Study finds moderate LoRA ranks most efficient for diffusion fine-tuning
A new arXiv paper presents a controlled study on the trade-offs of Low-Rank Adaptation (LoRA) ranks when fine-tuning diffusion models. The researchers utilized a DDPM U-Net architecture on the CIFAR-10 dataset, testing ranks of 2, 4, 8, 16, and 32 with fixed optimization settings. They measured performance using FID scores, trainable parameter counts, runtime, and GPU memory usage, validating…
Key points
- Rank 4 achieved the best FID score (124.1380) in DDPM fine-tuning experiments on CIFAR-10.
- Higher LoRA ranks yielded limited quality gains despite increased computational and memory costs.
- The study validates that small-to-moderate ranks are practical defaults for fixed training budgets.
The results indicate that moderate ranks offer the best balance between quality and computational cost. Specifically, rank 4 achieved the best FID score of 124.1380, while rank 8 followed closely at 124.2136. Higher ranks provided only limited improvements in image quality despite significantly increasing adaptation costs. The authors conclude that small-to-moderate ranks are practical defaults for practitioners working under fixed training budgets, suggesting that increasing rank size is not always necessary for optimal performance.
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