FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers

• URL: http://arxiv.org/abs/2512.07150v1
• Date: Mon, 08 Dec 2025 04:18:13 GMT
• Title: FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers
• Authors: Jonghyun Park, Jong Chul Ye,
• Abstract summary: FlowLPS solves inverse problems with pretrained flow models via a Langevin Proximal Sampling (LPS) strategy.<n>Our method integrates Langevin dynamics for manifold-consistent exploration with proximal optimization for precise mode seeking, achieving a superior balance between reconstruction fidelity and perceptual quality.
• Score: 51.56484100374058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep generative models have become powerful priors for solving inverse problems, and various training-free methods have been developed. However, when applied to latent flow models, existing methods often fail to converge to the posterior mode or suffer from manifold deviation within latent spaces. To mitigate this, here we introduce a novel training-free framework, FlowLPS, that solves inverse problems with pretrained flow models via a Langevin Proximal Sampling (LPS) strategy. Our method integrates Langevin dynamics for manifold-consistent exploration with proximal optimization for precise mode seeking, achieving a superior balance between reconstruction fidelity and perceptual quality across multiple inverse tasks on FFHQ and DIV2K, outperforming state of the art inverse solvers.

Related papers

• Trajectory Stitching for Solving Inverse Problems with Flow-Based Models [68.36374645801901]
  Flow-based generative models have emerged as powerful priors for solving inverse problems.<n>We propose MS-Flow, which represents the trajectory as a sequence of intermediate latent states rather than a single initial code.<n>We demonstrate the effectiveness of MS-Flow over existing methods on image recovery and inverse problems, including inpainting, super-resolution, and computed tomography.
  arXiv  Detail & Related papers  (2026-02-09T11:36:41Z)
• Solving Diffusion Inverse Problems with Restart Posterior Sampling [2.9527010146189556]
  Inverse problems are fundamental to science and engineering, where the goal is to infer an underlying signal or state from noisy measurements.<n>Recent approaches employ diffusion models as powerful implicit priors for such problems, owing to their ability to capture complex data distributions.<n>We propose Restart for Posterior Sampling (RePS), a general and efficient framework for solving both linear and non-linear inverse problems.
  arXiv  Detail & Related papers  (2025-11-24T20:42:33Z)
• Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving [18.226350407462643]
  We propose LFlow, a training-free framework for solving linear inverse problems via pretrained latent flow priors.<n>Our proposed method outperforms state-of-the-art latent diffusion solvers in reconstruction quality across most tasks.
  arXiv  Detail & Related papers  (2025-11-08T21:20:59Z)
• FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems [51.99765487172328]
  Posterior sampling for inverse problem solving can be effectively achieved using flows.<n>Flow-Driven Posterior Sampling (FlowDPS) outperforms state-of-the-art alternatives.
  arXiv  Detail & Related papers  (2025-03-11T07:56:14Z)
• Solving Video Inverse Problems Using Image Diffusion Models [58.464465016269614]
  We introduce an innovative video inverse solver that leverages only image diffusion models.<n>Our method treats the time dimension of a video as the batch dimension image diffusion models.<n>We also introduce a batch-consistent sampling strategy that encourages consistency across batches.
  arXiv  Detail & Related papers  (2024-09-04T09:48:27Z)
• CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems [3.3969056208620128]
  We propose to push the boundary of inference steps to 1-2 NFEs while still maintaining high reconstruction quality. Our method achieves new state-of-the-art in diffusion-based inverse problem solving.
  arXiv  Detail & Related papers  (2024-07-17T15:57:50Z)
• Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance [52.093434664236014]
  Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems. Inspired by this finding, we propose to improve recent methods by using more principled covariance determined by maximum likelihood estimation.
  arXiv  Detail & Related papers  (2024-02-03T13:35:39Z)
• Training-free Linear Image Inverses via Flows [17.291903204982326]
  We propose a training-free method for solving linear inverse problems by using pretrained flow models. Our approach requires no problem-specific tuning across an extensive suite of noisy linear inverse problems on high-dimensional datasets.
  arXiv  Detail & Related papers  (2023-09-25T22:13:16Z)
• Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems [14.809545109705256]
  This paper presents a fast and effective solution by proposing a simple closed-form approximation to the likelihood score. For both diffusion and flow-based models, extensive experiments are conducted on various noisy linear inverse problems. Our method demonstrates highly competitive or even better reconstruction performances while being significantly faster than all the baseline methods.
  arXiv  Detail & Related papers  (2022-11-20T01:09:49Z)
• Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problem [97.64313409741614]
  We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators. We propose to do posterior sampling in the latent space of a pre-trained generative model.
  arXiv  Detail & Related papers  (2022-06-18T03:47:37Z)
• Improving Diffusion Models for Inverse Problems using Manifold Constraints [55.91148172752894]
  We show that current solvers throw the sample path off the data manifold, and hence the error accumulates. To address this, we propose an additional correction term inspired by the manifold constraint. We show that our method is superior to the previous methods both theoretically and empirically.
  arXiv  Detail & Related papers  (2022-06-02T09:06:10Z)

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.