer to the README document of SIMSWAP to configure the environment and download the pretrained model;
（2）In order to support custom resolution, you need to modify two places in /*your envs*/site-packages/insightface/utils/face_align.py:
  line28: src_all = np.array([src1, src2, src3, src4, src5])
  line53: src = src_all * image_size / 112

2.Making Training Data

python make_dataset.py --dataroot ./dataset/CelebA --extract_size 512 --output_img_dir ./dataset/CelebA/imgs --output_latent_dir ./dataset/CelebA/latents

The face images and latents will be recored in the output_img_dir and output_latent_dir directories.

3.Start Training

（1）New Training

CUDA_VISIBLE_DEVICES=0 python train.py --name CelebA_512 --dataroot ./dataset/CelebA --image_size 512 --display_winsize 512

Training visualization, loss log-files and model weights will be stored in chekpoints/name folder.

（2）Finetuning

CUDA_VISIBLE_DEVICES=0 python train.py --name CelebA_512_finetune --dataroot ./dataset/CelebA --image_size 512 --display_winsize 512 --continue_train

If chekpoints/name is an un-existed folder, it will first copy the official model from chekpoints/people to chekpoints/name; then finetuning.

4.Training Result

（1）CelebA with 224x224 res

（2）CelebA with 512x512 res