系统梳理扩散模型的高效设计与应用实践，助力快速落地。
Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
- 从架构、训练到推理部署，聚焦效率优化路径
- 涵盖图像、视频、分子生成等多场景应用优势
- 适合希望快速掌握扩散模型实用技术的研究者
近年来，扩散模型作为最受欢迎且备受关注的生成模型之一，在图像合成、视频生成、分子设计、3D场景渲染及多模态生成等任务中展现出显著优势，其成功源于扎实的理论基础和可靠的实践方法。这一系列进展主要得益于渐进式的设计原则以及高效的架构、训练、推理与部署技术。然而，目前尚缺乏全面深入的综述来系统总结这些原理与实践，以促进扩散模型的快速理解与应用。本文从效率导向视角出发，系统梳理现有研究中的深层原理与高效实践，涵盖架构设计、模型训练、快速推理与可靠部署等方面，以通俗易懂的方式引导后续理论研究、算法迁移及新场景下的模型应用。项目主页：https://github.com/ponyzym/Efficient-DMs-Survey
原文摘要 · Abstract (English)
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. \url{https://github.com/ponyzym/Efficient-DMs-Survey}
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。