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 …

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

Text-conditioned image editing has recently attracted considerable interest. However, most
methods are currently limited to one of the following: specific editing types (eg, object …

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 …

We present DiffBIR, a general restoration pipeline that could handle different blind image
restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem …

Diffusion models have significantly advanced the fields of image, audio, and video
generation, but they depend on an iterative sampling process that causes slow generation …

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 …

Diffusion-based image super-resolution (SR) methods are mainly limited by the low
inference speed due to the requirements of hundreds or even thousands of sampling steps …

Diffusion models have been recently studied as powerful generative inverse problem
solvers, owing to their high quality reconstructions and the ease of combining existing …