Drift Flow Matching

• URL: http://arxiv.org/abs/2605.17244v1
• Date: Sun, 17 May 2026 03:58:01 GMT
• Title: Drift Flow Matching
• Abstract summary: We propose Drift Flow Matching, a framework that connects drifting generative modeling with flow-based iterative generation.<n>DFM preserves the efficiency of direct transport maps while enabling generation to be refined through multiple inference steps.
• Score: 69.16550765154163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference computation can improve generation quality. In contrast, Drift Models offer efficient one-step generation, but their direct generation paradigm limits such flexibility. In this work, we propose Drift Flow Matching (DFM), a framework that connects drifting generative modeling with flow-based iterative generation. DFM preserves the efficiency of direct transport maps while enabling generation to be refined through multiple inference steps when desired. This bridges the gap between one-step Drift Models and multi-step Flow Matching methods, and provides a novel generative paradigm that can adapt sampling computation to different quality--efficiency requirements. Extensive experiments across different tasks and datasets demonstrate the effectiveness and generality of the proposed framework.

Related papers

• Perceptual Flow Matching for Few-Step Generative Modeling [64.26537634881244]
  Perceptual Flow Matching is a framework for few-step generation in flow-matching models.<n>We show that PFM produces high-quality few-step generators when supervised in an appropriate representation space.
  arXiv  Detail & Related papers  (2026-07-03T17:55:01Z)
• On Approaches to Building Surrogate ODE Models for Diffusion Bridges [39.92969675794945]
  Diffusion and Schrdinger Bridge models have established state-of-the-art performance in generative modeling.<n>These models are often hampered by significant computational costs and complex training procedures.<n>This work introduces a novel paradigm that uses surrogate models to create simpler, faster, and more flexible approximations of these dynamics.
  arXiv  Detail & Related papers  (2025-12-14T12:49:38Z)
• Transport Based Mean Flows for Generative Modeling [19.973366424307077]
  Flow-matching generative models have emerged as a powerful paradigm for continuous data generation.<n>These models suffer from slow inference due to the requirement of numerous sequential sampling steps.<n>Recent work has sought to accelerate inference by reducing the number of sampling steps.
  arXiv  Detail & Related papers  (2025-09-26T17:12:19Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step.<n>Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Local Flow Matching Generative Models [19.859984725284896]
  Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions.<n>We introduce a stepwise FM model called Local Flow Matching (LFM), which consecutively learns a sequence of FM sub-models.<n>We empirically demonstrate improved training efficiency and competitive generative performance of LFM compared to FM.
  arXiv  Detail & Related papers  (2024-10-03T14:53:10Z)
• Flow map matching with stochastic interpolants: A mathematical framework for consistency models [15.520853806024943]
  Flow Map Matching is a principled framework for learning the two-time flow map of an underlying generative model.<n>We show that FMM unifies and extends a broad class of existing approaches for fast sampling.
  arXiv  Detail & Related papers  (2024-06-11T17:41:26Z)
• Text-to-Image Rectified Flow as Plug-and-Play Priors [52.586838532560755]
  Rectified flow is a novel class of generative models that enforces a linear progression from the source to the target distribution.<n>We show that rectified flow approaches surpass in terms of generation quality and efficiency, requiring fewer inference steps.<n>Our method also displays competitive performance in image inversion and editing.
  arXiv  Detail & Related papers  (2024-06-05T14:02:31Z)
• Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
  We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech. Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T15:07:59Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)

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.