codeformer/docs/train.md

:milky_way: 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 and the corresponding code sequence 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

🛸 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 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