Results show moderate ranks are most efficient: rank 4 achieves the best DDPM FID, rank 8 is close, and higher ranks provide limited gains despite larger adaptation cost, support small-to-moderate ranks as practical defaults under fixed training budgets.
Abstract
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.
A stochastic few-step generator whose FID does not degrade as the sampling budget grows from 4 to 50 NFE, and the same recipe transfers to text-to-image generation.
Tommaso Martorella, Alexandre Galashov, F. Krause et al.· 1 citation
SplitLite is proposed, a communication-efficient split federated LoRA fine-tuning method that exploits the low effective rank structure of consecutive-epoch activation and gradient residuals, thereby significantly reducing both activation uplink and gradient downlink traffic.
Personalizing text-to-image diffusion models from a few reference images requires preserving subject identity while following prompts that describe new contexts. Full-model fine-tuning is parameter-intensive, whereas low-rank adaptation (LoRA) reduces the number of trainable parameters but leaves open how adaptation ca...
This paper introduces a lightweight probe for multi-step gradients of pretrained weights that incurs no additional GPU memory cost and only marginal time overhead, and employs a spectrum-aware, importance-based rank allocation and optimal initialization derived from multi-step gradients.
Low-Rank Adaptation (LoRA) has become a standard approach for parameter-efficient fine-tuning, yet a fundamental practical question remains unresolved: how should the adapter rank be chosen? An overly small rank may lead to a poorly conditioned optimization landscape, whereas an unnecessarily large rank sacrifices the...
Bo-Wen Zhang, Chang-Rui Fang, Xin-Song Ma et al.· 0 citations
This project presents a focused empirical study of DPZero, a scalable differentially private fine-tuning method for language models that avoids backpropagation and instead uses zeroth-order, forward-only updates, and identifies a plausible operating regime for DPZero.
Anushka Singh· 0 citations
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