AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models (2410.03941v1)

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.

The paper "AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models" introduces an innovative approach to enhance the performance of diffusion models fine-tuned using Low-Rank Adaptation (LoRA). This technique specifically targets the challenges associated with fine-tuning models with limited context data, which often results in strong context bias and reduced variability in image generation.

Here are the key elements of the paper:

Low-Rank Adaptation (LoRA):

LoRA is a fine-tuning strategy that adapts diffusion models to specific domains, characters, styles, or concepts with a minimal number of context examples. Despite its effectiveness, LoRA tends to offer limited variability due to the constraints imposed by the small dataset used for tuning.

Introduction of AutoLoRA:

AutoLoRA is proposed as a solution to balance the trade-off between maintaining consistency in the domain imposed by LoRA weights and preserving sample diversity derived from the base conditional diffusion model. Essentially, it seeks to overcome the restrictive bias introduced by LoRA.

Guidance Techniques:

Inspired by existing guidance mechanisms in diffusion models, AutoLoRA incorporates features from classifier-free guidance. This aids in fine-tuning both LoRA-adapted and base models, ultimately enhancing the diversity and quality of generated samples.

Experimental Results:

The paper presents experiments across various domains fine-tuned with LoRA. These experiments demonstrate AutoLoRA's superiority over current guidance techniques, reflected in their selected evaluation metrics. The results show enhanced diversity and improved quality of images, validating the proposed method's effectiveness.

In summary, AutoLoRA advances diffusion model fine-tuning by introducing a novel interaction between adaptive guidance and low-rank adaptation, addressing key limitations of existing techniques and broadening the application potential for conditional generative diffusion models.