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SVDiff SVDiff: Compact Parameter Space for Diffusion Fine-tuning ICCV 2023 Ligong Han 1,2 Yinxiao Li 2 Han Zhang 2 Peyman Milanfar 2 Dimitris Metaxas 1 Feng Yang 2 1 Rutgers University     2 Google Research [Paper] [Code (coming soon)] [Unofficial Code] [BibTeX] Abstract Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities. However, existing methods for customizing these models are limited by handling multiple personalized subjects and the risk of overfitting. Moreover, their large number of parameters is inefficient for model storage. In this paper, we propose a novel approach to address these limitations in existing text-to-image diffusion models for personalization. Our method involves fine-tuning the singular values of the weight matrices, leading to a compact and efficient parameter space that reduces the...
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