modelscope / modelscope/DiffSynth-Studio
VACE finetune时候，如何断点训练？
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Description
你好👋 Great jobs!
请问在训练VACE模型的时候，如何实现断点ckpt加载训练呢？
accelerate launch examples/wanvideo/model_training/train.py
--dataset_base_path data/example_video_dataset
--dataset_metadata_path data/example_video_dataset/metadata_vace.csv
--data_file_keys "video,vace_video,vace_reference_image"
--height 480
--width 832
--num_frames 49
--dataset_repeat 100
--model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-1.3B:Wan2.1_VAE.pth"
--learning_rate 1e-4
--num_epochs 2
--remove_prefix_in_ckpt "pipe.vace."
--output_path "./models/train/Wan2.1-VACE-1.3B_full"
--trainable_models "vace"
--extra_inputs "vace_video,vace_reference_image"
--use_gradient_checkpointing_offload
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with examples/wanvideo/model_training/train.py and the supplied accelerate launch command. Check how this entry point handles checkpoint loading and resume-related arguments, then document the supported procedure and a complete VACE command so users can verify that training continues from the saved checkpoint.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100