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 popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …

The field of deep generative modeling has grown rapidly and consistently over the years.
With the availability of massive amounts of training data coupled with advances in scalable …

Diffusion models have demonstrated exceptional efficacy in various generative applications.
While existing models focus on minimizing a weighted sum of denoising score matching …

Generative models inspired by dynamical transport of measure--such as flows and diffusions-
-construct a continuous-time map between two probability densities. Conventionally, one of …

Reversing a diffusion process by learning its score forms the heart of diffusion-based
generative modeling and for estimating properties of scientific systems. The diffusion …

Diffusion models may be viewed as hierarchical variational autoencoders (VAEs) with two
improvements: parameter sharing for the conditional distributions in the generative process …

Abstract Score-based Generative Models (SGMs) have demonstrated exceptional synthesis
outcomes across various tasks. However, the current design landscape of the forward …

Diffusion models have shown remarkable performance on many generative tasks. Despite
recent success, most diffusion models are restricted in that they only allow linear …

We introduce the first continuous-time score-based generative model that leverages
fractional diffusion processes for its underlying dynamics. Although diffusion models have …