We provide theoretical convergence guarantees for score-based generative models (SGMs)
such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of …

Diffusion models achieve state-of-the-art performance in various generation tasks. However,
their theoretical foundations fall far behind. This paper studies score approximation …

A class of generative models that unifies flow-based and diffusion-based methods is
introduced. These models extend the framework proposed in Albergo & Vanden-Eijnden …

We provide the first polynomial-time convergence guarantees for the probabilistic flow ODE
implementation (together with a corrector step) of score-based generative modeling. Our …

We develop a framework for non-asymptotic analysis of deterministic samplers used for
diffusion generative modeling. Several recent works have analyzed stochastic samplers …

While efficient distribution learning is no doubt behind the groundbreaking success of
diffusion modeling, its theoretical guarantees are quite limited. In this paper, we provide the …

Diffusion models are a powerful method for generating approximate samples from high-
dimensional data distributions. Several recent results have provided polynomial bounds on …

Recent works have shown that diffusion models can learn essentially any distribution
provided one can perform score estimation. Yet it remains poorly understood under what …

We explore the methodology and theory of reward-directed generation via conditional
diffusion models. Directed generation aims to generate samples with desired properties as …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …