Demystifying Variational Diffusion Models
Fabio De Sousa Ribeiro Ben Glocker
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.