Diffusion models, a powerful and universal generative AI technology, have achieved
tremendous success in computer vision, audio, reinforcement learning, and computational …

Diffusion models, a powerful and universal generative artificial intelligence technology, have
achieved tremendous success and opened up new possibilities in diverse applications. In …

Abstract We present Scalable Interpolant Transformers (SiT), a family of generative models
built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which …

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 propose a simple, efficient, yet powerful framework for dense visual predictions based
on the conditional diffusion pipeline. Our approach follows a" noise-to-map" generative …

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

We give an improved theoretical analysis of score-based generative modeling. Under a
score estimate with small $ L^ 2$ error (averaged across timesteps), we provide efficient …

Consistency Models (CM)(Song et al., 2023) accelerate score-based diffusion model
sampling at the cost of sample quality but lack a natural way to trade-off quality for speed. To …

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 …

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 …