SimSwap-train
Reimplement of SimSwap training code
趁着中秋节复现了一版SimSwap的训练代码，支持高分辨率数据的训练，分享给大家。
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
5.Inference
I applied spNorm to the high-resolution image during training, which is conducive to the the model learning. Therefore, during Inference you need to modify
swap_result = swap_model(None, frame_align_crop_tenor, id_vetor, None, True)[0]
to
swap_result = swap_model(None, spNorm(frame_align_crop_tenor), id_vetor, None, True)[0]