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DDPM

model1 events
papersSEP 12 04:00 UTC

Study Examines LoRA Rank Trade-offs for Diffusion Model Fine-Tuning

A new arXiv paper reports a controlled experiment on CIFAR-10 using a DDPM U-Net to measure how LoRA rank affects fine-tuning quality and compute cost. The authors tested ranks of 2, 4, 8, 16, and 32 under fixed optimization settings and evaluated results with PyTorch-FID in a reproducible setup. The work aims to give practitioners clearer guidance on choosing a rank that balances output quality against training expense.

lora/diffusion-models/fine-tuning/benchmarks