Summary
A controlled study on choosing LoRA rank for diffusion model fine-tuning, balancing generation quality against compute cost. The authors fine-tune a DDPM U-Net on CIFAR-10 with ranks {2,4,8,16,32} under fixed optimization settings, measuring FID, trainable parameters, runtime, and GPU memory via a reproducible local pytorch-fid protocol. Trends are validated 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 nearly identical (124.2136), while higher ranks yield limited gains despite larger adaptation cost. The paper supports small-to-moderate LoRA ranks as practical defaults under fixed training budgets.
Paper Overview
- arXiv: 2609.10656
- Authors: Iman Khazrak, Narges Nejad, Mostafa M. Rezaee, Robert C. Green II
- Categories: cs.AI, cs.LG
- Published: 2026-09-13
Abstract (original)
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.Key takeaways
- Controlled LoRA rank sweep {2, 4, 8, 16, 32} on a DDPM U-Net over CIFAR-10 with fixed optimization settings.
- Evaluation uses a reproducible local-folder pytorch-fid protocol, reporting FID, trainable parameters, runtime, and GPU memory.
- Rank 4 gives the best DDPM FID (124.1380); rank 8 is nearly as good (124.2136).
- Higher ranks (16, 32) deliver limited quality gains despite increased parameters, runtime, and GPU memory.
- Trends validated with extended-budget DDPM runs (20 epochs, ranks 4/8/16) and a Tiny DiT backbone (10 epochs, ranks 4/8/16).
- Practical recommendation: use small-to-moderate LoRA ranks as defaults when training budget is fixed.
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*Auto-collected on 2026-09-13*
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