Orthogonal Adaptation for Modular Customization of Diffusion Models

TL;DR

This paper introduces Orthogonal Adaptation, a novel method enabling the scalable merging of independently fine-tuned diffusion models for concept customization, maintaining high fidelity and efficiency in synthesis.

Contribution

The paper proposes Orthogonal Adaptation, a simple and versatile technique that encourages orthogonal residual weights in models, allowing seamless merging of customized diffusion models without interference.

Findings

Outperforms baselines in efficiency and identity preservation

Enables merging of independently fine-tuned models

Maintains high fidelity in concept synthesis

Abstract

Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications, enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited, pre-defined set of them, they fall short of achieving scalability, where a single model can seamlessly render countless concepts. In this paper, we address a new problem called Modular Customization, with the goal of efficiently merging customized models that were fine-tuned independently for individual concepts. This allows the merged model to jointly synthesize concepts in one image without compromising fidelity or incurring any additional computational costs. To address this problem, we introduce Orthogonal Adaptation, a method designed to encourage the customized models, which do not have access to each other during fine-tuning, to have orthogonal residual weights. This ensures that during inference time, the customized models can be summed with minimal interference. Our proposed method is both simple and versatile, applicable to nearly all optimizable weights in the model architecture. Through an extensive set of quantitative and qualitative evaluations, our method consistently outperforms relevant baselines in terms of efficiency and identity preservation, demonstrating a significant leap toward scalable customization of diffusion models.