Generative Modeling via Drifting

Generative modeling can be formulated as learning a mapping $f$ such that its pushforward distribution matches the data distribution. The pushforward behavior can be carried out iteratively at inference time, for example, in diffusion/flow-based models. In this paper, we propose *Drifting Models*, a generative modeling framework that evolves the pushforward distribution during training and naturally admits one-step inference. We introduce a drifting field that governs the sample movement and achieves equilibrium when the distributions match. This leads to a training objective that allows the neural network optimizer to evolve the distribution. In experiments, our one-step generator achieves state-of-the-art results on ImageNet 256$\times$256, with FID 1.54 in latent space and 1.61 in pixel space. We hope that our work opens up new opportunities for high-quality one-step generation.