:milky_way: Training Procedures
Preparing Dataset
- Download training dataset: FFHQ
Training
For PyTorch versions >= 1.10, please replace `python -m torch.distributed.launch` in the commands below with `torchrun`.
๐พ Stage I - VQGAN
Training VQGAN:
python -m torch.distributed.launch --nproc_per_node=gpu_num --master_port=4321 basicsr/train.py -opt options/VQGAN_512_ds32_nearest_stage1.yml --launcher pytorch
After VQGAN training, you can pre-calculate code sequence for the training dataset to speed up the later training stages:
python scripts/generate_latent_gt.py
If you don't require training your own VQGAN, you can find pre-trained VQGAN (
vqgan_code1024.pth) and the corresponding code sequence (latent_gt_code1024.pth) in the folder of Releases v0.1.0: https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0
๐ Stage II - CodeFormer (w=0)
Training Code Sequence Prediction Module:
python -m torch.distributed.launch --nproc_per_node=gpu_num --master_port=4322 basicsr/train.py -opt options/CodeFormer_stage2.yml --launcher pytorch
Pre-trained CodeFormer of stage II (
codeformer_stage2.pth) can be found in the folder of Releases v0.1.0: https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0
๐ธ Stage III - CodeFormer (w=1)
Training Controllable Module:
python -m torch.distributed.launch --nproc_per_node=gpu_num --master_port=4323 basicsr/train.py -opt options/CodeFormer_stage3.yml --launcher pytorch
Pre-trained CodeFormer (
codeformer.pth) can be found in the folder of Releases v0.1.0: https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0
:whale: The project was built using the framework BasicSR. For detailed information on training, resuming, and other related topics, please refer to the documentation: https://github.com/XPixelGroup/BasicSR/blob/master/docs/TrainTest.md