Improved Mean Flows: On the Challenges of Fastforward Generative Models

MeanFlow provides a principled framework for fastforward generative modeling. However, the original MeanFlow has key limitations in both the training objective and the guidance. First, the original MeanFlow prediction depends not only on the noisy state but also explicitly on the noise and data, causing the training target to drift with the network. We reformulate it as velocity prediction, predicting the instantaneous velocity solely from the noisy state and reducing it to the regression problem. Second, on the guidance side, the original MeanFlow fixes the guidance scale during training by directly learning a guided field, achieving 1-NFE sampling but losing the flexibility to adjust the guidance at inference. Instead, we condition the model on guidance scale and train it on a range of guidance scales, enabling flexible guidance as diffusion/flow models in inference while preserving one-step sampling. On ImageNet 256$\times$256, our improved MeanFlow (iMF) achieves a 1-step FID of 2.74 with a model of 118M parameters, and our largest model further pushes the 1-step FID to 1.72, establishing a new state of the art for one-step generative modeling.