SODA: Spectral Orthogonal Decomposition Adaptation for Diffusion Models

Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Vladimir Pavlovic, Hao Wang, Molei Tao, Dimitris Metaxas; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4665-4682

Adapting large-scale pre-trained generative models in a parameter-efficient manner is gaining traction. Traditional methods like low rank adaptation achieve parameter efficiency by imposing constraints but may not be optimal for tasks requiring high representation capacity. We propose a novel spectrum-aware adaptation framework for generative models. Our method adjusts both singular values and their basis vectors of pretrained weights. Using the Kronecker product and efficient Stiefel optimizers we achieve parameter-efficient adaptation of orthogonal matrices. Specifically we introduce Spectral Orthogonal Decomposition Adaptation (SODA) which balances computational efficiency and representation capacity. Extensive evaluations on text-to-image diffusion models demonstrate SODA's effectiveness offering a spectrum-aware alternative to existing fine-tuning methods.