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🌌 Training Procedures

English | 简体中文

Preparing Dataset

  • Download training dataset: FFHQ

Training

👾 Stage I - VQGAN

  • Training VQGAN:

    python -m torch.distributed.launch --nproc_per_node=8 --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

🚀 Stage II - CodeFormer (w=0)

  • Training Code Sequence Prediction Module:

    python -m torch.distributed.launch --nproc_per_node=8 --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

🛸 Stage III - CodeFormer (w=1)

  • Training Controllable Module:

    python -m torch.distributed.launch --nproc_per_node=8 --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

🐳 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