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

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 …

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 …

Diffusion models, which convert noise into new data instances by learning to reverse a
Markov diffusion process, have become a cornerstone in contemporary generative …

Diffusion models, which convert noise into new data instances by learning to reverse a
diffusion process, have become a cornerstone in contemporary generative modeling. In this …

Diffusion models have made rapid progress in generating high-quality samples across
various domains. However, a theoretical understanding of the Lipschitz continuity and …

Score-based diffusion models, while achieving remarkable empirical performance, often
suffer from low sampling speed, due to extensive function evaluations needed during the …

Diffusion models play a pivotal role in contemporary generative modeling, claiming state-of-
the-art performance across various domains. Despite their superior sample quality …

Diffusion probabilistic models (DPMs) have emerged as a promising technology in
generative modeling. The success of DPMs relies on two ingredients: time reversal of …