Diffusion Network

Diffusion networks are a class of generative models increasingly used for various tasks, primarily leveraging the process of iteratively denoising noisy data to generate high-quality outputs. Current research focuses on improving efficiency (e.g., one-step diffusion), enhancing control and stability through techniques like ensemble methods and variational score distillation, and adapting diffusion models to diverse applications such as image super-resolution, 3D modeling, and trajectory prediction. This rapidly evolving field is significantly impacting areas like computer vision, scientific computing, and information diffusion analysis by offering powerful tools for data generation, uncertainty quantification, and complex system modeling.