A Gaussian Perspective for Distributional Discrepancy in Generative Diffusion Models

This paper introduces an analytical approach to quantifying and optimizing the distributional discrepancy in generative diffusion models.

For a multivariate Gaussian source, we explicitly derive the closed-form evolution trajectory and the resulting Kullback-Leibler (KL) divergence between the distributions of the source data and the reversely sampled data.

Asymptotic analysis via the Euler-Maclaurin expansion characterizes the convergence behavior of this KL divergence, extracting its dominant term as an explicit functional of the noise schedule.

Minimizing this dominant term via the calculus of variations yields a noise schedule described by a tangent law, inherently determined by the source covariance spectrum.

We further prove that the Gaussian source exhibits an extremal property for the KL divergence among general source distributions with a given covariance.

We also utilize the analytical KL divergence as a principled metric to identify efficient time discretization strategies for pretrained diffusion models, and demonstrate via experiments over diverse datasets that the identified strategies consistently outperform established baselines, particularly under constrained function evaluation budgets.