title: Efficient Diffusion Models: A Comprehensive Survey from Principles to
    Practices
summary: As one of the most popular and sought-after generative models in the recent
  years, diffusion models have sparked the interests of many researchers and
  steadily shown excellent advantage in various generative tasks such as image
  synthesis, video generation, molecule design, 3D scene rendering and multimodal
  generation, relying on their dense theoretical principles and reliable
  application practices. The remarkable success of these recent efforts on
  diffusion models comes largely from progressive design principles and efficient
  architecture, training, inference, and deployment methodologies. However, there
  has not been a comprehensive and in-depth review to summarize these principles
  and practices to help the rapid understanding and application of diffusion
  models. In this survey, we provide a new efficiency-oriented perspective on
  these existing efforts, which mainly focuses on the profound principles and
  efficient practices in architecture designs, model training, fast inference and
  reliable deployment, to guide further theoretical research, algorithm migration
  and model application for new scenarios in a reader-friendly way.
  https://github.com/ponyzym/Efficient-DMs-Survey
paper_authors._id: 670f831527d36b2e25ace106
paper_authors.name: Zhiyuan Ma
paper_authors._id: 670f831527d36b2e25ace107
paper_authors.name: Yuzhu Zhang
paper_authors._id: 670f831527d36b2e25ace108
paper_authors.name: Guoli Jia
paper_authors._id: 670f831527d36b2e25ace109
paper_authors.name: Liangliang Zhao
paper_authors._id: 670f831527d36b2e25ace10a
paper_authors.name: Yichao Ma
paper_authors._id: 670f831527d36b2e25ace10b
paper_authors.name: Mingjie Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-17T11:26:46.424Z
paper_authors._id: 637b3a625bd64ccdb1436bf5
paper_authors.avatarUrl: /avatars/33d3efba541ccc5c9aefe0428d5c20bb.svg
paper_authors.fullname: Ma Mingjie
paper_authors.type: user
paper_authors.user: MaMJ
paper_authors._id: 670f831527d36b2e25ace10c
paper_authors.name: Gaofeng Liu
paper_authors._id: 670f831527d36b2e25ace10d
paper_authors.name: Kaiyan Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-16T09:42:56.304Z
paper_authors._id: 60bc94cd85a3ab33829b6211
paper_authors.avatarUrl: /avatars/b57d36c7577fbbb42ea5b963eef4144a.svg
paper_authors.fullname: Kaiyan Zhang
paper_authors.type: user
paper_authors.user: iseesaw
paper_authors._id: 670f831527d36b2e25ace10e
paper_authors.name: Jianjun Li
paper_authors._id: 670f831527d36b2e25ace10f
paper_authors.name: Bowen Zhou
paper_title: Efficient Diffusion Models: A Comprehensive Survey from Principles to
    Practices
paper_summary: As one of the most popular and sought-after generative models in the recent
  years, diffusion models have sparked the interests of many researchers and
  steadily shown excellent advantage in various generative tasks such as image
  synthesis, video generation, molecule design, 3D scene rendering and multimodal
  generation, relying on their dense theoretical principles and reliable
  application practices. The remarkable success of these recent efforts on
  diffusion models comes largely from progressive design principles and efficient
  architecture, training, inference, and deployment methodologies. However, there
  has not been a comprehensive and in-depth review to summarize these principles
  and practices to help the rapid understanding and application of diffusion
  models. In this survey, we provide a new efficiency-oriented perspective on
  these existing efforts, which mainly focuses on the profound principles and
  efficient practices in architecture designs, model training, fast inference and
  reliable deployment, to guide further theoretical research, algorithm migration
  and model application for new scenarios in a reader-friendly way.
  https://github.com/ponyzym/Efficient-DMs-Survey
paper_ai_keywords: diffusion models image synthesis video generation molecule design 3D scene rendering multimodal generation progressive design principles efficient architecture training inference deployment methodologies architecture designs model training fast inference reliable deployment algorithm migration