modelscope / modelscope/DiffSynth-Studio
about batch size in Wan I2V training
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 13.1k
- Forks
- 1.3k
- Avg merge
- 13h 12m
- Merged PRs (30d)
- 45
Description
Hi, when training lora I2V wan model, currently the default batch size is 1. When I try to adjust it to larger size in：
dataloader = torch.utils.data.DataLoader(
dataset,
shuffle=True,
batch_size=1,
num_workers=args.dataloader_num_workers
)
，there is an error: [rank1]: RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 4 but got size 1 for tensor number 1 in the list. I have not find the place to adjust it in the parse. Could you help me know how to increase the batch size? Thank you.
Contributor guide
No contributing guide indexed for this repository
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
The issue names a torch.utils.data.DataLoader snippet but no repository file or test. Start by locating that DataLoader construction and tracing how batch_size is passed from the parser, then reproduce the Wan I2V training error with a value greater than 1. Done means the larger batch size is accepted without the reported tensor-size mismatch.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100