Flow Straighter and Faster: Efficient One-Step Generative Modeling via MeanFlow on Rectified Trajectories
Flow-based generative models have recently demonstrated strong performance, yet sampling typically relies on expensive numerical integration of ordinary differential equations (ODEs). Rectified Flow enables one-step sampling by learning nearly straight probability paths, but achieving such straightness requires multiple computationally intensive reflow iterations. MeanFlow achieves one-step generation by directly modeling the average velocity over time; however, when trained on highly curved flows, it suffers from slow convergence and noisy supervision. To address these limitations, we propose Rectified MeanFlow, a framework that models the mean velocity field along the rectified trajectory using only a single reflow step. This eliminates the need for perfectly straightened trajectories while enabling efficient training. Furthermore, we introduce a simple yet effective truncation heuristic that aims to reduce residual curvature and further improve performance. Extensive 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. Code is available at https://github.com/Xinxi-Zhang/Re-MeanFlow.
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Xinxi Zhang
claimed_verified
2025-12-03T09:16:02.690Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1678814698406-noauth.jpeg
Xinxi Zhang
Xinxi-Zhang
user
Xinxi-Zhang
692f263e26742347f61daa41
Shiwei Tan
692f263e26742347f61daa42
Quang Nguyen
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Quan Dao
claimed_verified
2026-03-30T09:12:38.853Z
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Quan Dao
quandao10
user
quandao10
692f263e26742347f61daa44
Ligong Han
692f263e26742347f61daa45
Xiaoxiao He
692f263e26742347f61daa46
Tunyu Zhang
692f263e26742347f61daa47
Alen Mrdovic
692f263e26742347f61daa48
Dimitris Metaxas
Flow Straighter and Faster: Efficient One-Step Generative Modeling via MeanFlow on Rectified Trajectories
Flow-based generative models have recently demonstrated strong performance, yet sampling typically relies on expensive numerical integration of ordinary differential equations (ODEs). Rectified Flow enables one-step sampling by learning nearly straight probability paths, but achieving such straightness requires multiple computationally intensive reflow iterations. MeanFlow achieves one-step generation by directly modeling the average velocity over time; however, when trained on highly curved flows, it suffers from slow convergence and noisy supervision. To address these limitations, we propose Rectified MeanFlow, a framework that models the mean velocity field along the rectified trajectory using only a single reflow step. This eliminates the need for perfectly straightened trajectories while enabling efficient training. Furthermore, we introduce a simple yet effective truncation heuristic that aims to reduce residual curvature and further improve performance. Extensive 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. Code is available at https://github.com/Xinxi-Zhang/Re-MeanFlow.
Rectified MeanFlow enables efficient one-step sampling for flow-based generative models by modeling mean velocity fields with a single reflow step and a truncation heuristic, improving both sample quality and training efficiency.
flow-based generative models ordinary differential equations Rectified Flow MeanFlow one-step sampling probability paths mean velocity field reflow iterations sample quality training efficiency