Unified Generative

Unified generative modeling aims to create single frameworks capable of handling multiple related tasks within a given domain, avoiding the limitations of separate models. Current research focuses on developing such unified models using architectures like diffusion models, Bayesian flow networks, and transformers, often incorporating techniques like multi-modal alignment and autoregressive generation to improve performance and efficiency. This approach promises significant advancements by streamlining complex processes, improving generalization across tasks, and potentially reducing computational costs in diverse applications ranging from image and video generation to natural language processing and scientific modeling.