You can train diffusion models to solve inverse problems by reformulating posterior sampling as a shifted denoising problem—this gives better results than steering pretrained models and requires far fewer model evaluations.
This paper solves a key problem in using diffusion models for image reconstruction: how to sample from the posterior distribution when you have a measurement constraint.