Core Viewpoint - The article discusses a new generative modeling framework called MeanFlow, which significantly improves existing flow matching methods by introducing the concept of average velocity, achieving a FID score of 3.43 on the ImageNet 256×256 dataset without the need for pre-training, distillation, or curriculum learning [3][5][7]. Methodology - MeanFlow introduces a new ground-truth field representing average velocity instead of the commonly used instantaneous velocity in flow matching [3][8]. - The average velocity is defined as the displacement over a time interval, and the relationship between average and instantaneous velocity is derived to guide network training [9][10]. Performance Results - MeanFlow demonstrates strong performance in one-step generative modeling, achieving a FID score of 3.43 with only 1-NFE, which is a 50% improvement over the best previous methods [5][16]. - In 2-NFE generation, MeanFlow achieves a FID score of 2.20, comparable to leading multi-step diffusion/flow models [18]. Comparative Analysis - The article provides a comparative analysis of MeanFlow against previous single-step diffusion/flow models, showing that MeanFlow outperforms them significantly, with a FID score of 3.43 compared to 7.77 for IMM [16][17]. - The results indicate that the proposed method effectively narrows the gap between single-step and multi-step diffusion/flow models [18].