AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models

• URL: http://arxiv.org/abs/2410.03941v1
• Date: Fri, 4 Oct 2024 21:57:11 GMT
• Title: AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
• Authors: Artur Kasymov, Marcin Sendera, Michał Stypułkowski, Maciej Zięba, Przemysław Spurek,
• Abstract summary: Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. We introduce AutoLoRA, a novel guidance technique for diffusion models fine-tuned with the LoRA approach.
• Score: 0.9514837871243403
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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