  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 into two stages: 1) degradation
removal: removing image-independent content; 2) information regeneration:
generating the lost image content. Each stage is developed independently but
they work seamlessly in a cascaded manner. In the first stage, we use
restoration modules to remove degradations and obtain high-fidelity restored
results. For the second stage, we propose IRControlNet that leverages the
generative ability of latent diffusion models to generate realistic details.
Specifically, IRControlNet is trained based on specially produced condition
images without distracting noisy content for stable generation performance.
Moreover, we design a region-adaptive restoration guidance that can modify the
denoising process during inference without model re-training, allowing users to
balance realness and fidelity through a tunable guidance scale. Extensive
experiments have demonstrated DiffBIR's superiority over state-of-the-art
approaches for blind image super-resolution, blind face restoration and blind
image denoising tasks on both synthetic and real-world datasets. The code is
available at https://github.com/XPixelGroup/DiffBIR.
