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

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 …

Abstract Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding
excessive compute demands by training a diffusion model in a compressed lower …

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …

Large-scale diffusion-based generative models have led to breakthroughs in text-
conditioned high-resolution image synthesis. Starting from random noise, such text-to-image …

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …

Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite
their high-quality generation performance, DPMs still suffer from their slow sampling as they …

We argue that the theory and practice of diffusion-based generative models are currently
unnecessarily convoluted and seek to remedy the situation by presenting a design space …

Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution
image synthesis, especially in recent large-scale text-to-image generation applications. An …

Denoising diffusion models hold great promise for generating diverse and realistic human
motions. However, existing motion diffusion models largely disregard the laws of physics in …