Unified Image

Unified image generation research aims to create single models capable of performing a wide range of image-related tasks, moving beyond specialized models for individual functions like text-to-image generation or image editing. Current efforts focus on diffusion models and multimodal large language models (MLLMs) as core architectures, often employing techniques like in-context learning and multi-stage processing to handle diverse inputs and achieve unified functionality across tasks such as image generation, editing, and even medical image analysis. This approach promises to simplify workflows, improve efficiency, and facilitate knowledge transfer across different image processing domains, impacting fields ranging from computer vision to medical imaging.