Delta-SVD: Efficient Compression for Personalized Text-to-Image Models

• URL: http://arxiv.org/abs/2508.16863v1
• Date: Sat, 23 Aug 2025 01:21:46 GMT
• Title: Delta-SVD: Efficient Compression for Personalized Text-to-Image Models
• Authors: Tangyuan Zhang, Shangyu Chen, Qixiang Chen, Jianfei Cai,
• Abstract summary: We present Delta-SVD, a post-hoc, training-free compression method that targets the parameter weights update induced by DreamBooth fine-tuning.<n>We show that Delta-SVD achieves substantial compression with negligible loss in generation quality measured by CLIP score, SSIM and FID.
• Score: 25.0585375727713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized text-to-image models such as DreamBooth require fine-tuning large-scale diffusion backbones, resulting in significant storage overhead when maintaining many subject-specific models. We present Delta-SVD, a post-hoc, training-free compression method that targets the parameter weights update induced by DreamBooth fine-tuning. Our key observation is that these delta weights exhibit strong low-rank structure due to the sparse and localized nature of personalization. Delta-SVD first applies Singular Value Decomposition (SVD) to factorize the weight deltas, followed by an energy-based rank truncation strategy to balance compression efficiency and reconstruction fidelity. The resulting compressed models are fully plug-and-play and can be re-constructed on-the-fly during inference. Notably, the proposed approach is simple, efficient, and preserves the original model architecture. Experiments on a multiple subject dataset demonstrate that Delta-SVD achieves substantial compression with negligible loss in generation quality measured by CLIP score, SSIM and FID. Our method enables scalable and efficient deployment of personalized diffusion models, making it a practical solution for real-world applications that require storing and deploying large-scale subject customizations.

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