modelscope / modelscope/DiffSynth-Studio
Question about optimal hyper parameters for training Qwen-Image-Edit-2509
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Description
DiffSynth is great. I am trying to train Qwen-Image-Edit-2509. I have a dataset of 5.4k examples. I'm curious if full parameter fine-tuning could be feasible with this dataset size, and if so at what hyperparameters? I've tried 1e-5 and 5e-6 at a 128 effective batch size and the model degrades when I evaluate it.
The next thing I'll try is LoRA, but I'm just confused how I couldn't get the training to have any positive impact on the model at any point across a variety of training runs. Curious if anyone, besides the Qwen team, knows how to successfully fine-tune this model. Thanks!
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Research direction
No file, test, or entry point is identified in the issue. Start by reviewing the reported full-parameter training runs, dataset size, learning rates, and effective batch size, then compare them with the proposed LoRA approach; done would require an agreed, reproducible fine-tuning configuration that improves evaluation results.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100