Generative diffusion models have achieved spectacular performance in many areas of machine learning and generative modeling. While the fundamental ideas behind these models come from non-equilibrium physics, variational inference and stochastic calculus, in this paper we show that many aspects of these models can be understood using the tools of equilibrium statistical mechanics. Using this reformulation, we show that generative diffusion models undergo second-order phase transitions corresponding to symmetry breaking phenomena. We show that these phase-transitions are always in a mean-field universality class, as they are the result of a self-consistency condition in the generative dynamics. We argue that the critical instability that arises from the phase transitions lies at the heart of their generative capabilities, which are characterized by a set of mean-field critical exponents. Finally, we show that the dynamic equation of the generative process can be interpreted as a stochastic adiabatic transformation that minimizes the free energy while keeping the system in thermal equilibrium.

翻译：生成式扩散模型在机器学习和生成建模的诸多领域取得了卓越的性能。尽管这些模型的基本思想源于非平衡物理、变分推断和随机微积分，但本文表明，这些模型的许多方面可以利用平衡统计力学的工具加以理解。基于这一重构，我们证明了生成式扩散模型会发生对应于对称性破缺现象的二阶相变。我们指出这些相变始终属于平均场普适类，因为它们是生成动力学中自洽条件的结果。我们认为，源于相变的临界不稳定性是其生成能力的核心，该能力可由一组平均场临界指数表征。最后，我们证明了生成过程的动力学方程可被解释为一种随机绝热变换，该变换在保持系统处于热平衡的同时最小化自由能。