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：扩散模型是一种生成模型，通过逐步添加和移除噪声来学习训练数据的潜在分布，从而实现数据生成。扩散模型的各组件已引起广泛关注，并提出了多种设计选择。现有综述主要侧重于高层级解决方案，对组件设计基础的探讨相对不足。本研究旨在填补这一空白，对扩散模型中各组件的设计选择进行全面且连贯的综述。具体而言，我们根据扩散模型的三个关键组件——前向过程、反向过程和采样过程——来组织本综述。这使我们能够提供扩散模型的细粒度视角，有助于未来在单个组件分析、设计选择的适用性以及扩散模型实现等方面的研究。