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

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.

13 pages, 5 figures, 3 tables. Accepted at CSCE 2026

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.10656 [cs.AI]

(or arXiv:2609.10656v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.10656 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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