SVDiff: Compact Parameter Space for Diffusion Fine-Tuning

Understand

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.