AlphaFlow: Understanding and Improving MeanFlow Models
MeanFlow has recently emerged as a powerful framework for few-step generative
modeling trained from scratch, but its success is not yet fully understood. In
this work, we show that the MeanFlow objective naturally decomposes into two
parts: trajectory flow matching and trajectory consistency. Through gradient
analysis, we find that these terms are strongly negatively correlated, causing
optimization conflict and slow convergence. Motivated by these insights, we
introduce alpha-Flow, a broad family of objectives that unifies trajectory
flow matching, Shortcut Model, and MeanFlow under one formulation. By adopting
a curriculum strategy that smoothly anneals from trajectory flow matching to
MeanFlow, alpha-Flow disentangles the conflicting objectives, and achieves
better convergence. When trained from scratch on class-conditional ImageNet-1K
256x256 with vanilla DiT backbones, alpha-Flow consistently outperforms
MeanFlow across scales and settings. Our largest alpha-Flow-XL/2+ model
achieves new state-of-the-art results using vanilla DiT backbones, with FID
scores of 2.58 (1-NFE) and 2.15 (2-NFE).
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Huijie Zhang
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Aliaksandr Siarohin
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Willi Menapace
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Michael Vasilkovsky
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Sergey Tulyakov
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Qing Qu
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Ivan Skorokhodov
AlphaFlow: Understanding and Improving MeanFlow Models
MeanFlow has recently emerged as a powerful framework for few-step generative
modeling trained from scratch, but its success is not yet fully understood. In
this work, we show that the MeanFlow objective naturally decomposes into two
parts: trajectory flow matching and trajectory consistency. Through gradient
analysis, we find that these terms are strongly negatively correlated, causing
optimization conflict and slow convergence. Motivated by these insights, we
introduce alpha-Flow, a broad family of objectives that unifies trajectory
flow matching, Shortcut Model, and MeanFlow under one formulation. By adopting
a curriculum strategy that smoothly anneals from trajectory flow matching to
MeanFlow, alpha-Flow disentangles the conflicting objectives, and achieves
better convergence. When trained from scratch on class-conditional ImageNet-1K
256x256 with vanilla DiT backbones, alpha-Flow consistently outperforms
MeanFlow across scales and settings. Our largest alpha-Flow-XL/2+ model
achieves new state-of-the-art results using vanilla DiT backbones, with FID
scores of 2.58 (1-NFE) and 2.15 (2-NFE).
The $\alpha$-Flow framework improves few-step generative modeling by unifying and disentangling conflicting objectives, leading to better convergence and state-of-the-art performance on ImageNet-1K.
MeanFlow trajectory flow matching trajectory consistency gradient analysis $\alpha$-Flow Shortcut Model curriculum strategy class-conditional ImageNet-1K DiT backbones FID scores