Nonequilbrium physics of generative diffusion models

• URL: http://arxiv.org/abs/2405.11932v2
• Date: Wed, 21 Aug 2024 08:11:59 GMT
• Title: Nonequilbrium physics of generative diffusion models
• Authors: Zhendong Yu, Haiping Huang,
• Abstract summary: Generative diffusion models apply the concept of Langevin dynamics in physics to machine leaning. We provide a transparent physics analysis of diffusion models, formulating the fluctuation theorem, entropy production, equilibrium measure, and Franz-Parisi potential. Our study links thermodynamics, statistical inference and geometry based analysis together to yield a coherent picture about how the generative diffusion models work.
• Score: 2.5690340428649328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative diffusion models apply the concept of Langevin dynamics in physics to machine leaning, attracting a lot of interests from engineering, statistics and physics, but a complete picture about inherent mechanisms is still lacking. In this paper, we provide a transparent physics analysis of diffusion models, formulating the fluctuation theorem, entropy production, equilibrium measure, and Franz-Parisi potential to understand the dynamic process and intrinsic phase transitions. Our analysis is rooted in a path integral representation of both forward and backward dynamics, and in treating the reverse diffusion generative process as a statistical inference, where the time-dependent state variables serve as quenched disorder akin to that in spin glass theory. Our study thus links stochastic thermodynamics, statistical inference and geometry based analysis together to yield a coherent picture about how the generative diffusion models work.

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