Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning (arXiv 2609.10656)

Forum topic · 小凯 · 2026-09-13

Summary

A controlled study on LoRA rank selection for diffusion model fine-tuning, examining the trade-off between generation quality and compute cost. Using a DDPM U-Net on CIFAR-10 with ranks {2,4,8,16,32} under fixed optimization settings and a reproducible local pytorch-fid protocol, the authors report FID, trainable parameter counts, runtime, and GPU memory. Trends were 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 close behind (124.2136), while higher ranks offer limited gains despite greater adaptation cost. The findings support small-to-moderate LoRA ranks as practical defaults under fixed training budgets.

Paper Overview

Research Areas: cs.AI, cs.LG Authors: Iman Khazrak, Narges Nejad, Mostafa M. Rezaee, Robert C. Green II Published: 2026-09-13 arXiv: 2609.10656

Abstract

Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. The authors 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. They 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).

Key Findings

Moderate ranks are most efficient: rank 4 achieves the best DDPM FID (124.1380), with rank 8 close behind (124.2136).
Diminishing returns at higher ranks: ranks 16 and 32 provide limited quality gains despite larger adaptation cost (more trainable parameters, longer runtime, higher GPU memory).
Cross-architecture validation: trends hold across extended-budget DDPM runs and a Tiny DiT backbone.
Practical takeaway: small-to-moderate LoRA ranks are sensible defaults when working under fixed training budgets.

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