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.

翻译：扩散模型和泊松流模型在多种生成任务中展现出显著成功。然而，其迭代特性导致计算成本高昂，所需的函数评估次数（NFE）可能比单步方法高出数个数量级。一致性模型是近年兴起的一类深度生成模型，能够在无需对抗训练的情况下实现单步高质量数据采样。本文提出一种新颖的图像去噪技术，该技术结合了泊松流生成模型（PFGM）++的灵活性与一致性模型的高质量单步采样能力。所提方法首先通过监督训练方式学习噪声分布与目标后验分布之间的轨迹，随后利用更新版的一致性蒸馏方法将这些预训练的PFGM++模型“蒸馏”为泊松流一致性模型（PFCM）。我们将此方法称为后验采样泊松流一致性模型（PS-PFCM）。实验结果表明，通过调节PFGM++中增广变量维度的超参数D所引入的额外灵活性，使得本方法在临床低剂量CT图像上超越了当前以NFE=1为特征的扩散类最先进模型——一致性模型。值得注意的是，PFCM本身即构成新颖的深度生成模型家族，我们在CIFAR-10数据集上提供了初步实验结果。