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
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Abstract

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.

SODA:光谱正交分解自适应扩散模型。
以参数高效的方式适应大规模预训练生成模型正在获得关注。低秩自适应等传统方法通过施加约束来实现参数效率,但对于需要高表示能力的任务可能不是最优的。我们提出了一种新的频谱感知自适应框架的生成模型。我们的方法调整了奇异值及其预训练权值的基向量。利用Kronecker积和高效的Stiefel优化器,实现了正交矩阵的参数高效自适应。具体来说,我们引入了光谱正交分解自适应(SODA),它平衡了计算效率和表示能力。对文本到图像扩散模型的广泛评估证明了SODA的有效性,为现有的微调方法提供了频谱感知替代方案。
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