Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling
This work addresses the need for stable and scalable one-step generative modeling, offering a flexible approach that generalizes existing methods, though it appears incremental in nature.
The paper tackles the problem of generating high-quality data samples efficiently in one step, introducing Modular MeanFlow (MMF) which achieves competitive sample quality and robust convergence across tasks like image synthesis and trajectory modeling.
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.