Mean Flows for One-step Generative Modeling

• URL: http://arxiv.org/abs/2505.13447v1
• Date: Mon, 19 May 2025 17:59:42 GMT
• Title: Mean Flows for One-step Generative Modeling
• Abstract summary: We propose a principled and effective framework for one-step generative modeling.<n>A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training.<n>Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning.
• Score: 64.4997821467102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256x256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.

Related papers

• One-Step Generative Modeling via Wasserstein Gradient Flows [56.266175641614986]
  W-Flow is a framework for training a generator that transforms samples from a simple reference distribution into samples from a target data distribution in a single step.<n>We set a new state of the art for one-step ImageNet 256$times$256 generation, achieving 1.29 FID, with improved mode coverage and domain transfer.
  arXiv  Detail & Related papers  (2026-05-12T08:29:44Z)
• Discrete Meanflow Training Curriculum [11.664282029505353]
  One-step generative models can produce high quality image samples but can be difficult to optimize.<n>Meanflow models exhibit excellent few-step sampling performance and tantalizing one-step sampling performance.
  arXiv  Detail & Related papers  (2026-04-10T00:25:44Z)
• Flow Straighter and Faster: Efficient One-Step Generative Modeling via MeanFlow on Rectified Trajectories [14.36205662558203]
  Rectified MeanFlow is a framework that models the mean velocity field along the rectified trajectory using only a single reflow step.<n>Experiments on ImageNet at 64, 256, and 512 resolutions show that Re-MeanFlow consistently outperforms prior one-step flow distillation and Rectified Flow methods in both sample quality and training efficiency.
  arXiv  Detail & Related papers  (2025-11-28T16:50:08Z)
• Understanding, Accelerating, and Improving MeanFlow Training [64.84964628592418]
  MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields.<n>We analyze the interaction between the two velocities and find: (i) well-established instantaneous velocity is a prerequisite for learning average velocity.<n>We design an effective training scheme that accelerates the formation of instantaneous velocity, then shifts emphasis from short to long-interval average velocity.
  arXiv  Detail & Related papers  (2025-11-24T12:59:27Z)
• Decoupled MeanFlow: Turning Flow Models into Flow Maps for Accelerated Sampling [68.76215229126886]
  We introduce Decoupled MeanFlow, a simple decoding strategy that converts flow models into flow map models without architectural modifications.<n>Our method conditions the final blocks of diffusion transformers on the subsequent timestep, allowing pretrained flow models to be directly repurposed as flow maps.<n>On ImageNet 256x256 and 512x512, our models attain 1-step FID of 2.16 and 2.12, respectively, surpassing prior art by a large margin.
  arXiv  Detail & Related papers  (2025-10-28T14:43:48Z)
• Flow-Anchored Consistency Models [32.04797599813587]
  Continuous-time Consistency Models (CMs) promise efficient few-step generation but face challenges with training instability.<n>We argue this instability stems from a fundamental conflict: by training a network to learn only a shortcut across a probability flow, the model loses its grasp on the instantaneous velocity field that defines the flow.<n>We introduce the Flow-Anchored Consistency Model (FACM), a simple but effective training strategy that uses a Flow Matching task as an anchor for the primary CM shortcut objective.
  arXiv  Detail & Related papers  (2025-07-04T17:56:51Z)
• Align Your Flow: Scaling Continuous-Time Flow Map Distillation [63.927438959502226]
  Flow maps connect any two noise levels in a single step and remain effective across all step counts.<n>We extensively validate our flow map models, called Align Your Flow, on challenging image generation benchmarks.<n>We show text-to-image flow map models that outperform all existing non-adversarially trained few-step samplers in text-conditioned synthesis.
  arXiv  Detail & Related papers  (2025-06-17T15:06:07Z)
• LeDiFlow: Learned Distribution-guided Flow Matching to Accelerate Image Generation [1.1847464266302488]
  Flow Matching (FM) is a powerful generative modeling paradigm based on a simulation-free training objective instead of a score-based one used in DMs.<n>We present Learned Distribution-guided Flow Matching (LeDiFlow), a novel scalable method for training FM-based image generation models.<n>Our method utilizes a State-Of-The-Art (SOTA) transformer architecture combined with latent space sampling and can be trained on a consumer workstation.
  arXiv  Detail & Related papers  (2025-05-27T05:07:37Z)
• Integration Flow Models [8.402025338436696]
  Integration Flow learns the integral of ODE-based trajectory paths without solving the ODE functions.<n>It is the first model with a unified structure to estimate ODE-based generative models.<n>It achieves one-step generation on CIFAR10 with FIDs of 2.86 for the Variance Exploding (VE) diffusion model, 3.36 for rectified flow without reflow, and 2.91 for PFGM++.
  arXiv  Detail & Related papers  (2025-04-28T18:29:15Z)
• OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs [20.652907645817713]
  OFTSR is a flow-based framework for one-step image super-resolution that can produce outputs with tunable levels of fidelity and realism.<n>We demonstrate that OFTSR achieves state-of-the-art performance for one-step image super-resolution, while having the ability to flexibly tune the fidelity-realism trade-off.
  arXiv  Detail & Related papers  (2024-12-12T17:14:58Z)
• FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner [70.90505084288057]
  Flow-based models tend to produce a straighter sampling trajectory during the sampling process. We introduce several techniques including a pseudo corrector and sample-aware compilation to further reduce inference time. FlowTurbo reaches an FID of 2.12 on ImageNet with 100 (ms / img) and FID of 3.93 with 38 (ms / img)
  arXiv  Detail & Related papers  (2024-09-26T17:59:51Z)
• Improving Consistency Models with Generator-Augmented Flows [16.049476783301724]
  Consistency models imitate the multi-step sampling of score-based diffusion in a single forward pass of a neural network.<n>They can be learned in two ways: consistency distillation and consistency training.<n>We propose a novel flow that transports noisy data towards their corresponding outputs derived from a consistency model.
  arXiv  Detail & Related papers  (2024-06-13T20:22:38Z)
• A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization [54.113083217869516]
  In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
  arXiv  Detail & Related papers  (2023-12-24T15:37:47Z)
• 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)
• Fast Sampling of Diffusion Models via Operator Learning [74.37531458470086]
  We use neural operators, an efficient method to solve the probability flow differential equations, to accelerate the sampling process of diffusion models. Compared to other fast sampling methods that have a sequential nature, we are the first to propose a parallel decoding method. We show our method achieves state-of-the-art FID of 3.78 for CIFAR-10 and 7.83 for ImageNet-64 in the one-model-evaluation setting.
  arXiv  Detail & Related papers  (2022-11-24T07:30:27Z)

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.