ArticleEntropy (Basel, Switzerland)2025
The Statistical Thermodynamics of Generative Diffusion Models: Phase Transitions, Symmetry Breaking, and Critical Instability.
Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Diffusion-Model-Driven Discovery of Ferroelectrics for Photocurrent Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The Information Dynamics of Generative Diffusion.Entropy (Basel, Switzerland) · 2026Article
- Can We Extract Physics-like Energies from Generative Protein Diffusion Models?bioRxiv : the preprint server for biology · 2025Article
- Explosive neural networks via higher-order interactions in curved statistical manifolds.Nature communications · 2025Article
- Latent Abstractions in Generative Diffusion Models.Entropy (Basel, Switzerland) · 2025Article
- Emerging AI approaches for cancer spatial omics.GigaScience · 2025Review
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1 author.
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Abstract
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 arising from these 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.
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