  Diffusion Probabilistic Models (DPMs) have recently shown remarkable
performance in image generation tasks, which are capable of generating highly
realistic images. When adopting DPMs for image restoration tasks, the crucial
aspect lies in how to integrate the conditional information to guide the DPMs
to generate accurate and natural output, which has been largely overlooked in
existing works. In this paper, we present a unified conditional framework based
on diffusion models for image restoration. We leverage a lightweight UNet to
predict initial guidance and the diffusion model to learn the residual of the
guidance. By carefully designing the basic module and integration module for
the diffusion model block, we integrate the guidance and other auxiliary
conditional information into every block of the diffusion model to achieve
spatially-adaptive generation conditioning. To handle high-resolution images,
we propose a simple yet effective inter-step patch-splitting strategy to
produce arbitrary-resolution images without grid artifacts. We evaluate our
conditional framework on three challenging tasks: extreme low-light denoising,
deblurring, and JPEG restoration, demonstrating its significant improvements in
perceptual quality and the generalization to restoration tasks.
