We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution (SR). Specifically, by employing our …

Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models
have become increasingly popular in solving the real-world image super-resolution …

We propose residual denoising diffusion models (RDDM) a novel dual diffusion process that
decouples the traditional single denoising diffusion process into residual diffusion and noise …

While super-resolution (SR) methods based on diffusion models exhibit promising results
their practical application is hindered by the substantial number of required inference steps …

Text-based diffusion models have exhibited remarkable success in generation and editing
showing great promise for enhancing visual content with their generative prior. However …

While deep learning-based methods for blind face restoration have achieved
unprecedented success, they still suffer from two major limitations. First, most of them …

Recent advances in vision-language models like Stable Diffusion have shown remarkable
power in creative image synthesis and editing. However most existing text-to-image editing …

We present DREAM a novel training framework representing Diffusion Rectification and
Estimation-Adaptive Models requiring minimal code changes (just three lines) yet …

Diffusion Models (DMs) represent a significant advancement in image Super-Resolution
(SR), aligning technical image quality more closely with human preferences and expanding …

Diffusion models have established new state of the art in a multitude of computer vision
tasks, including image restoration. Diffusion-based inverse problem solvers generate …