Diffusion modeling from a Bayesian inference perspective: instead of denoising a sample, we infer it from repeated noisy measurements.
Our perspective generalizes Bayesian Flow Networks and achieves better sample quality in fewer steps than BFNs.
arxiv.org/abs/2502.07580
github.com/martenlienen...
