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

Designing and generating new data under targeted properties has been attracting various
critical applications such as molecule design, image editing and speech synthesis …

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 …

Priors are essential for reconstructing images from noisy and/or incomplete measurements.
The choice of the prior determines both the quality and uncertainty of recovered images. We …

Text-to-image diffusion models often make implicit assumptions about the world when
generating images. While some assumptions are useful (eg, the sky is blue), they can also …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

Inspired by the remarkable success of Latent Diffusion Models (LDMs) for image synthesis,
we study LDM for text-to-video generation, which is a formidable challenge due to the …

Abstract Diffusion Probabilistic Models (DPMs) have recently been employed for image
deblurring, formulated as an image-conditioned generation process that maps Gaussian …

Diffusion models are generative models, which gradually add and remove noise to learn the
underlying distribution of training data for data generation. The components of diffusion …

Diffusion models can be used as learned priors for solving various inverse problems.
However, most existing approaches are restricted to linear inverse problems, limiting their …