Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling

• URL: http://arxiv.org/abs/2508.17426v1
• Date: Sun, 24 Aug 2025 16:00:08 GMT
• Title: Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling
• Authors: Haochen You, Baojing Liu, Hongyang He,
• Abstract summary: One-step generative modeling seeks to generate high-quality data samples in a single function evaluation.<n>In this work, we introduce Modular MeanFlow, a flexible and theoretically grounded approach for learning time-averaged velocity fields.
• Score: 0.07646713951724012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One-step generative modeling seeks to generate high-quality data samples in a single function evaluation, significantly improving efficiency over traditional diffusion or flow-based models. In this work, we introduce Modular MeanFlow (MMF), a flexible and theoretically grounded approach for learning time-averaged velocity fields. Our method derives a family of loss functions based on a differential identity linking instantaneous and average velocities, and incorporates a gradient modulation mechanism that enables stable training without sacrificing expressiveness. We further propose a curriculum-style warmup schedule to smoothly transition from coarse supervision to fully differentiable training. The MMF formulation unifies and generalizes existing consistency-based and flow-matching methods, while avoiding expensive higher-order derivatives. Empirical results across image synthesis and trajectory modeling tasks demonstrate that MMF achieves competitive sample quality, robust convergence, and strong generalization, particularly under low-data or out-of-distribution settings.

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