Generative diffusion in very large dimensions

Abstract

Generative models based on diffusion have become the state of the art in the last few years, notably for image generation. Here, we analyse them in the high-dimensional limit, where data are formed by a very large number of variables. We use methods from statistical physics and focus on two well-controlled high-dimensional cases: a Gaussian model and the Curie-Weiss model of ferromagnetism. In the latter case, we highlight the mechanism of symmetry breaking in the inverse diffusion, and point out that, in order to reconstruct the relative asymmetry of the two low-temperature states, and thus to obtain the correct probability weights, one needs a database with a number of points much larger than the dimension of each data point. We characterize the scaling laws in the number of data and in the number of dimensions for an efficient generation.

10 pages

References in corpus (3)

• Score-Based Generative Modeling through Stochastic Differential Equations
• From non-ergodic eigenvectors to local resolvent statistics and back: a random matrix perspective
• Wavelet Score-Based Generative Modeling

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