A Closer Look at Parameter-Efficient Tuning in Diffusion Models

• URL: http://arxiv.org/abs/2303.18181v2
• Date: Wed, 12 Apr 2023 14:41:12 GMT
• Title: A Closer Look at Parameter-Efficient Tuning in Diffusion Models
• Authors: Chendong Xiang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu
• Abstract summary: Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications. We investigate parameter-efficient tuning in large diffusion models by inserting small learnable modules.
• Score: 39.52999446584842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient. Motivated by the recent progress in natural language processing, we investigate parameter-efficient tuning in large diffusion models by inserting small learnable modules (termed adapters). In particular, we decompose the design space of adapters into orthogonal factors -- the input position, the output position as well as the function form, and perform Analysis of Variance (ANOVA), a classical statistical approach for analyzing the correlation between discrete (design options) and continuous variables (evaluation metrics). Our analysis suggests that the input position of adapters is the critical factor influencing the performance of downstream tasks. Then, we carefully study the choice of the input position, and we find that putting the input position after the cross-attention block can lead to the best performance, validated by additional visualization analyses. Finally, we provide a recipe for parameter-efficient tuning in diffusion models, which is comparable if not superior to the fully fine-tuned baseline (e.g., DreamBooth) with only 0.75 \% extra parameters, across various customized tasks.

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