高效扩散模型：从原理到实践的全面调研

Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices

October 15, 2024

作者: Zhiyuan Ma, Yuzhu Zhang, Guoli Jia, Liangliang Zhao, Yichao Ma, Mingjie Ma, Gaofeng Liu, Kaiyan Zhang, Jianjun Li, Bowen Zhou

cs.AI

摘要

作为近年来最受欢迎和炙手可热的生成模型之一，扩散模型引起了许多研究人员的兴趣，并在诸如图像合成、视频生成、分子设计、3D 场景渲染和多模态生成等各种生成任务中稳定展现出优势，依赖于其丰富的理论原则和可靠的应用实践。这些最近关于扩散模型的显著成功很大程度上源自渐进式设计原则和高效的架构、训练、推断和部署方法。然而，迄今为止还没有全面深入的审查来总结这些原则和实践，以帮助快速理解和应用扩散模型。在这项调查中，我们提供了一个新的以效率为导向的视角，主要侧重于架构设计、模型训练、快速推断和可靠部署中的深刻原则和高效实践，以引导进一步的理论研究、算法迁移和模型应用，为新场景提供读者友好的指导。

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. https://github.com/ponyzym/Efficient-DMs-Survey