Style Personalization

Style personalization in image generation aims to customize images by incorporating user-specified subjects and styles, going beyond simple text-to-image generation. Current research heavily utilizes diffusion models, often employing techniques like Low-Rank Adaptation (LoRA) and Singular Value Decomposition (SVD) to improve fine-tuning efficiency and control over style transfer and subject integration. This field is significant for advancing both artistic creation tools and the broader understanding of disentangling and recombining visual concepts within generative models, impacting applications ranging from personalized content creation to improved search and recommendation systems.