modelscope / modelscope/DiffSynth-Studio
Qwen-Image-i2L使用的时候建议使用几张图片？
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Description
Qwen-Image-i2L 我看了感觉是很棒的idea，特别是是使用 Coarse + Fine + Bias 作为 LoRA 训练的初始化权重来加速收敛速度。
请教一下使用Qwen-Image-i2L时候的经验：
- 使用 Qwen-Image-i2L-Style 的时候，建议使用几张图片（上限和下限）？
- 使用 Coarse + Fine + Bias 的时候，建议使用几张图片（上限和下限）？
我就想知道一个使用图片数量的经验即可。另外，我不知道不同数量图片下会不会有什么特别的现象，或者其他一些经验，如果有且方便的话，麻烦分享一下，感谢！~
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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
No files, tests, or entry points are mentioned. A maintainer familiar with Qwen-Image-i2L should provide recommended lower and upper image counts for Qwen-Image-i2L-Style and Coarse + Fine + Bias, plus any observed differences; done means the usage guidance is recorded in the issue or documentation.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 25/100