Despite the growing popularity of diffusion models, gaining a deep understanding of the model class remains somewhat elusive for the uninitiated in non-equilibrium statistical physics. With that in mind, we present what we believe is a more straightforward introduction to diffusion models using directed graphical modelling and variational Bayesian principles, which imposes relatively fewer prerequisites on the average reader. Our exposition constitutes a comprehensive technical review spanning from foundational concepts like deep latent variable models to recent advances in continuous-time diffusion-based modelling, highlighting theoretical connections between model classes along the way. We provide additional mathematical insights that were omitted in the seminal works whenever possible to aid in understanding, while avoiding the introduction of new notation. We envision this article serving as a useful educational supplement for both researchers and practitioners in the area, and we welcome feedback and contributions from the community at https://github.com/biomedia-mira/demystifying-diffusion.

翻译：尽管扩散模型日益流行，但对于非平衡统计物理学领域的初学者而言，深入理解这类模型仍然颇具挑战。鉴于此，我们提出了一种我们认为更为直观的扩散模型导论——利用有向图模型和变分贝叶斯原理，这大大降低了对普通读者的预备知识要求。我们的阐述构成了一篇全面的技术综述，涵盖从深度潜变量模型等基础概念到基于连续时间扩散建模的最新进展，其间重点揭示了不同模型类别之间的理论联系。我们尽可能补充了早期开创性工作中省略的数学见解以助理解，同时避免引入新符号。我们希望本文能成为该领域研究人员和实践者有用的教学补充材料，并欢迎社区通过 https://github.com/biomedia-mira/demystifying-diffusion 提供反馈与贡献。