Composing diffusion priors with explicit physical context via generative Gibbs sampling

ArXi:2605.10642v1 Announce Type: new Pretrained diffusion models provide powerful learned priors, but in scientific sampling the target distribution often depends on physical context that is not fully represented by one generative model. We