Generative Manifold Distillation: Aligning Restoration Trajectories with the Natural Image Prior

Pre-trained image restoration models often fail on out-of-distribution (OOD) real-world degradations. Adapting to these domainsis challenging as real-world data lacks paired ground truth, and unsuper-vised methods often require unstable architectural changes. We proposeGenerative Manifold Distillation (GMD), which reframes domain adapta-tion as geometric manifold alignment. GMD operates in a strictly unpairedsetting, requiring only low-quality (LQ) target observations. By leveragingthe flow-matching dynamics of a frozen text-to-image foundation model,GMD projects off-manifold restorations onto the natural image manifoldto generate high-quality pseudo-targets. To ensure stability, a quality-gated manifold filter rejects off-manifold samples, while source-anchoredtrajectory regularization prevents error accumulation. Ultimately, GMDdistills a powerful generative prior into an efficient restoration network.Experiments demonstrate that GMD seamlessly adapts to new distribu-tions using only LQ inputs, drastically improving perceptual quality withzero architectural modifications or added inference latency.