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Generative diffusion models have achieved spectacular performance in many
areas of generative modeling. While the fundamental ideas behind these models
come from non-equilibrium physics, 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 argue that this lead to a form of instability that lies at the
heart of their generative capabilities and that can be described by a set of
mean field critical exponents. We conclude by analyzing recent work connecting
diffusion models and associative memory networks in view of the thermodynamic
formulations.
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