DualDrift: Combining Forward and Reverse Drifts for One-Step Generative Modeling

Drifting Models have recently achieved state-of-the-art performance in one-step image generation by training generators to follow corrective vector fields. However, their standard reverse-style drift improves samples from the model side, which can severely underutilize training samples and bottleneck early convergence. To address this, we introduce DualDrift, a unified framework that augments the original reverse drift with a novel forward drift. The forward drift lets each data sample assign correction to generated rollouts, providing denser data-side signals and accelerating early training. At the same time, this dense data-side signal can overemphasize dominant regions when used alone. DualDrift therefore uses a parameter-free scheduler driven by empirical batch statistics to balance the fast early correction of forward drift with the stable refinement of reverse drift. Evaluated on ImageNet $256\times256$, DualDrift achieves faster convergence and significantly improves over the baseline Drifting Models in both FID and Inception Score.