Diffusion models are generative models, which gradually add and remove noise to learn the underlying distribution of training data for data generation. The components of diffusion models have gained significant attention with many design choices proposed. Existing reviews have primarily focused on higher-level solutions, thereby covering less on the design fundamentals of components. This study seeks to address this gap by providing a comprehensive and coherent review on component-wise design choices in diffusion models. Specifically, we organize this review according to their three key components, namely the forward process, the reverse process, and the sampling procedure. This allows us to provide a fine-grained perspective of diffusion models, benefiting future studies in the analysis of individual components, the applicability of design choices, and the implementation of diffusion models.

翻译：扩散模型是一类生成模型，通过逐步添加和去除噪声来学习训练数据的潜在分布，从而实现数据生成。扩散模型的组件因其提出的多种设计方案而备受关注。现有综述主要聚焦于高级解决方案，对组件层面的设计基础涉及较少。本研究旨在弥补这一空白，对扩散模型中各组件级的设计方案进行全面、系统的梳理。具体而言，我们按照三个关键组件——前向过程、反向过程和采样过程——组织本综述。这使我们能够提供扩散模型的细粒度视角，从而有益于未来在单个组件分析、设计方案适用性以及扩散模型实现等方面的研究。