  Diffusion models have made remarkable progress in solving various inverse
problems, attributing to the generative modeling capability of the data
manifold. Posterior sampling from the conditional score function enable the
precious data consistency certified by the measurement-based likelihood term.
However, most prevailing approaches confined to the deterministic deterioration
process of the measurement model, regardless of capricious unpredictable
disturbance in real-world sceneries. To address this obstacle, we show that the
measurement-based likelihood can be renovated with restoration-based likelihood
via the opposite probabilistic graphic direction, licencing the patronage of
various off-the-shelf restoration models and extending the strictly
deterministic deterioration process to adaptable clustered processes with the
supposed prototype, in what we call restorer guidance. Particularly, assembled
with versatile prototypes optionally, we can resolve inverse problems with
bunch of choices for assorted sample quality and realize the proficient
deterioration control with assured realistic. We show that our work can be
formally analogous to the transition from classifier guidance to
classifier-free guidance in the field of inverse problem solver. Experiments on
multifarious inverse problems demonstrate the effectiveness of our method,
including image dehazing, rain streak removal, and motion deblurring.
