  Universal image restoration is a practical and potential computer vision task
for real-world applications. The main challenge of this task is handling the
different degradation distributions at once. Existing methods mainly utilize
task-specific conditions (e.g., prompt) to guide the model to learn different
distributions separately, named multi-partite mapping. However, it is not
suitable for universal model learning as it ignores the shared information
between different tasks. In this work, we propose an advanced selective
hourglass mapping strategy based on diffusion model, termed DiffUIR. Two novel
considerations make our DiffUIR non-trivial. Firstly, we equip the model with
strong condition guidance to obtain accurate generation direction of diffusion
model (selective). More importantly, DiffUIR integrates a flexible shared
distribution term (SDT) into the diffusion algorithm elegantly and naturally,
which gradually maps different distributions into a shared one. In the reverse
process, combined with SDT and strong condition guidance, DiffUIR iteratively
guides the shared distribution to the task-specific distribution with high
image quality (hourglass). Without bells and whistles, by only modifying the
mapping strategy, we achieve state-of-the-art performance on five image
restoration tasks, 22 benchmarks in the universal setting and zero-shot
generalization setting. Surprisingly, by only using a lightweight model (only
0.89M), we could achieve outstanding performance. The source code and
pre-trained models are available at https://github.com/iSEE-Laboratory/DiffUIR
