Generative Modeling via Drifting

This paper discusses Drifting Models, a novel generative modeling paradigm that enables high-quality, one-step image generation without the iterative inference required by diffusion or flow-matching models. Instead of decomposing transformations at the sampling stage, this method evolves a pushforward distribution during the training process by utilizing a neural network optimizer. The core mechanism is a drifting field governed by an anti-symmetric property, which uses positive data samples for attraction and generated negative samples for repulsion to achieve a state of equilibrium.This approach minimizes a training-time loss based on the movement of samples, effectively shifting the iterative complexity from the user's inference phase to the model's optimization phase. To handle high-dimensional data like images, the researchers implement the drifting loss within a multi-scale feature space using self-supervised encoders such as latent-MAE. Their results demonstrate state-of-the-art performance on ImageNet 256×256, achieving superior FID scores in both latent and pixel spaces. Furthermore, the model's versatility is highlighted by its success in robotic control tasks, where it matches or exceeds the performance of traditional multi-step diffusion policies.