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October 8, 2025Open Access

SplitMeanFlow: Interval Splitting Consistency in Few-Step Generative Modeling

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SplitMeanFlow establishes a new training framework for generative models that enhances efficiency and stability.
Utilizing an algebraic identity, it improves the average velocity field learning without differential operators.
The novel formulation results in significant speedups and simpler implementations over previous methods.
It has been successfully applied in large-scale speech synthesis, yielding a performance increase of 20x.

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This framework improves average velocity learning in generative models, indicating faster and more stable training.

Guo et al. (2025) studied this question.

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