Personalized Image Generation

Personalized image generation aims to create images of specific subjects or in particular styles using text prompts and a few reference images, overcoming limitations of generic text-to-image models. Current research focuses on developing efficient, tuning-free methods, often employing diffusion models with techniques like adapter layers, masked attention, and multimodal prompts to achieve high-fidelity results while preserving identity and diversity. This field is significant because it enables customized content creation across various applications, from personalized avatars to artistic style transfer, and pushes the boundaries of generative model control and efficiency.