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

A controlled study submitted to arXiv on 9 Sep 2026 found that moderate LoRA ranks are the most efficient setting for diffusion model fine-tuning, with rank 4 achieving the best DDPM FID of 124.1380 on CIFAR-10 and rank 8 close behind at 124.2136. The study, which tested ranks of 2, 4, 8, 16, and 32 on a DDPM U-Net under fixed optimization settings, reported that higher ranks delivered limited gains despite larger adaptation cost. The authors validated the trends with extended-budget DDPM runs of 20 epochs at ranks 4, 8, and 16 and a Tiny DiT backbone at 10 epochs, concluding that small-to-moderate ranks are practical defaults under fixed training budgets.

Computer Science Artificial Intelligence Submitted on 9 Sep 2026 Title:Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning View PDF /pdf/2609.10656 HTML experimental https://arxiv.org/html/2609.10656v1 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 local-folder pytorch-fid protocol. We report FID, trainable parameters, runtime, and GPU memory, then validate trends with extended-budget DDPM runs 20 epochs; ranks 4/8/16 and a Tiny DiT backbone 10 epochs; ranks 4/8/16 . Results show moderate ranks are most efficient: rank 4 achieves the best DDPM FID 124.1380 , rank 8 is close 124.2136 , and higher ranks provide limited gains despite larger adaptation cost. These findings support small-to-moderate ranks as practical defaults under fixed training budgets. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .