modelscope / modelscope/DiffSynth-Studio
Questions about batch_size
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Description
Hi, Thank you for your great work! I noticed this line in DiffSynth-Studio/diffsynth/trainers/utils.py:
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
It keeps only the first sample of every batch, so the effective batch size becomes 1 no matter what we set. Could you kindly explain the reasoning behind this design choice? Was it intentional to limit each batch to a single sample, or is there a specific use case this addresses?
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start by reading the DataLoader call in DiffSynth-Studio/diffsynth/trainers/utils.py and checking how its dataset and batch settings are used elsewhere. Determine whether collate_fn=lambda x: x[0] is intentional for diffusion training or makes batch_size ineffective. Done means clarifying the rationale and expected batch behavior in the issue or relevant documentation.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100