β-Flow unifies diffusion and flow-based inverse problem solvers through a single framework with tunable prior strength, showing that strict priors help with sparse data while loose priors work better with informative observations.
This paper presents β-Flow, a unified method for solving inverse problems using diffusion and flow-based models as priors. It uses exact Gaussian conditioning (Kalman updates) to guide sampling, avoiding gradient computation and working across different trajectory types.