  Diffusion models have emerged as a powerful new family of deep generative
models with record-breaking performance in many applications, including image
synthesis, video generation, and molecule design. In this survey, we provide an
overview of the rapidly expanding body of work on diffusion models,
categorizing the research into three key areas: efficient sampling, improved
likelihood estimation, and handling data with special structures. We also
discuss the potential for combining diffusion models with other generative
models for enhanced results. We further review the wide-ranging applications of
diffusion models in fields spanning from computer vision, natural language
generation, temporal data modeling, to interdisciplinary applications in other
scientific disciplines. This survey aims to provide a contextualized, in-depth
look at the state of diffusion models, identifying the key areas of focus and
pointing to potential areas for further exploration. Github:
https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy.
