Poisson Flow Consistency Training

• URL: http://arxiv.org/abs/2510.21857v1
• Date: Thu, 23 Oct 2025 03:23:11 GMT
• Title: Poisson Flow Consistency Training
• Authors: Anthony Zhang, Mahmut Gokmen, Dennis Hein, Rongjun Ge, Wenjun Xia, Ge Wang, Jin Chen,
• Abstract summary: The Poisson Flow Consistency Model (PFCM) is a consistency-style model based on the robust Poisson Flow Generative Model++ (PFGM++)<n>The objective of this research was to create a method to train the PFCM in isolation called Poisson Flow Consistency Training (PFCT)
• Score: 13.665025524909964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Poisson Flow Consistency Model (PFCM) is a consistency-style model based on the robust Poisson Flow Generative Model++ (PFGM++) which has achieved success in unconditional image generation and CT image denoising. Yet the PFCM can only be trained in distillation which limits the potential of the PFCM in many data modalities. The objective of this research was to create a method to train the PFCM in isolation called Poisson Flow Consistency Training (PFCT). The perturbation kernel was leveraged to remove the pretrained PFGM++, and the sinusoidal discretization schedule and Beta noise distribution were introduced in order to facilitate adaptability and improve sample quality. The model was applied to the task of low dose computed tomography image denoising and improved the low dose image in terms of LPIPS and SSIM. It also displayed similar denoising effectiveness as models like the Consistency Model. PFCT is established as a valid method of training the PFCM from its effectiveness in denoising CT images, showing potential with competitive results to other generative models. Further study is needed in the precise optimization of PFCT and in its applicability to other generative modeling tasks. The framework of PFCT creates more flexibility for the ways in which a PFCM can be created and can be applied to the field of generative modeling.

Related papers

• ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction [7.644299873269135]
  Sparse-view computed tomography (CT) is a practical solution to reduce radiation dose, but the resulting inverse problem poses significant challenges for accurate image reconstruction.<n>Recent advances in generative modeling, particularly Poisson Flow Generative Models (PFGM), enable high-fidelity image synthesis.<n>We propose Residual Poisson Flow (ResPF) Generative Models for efficient and accurate sparse-view CT reconstruction.
  arXiv  Detail & Related papers  (2025-06-06T01:43:35Z)
• Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques [7.694256285730863]
  In this paper, we introduce the beta noise distribution, which provides flexibility in adjusting noise levels. High Noise Improved Consistency Training (HN-iCT) is trained in a supervised fashion. Our results indicate that unconditional image generation using HN-iCT significantly outperforms basic CT and iCT training techniques with NFE=1.
  arXiv  Detail & Related papers  (2024-11-19T02:48:36Z)
• Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization [97.35427957922714]
  We present an algorithm named pairwise sample optimization (PSO), which enables the direct fine-tuning of an arbitrary timestep-distilled diffusion model.<n>PSO introduces additional reference images sampled from the current time-step distilled model, and increases the relative likelihood margin between the training images and reference images.<n>We show that PSO can directly adapt distilled models to human-preferred generation with both offline and online-generated pairwise preference image data.
  arXiv  Detail & Related papers  (2024-10-04T07:05:16Z)
• TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method [2.626378252978696]
  We propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model.
  arXiv  Detail & Related papers  (2024-02-17T13:09:00Z)
• PFCM: Poisson flow consistency models for low-dose CT image denoising [3.3153763098433258]
  We introduce PFCM, a novel family of deep generative models that combines the robustness of PFGM++ with the efficient single-step sampling of consistency models.<n>We exploit to adapt PFCM for the task of low-dose CT image denoising, via a task-specific'' sampler that hijacks'' the generative process.<n>The resulting sampler achieves excellent performance in terms of LPIPS, SSIM, and PSNR on the Mayo low-dose CT dataset.
  arXiv  Detail & Related papers  (2024-02-13T01:39:56Z)
• PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models [3.7080630916211152]
  We present posterior sampling Poisson flow generative models (PPFM), a novel image denoising technique for low-dose and photon-counting CT. Our results shed light on the benefits of the PFGM++ framework compared to diffusion models.
  arXiv  Detail & Related papers  (2023-12-15T12:49:08Z)
• Image Inpainting via Tractable Steering of Diffusion Models [48.16994134964729]
  This paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior.<n>Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs)
  arXiv  Detail & Related papers  (2023-11-28T21:14:02Z)
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference [60.32804641276217]
  We propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs. A high-quality 768 x 768 24-step LCM takes only 32 A100 GPU hours for training. We also introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets.
  arXiv  Detail & Related papers  (2023-10-06T17:11:58Z)
• The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation [42.48819460873482]
  Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures and loss functions.
  arXiv  Detail & Related papers  (2023-06-02T21:26:20Z)
• Training Diffusion Models with Reinforcement Learning [82.29328477109826]
  Diffusion models are trained with an approximation to the log-likelihood objective. In this paper, we investigate reinforcement learning methods for directly optimizing diffusion models for downstream objectives. We describe how posing denoising as a multi-step decision-making problem enables a class of policy gradient algorithms.
  arXiv  Detail & Related papers  (2023-05-22T17:57:41Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Improving and generalizing flow-based generative models with minibatch optimal transport [90.01613198337833]
  We introduce the generalized conditional flow matching (CFM) technique for continuous normalizing flows (CNFs) CFM features a stable regression objective like that used to train the flow in diffusion models but enjoys the efficient inference of deterministic flow models. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference.
  arXiv  Detail & Related papers  (2023-02-01T14:47:17Z)
• Normalizing Flows with Multi-Scale Autoregressive Priors [131.895570212956]
  We introduce channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR) Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. We show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.
  arXiv  Detail & Related papers  (2020-04-08T09:07:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.