# Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

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**Published:** 2026年9月12日
**Author:** Iman Khazrak, Narges Nejad, Mostafa M. Rezaee, Robert C. Green II
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/understanding-lora-rank-trade-offs-in-diffusion-model-fine-tuning

arXiv:2609.10656v1 Announce Type: new Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible…

> 内容来自原始来源： **arXiv — cs.AI**
> [在 arXiv — cs.AI 阅读全文](https://arxiv.org/abs/2609.10656)

**标签：** #arXiv

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