  Diffusion and Poisson flow models have demonstrated remarkable success for a
wide range of generative tasks. Nevertheless, their iterative nature results in
computationally expensive sampling and the number of function evaluations (NFE)
required can be orders of magnitude larger than for single-step methods.
Consistency models are a recent class of deep generative models which enable
single-step sampling of high quality data without the need for adversarial
training. In this paper, we introduce a novel image denoising technique which
combines the flexibility afforded in Poisson flow generative models (PFGM)++
with the, high quality, single step sampling of consistency models. The
proposed method first learns a trajectory between a noise distribution and the
posterior distribution of interest by training PFGM++ in a supervised fashion.
These pre-trained PFGM++ are subsequently "distilled" into Poisson flow
consistency models (PFCM) via an updated version of consistency distillation.
We call this approach posterior sampling Poisson flow consistency models
(PS-PFCM). Our results indicate that the added flexibility of tuning the
hyperparameter D, the dimensionality of the augmentation variables in PFGM++,
allows us to outperform consistency models, a current state-of-the-art
diffusion-style model with NFE=1 on clinical low-dose CT images. Notably, PFCM
is in itself a novel family of deep generative models and we provide initial
results on the CIFAR-10 dataset.
