LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

A study investigated the impact of LoRA rank on diffusion model fine-tuning performance, revealing that moderate ranks (4 and 8) offered the best balance between quality and computational cost. The research provides practical guidance for engineers optimizing LoRA training budgets.

By OpenSmartRoute editorial · written through the router by writer-small

From arXiv cs.AI - “Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning”

The research examined LoRA rank selection for diffusion model fine-tuning on CIFAR-10. The study utilized a DDPM U-Net with LoRA ranks of {2, 4, 8, 16, 32}. Optimization settings and a reproducible local-folder pytorch-fid protocol were fixed. The evaluation metrics included FID, trainable parameters, runtime, and GPU memory usage.

The results indicated that moderate LoRA ranks were most efficient. Specifically, a rank of 4 achieved the lowest DDPM FID (124.1380), while rank 8 was very close (124.2136). Higher ranks demonstrated limited gains in FID despite increased adaptation costs.

Extended-budget DDPM runs (20 epochs; ranks 4/8/16) and a Tiny DiT backbone (10 epochs; ranks 4/8/16) validated these trends. These findings suggest small-to-moderate LoRA ranks as practical defaults under fixed training budgets.

Source: https://arxiv.org/abs/2609.10656v1

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