![]() ![]() Significantly smaller model size (1.7MB for StableDiffusion) compared toĮxisting methods (vanilla DreamBooth 3.66GB, Custom Diffusion 73MB), making it To enhance the quality of multi-subject image generation and a simple ![]() We also propose a Cut-Mix-Unmix data-augmentation technique Involves fine-tuning the singular values of the weight matrices, leading to aĬompact and efficient parameter space that reduces the risk of overfitting and This paper, we propose a novel approach to address these limitations inĮxisting text-to-image diffusion models for personalization. Moreover, their large number of parameters is inefficient for model storage. Limited by handling multiple personalized subjects and the risk of overfitting. However, existing methods for customizing these models are Generation, enabling the creation of high-quality images from text prompts or Download a PDF of the paper titled SVDiff: Compact Parameter Space for Diffusion Fine-Tuning, by Ligong Han and 5 other authors Download PDF Abstract: Diffusion models have achieved remarkable success in text-to-image ![]()
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