AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models

Artur Kasymov, Marcin Sendera, Michał Stypułkowski, Maciej Zieba, Przemysław Spurek

Keywords: diffusion models, guidance, generative model, LoRA

TL;DR: We propose a novel guidance technique for LoRA-tuned diffusion models that increases the variability of generated images.

Abstract: Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the model to a specific domain, character, style, or concept. However, due to the limited data utilized during training, the fine-tuned model performance is often characterized by strong context bias and a low degree of variability in the generated images. To solve this issue, we introduce AutoLoRA, a novel guidance technique for diffusion models fine-tuned with the LoRA approach. Inspired by other guidance techniques, AutoLoRA searches for a trade-off between consistency in the domain represented by LoRA weights and sample diversity from the base conditional diffusion model. Moreover, we show that incorporating classifier-free guidance for both LoRA fine-tuned and base models leads to generating samples with higher diversity and better quality. The experimental results for several fine-tuned LoRA domains show superiority over existing guidance techniques on selected metrics.

Primary Area: generative models

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Submission Number: 2177