Mean Flows for One-step Generative Modeling

• URL: http://arxiv.org/abs/2505.13447v1
• Date: Mon, 19 May 2025 17:59:42 GMT
• Title: Mean Flows for One-step Generative Modeling
• Authors: Zhengyang Geng, Mingyang Deng, Xingjian Bai, J. Zico Kolter, Kaiming He,
• Abstract summary: We propose a principled and effective framework for one-step generative modeling.<n>A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training.<n>Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning.
• Score: 64.4997821467102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256x256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.

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