  Denoising diffusion models have emerged as the go-to framework for solving
inverse problems in imaging. A critical concern regarding these models is their
performance on out-of-distribution (OOD) tasks, which remains an under-explored
challenge. Realistic reconstructions inconsistent with the measured data can be
generated, hallucinating image features that are uniquely present in the
training dataset. To simultaneously enforce data-consistency and leverage
data-driven priors, we introduce a novel sampling framework called Steerable
Conditional Diffusion. This framework adapts the denoising network specifically
to the available measured data. Utilising our proposed method, we achieve
substantial enhancements in OOD performance across diverse imaging modalities,
advancing the robust deployment of denoising diffusion models in real-world
applications.
