Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

This paper introduces Diffusion Policy, a new way of generating robot behavior by
representing a robot's visuomotor policy as a conditional denoising diffusion process. We …

We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to
previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone …

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of
proteins and their interactions, enabling a huge range of applications in protein modelling …

Text-guided generative diffusion models unlock powerful image creation and editing tools.
Recent approaches that edit the content of footage while retaining structure require …

We explore a new class of diffusion models based on the transformer architecture. We train
latent diffusion models of images, replacing the commonly-used U-Net backbone with a …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D
generative models which produce a single output representation, Shap-E directly generates …