  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.
