When using diffusion models to solve inverse problems with multiple valid solutions, tracking prior consistency during sampling—not just measurement fit—prevents the solver from converging to measurement-consistent but prior-inconsistent modes.
This paper addresses a failure mode in diffusion-based solvers for inverse problems: when measurements have multiple valid solutions, existing methods can get stuck in solutions that fit the measurements but don't match the learned prior.