Self-Drift: One-Step Generative Flows Learn from Their Own Representations

One-step flow models promise low-latency generation by compressing transport into a single network evaluation, but trajectory supervision alone can leave a mismatch between predicted endpoints and the data distribution. Existing distribution-correction methods commonly obtain their comparison geometry from an external teacher, encoder, or discriminator, so the feedback comes from a model separate from the flow being optimized. We introduce Self-Drift, a joint trajectory–distribution training scheme that closes this loop inside the flow itself. Our dual-horizon trajectory formulation pairs local transport learning with long-horizon prediction to a fixed clean endpoint, enabling the flow to compare predicted and real endpoints through its own representation and class-conditional feature memories. A frozen-lens semi-gradient routes a detached attraction–repulsion target through the endpoint, providing distribution feedback across the trajectory while preserving path-wise transport learning. Real-data-anchored, condition-number-capped whitening stabilizes the evolving feature space. Self-Drift learns this correction geometry in the same from-scratch process. The correction is self-contained and training-only: it needs no separately trained distribution-correction model and leaves the original one-step inference path unchanged. Self-Drift achieves a leading FID of 1.36 at NFE=1 among the listed from-scratch diffusion/flow methods on class-conditional ImageNet $256\times 256$.