Flow-Coupled Drifting for One-Step Generation

One-step generation produces samples with a single network evaluation, but training high-quality one-step generators remains challenging. Drifting Models train such generators with attraction and repulsion fields, yet estimating these fields requires many real and generated samples per condition and therefore large batches. With only one real sample, the positive, and one other generated sample, the negative, the normalized field reduces to the positive–negative difference, which ignores where the current output lies relative to the positive and the negative. We propose Flow-Coupled Drifting (FCD) to make such single-pair training effective. First, we scale the attraction and repulsion displacements separately according to their distances from the current output. The resulting force depends on the output, and its expectation is proportional to the formal 2-Wasserstein gradient-flow velocity of a smoothed energy distance in pretrained feature spaces. Second, to pair each generator input with its own positive, we form two inputs by interpolating the same positive with independent Gaussian noises at a shared time, and the generator maps them to the output and the negative. We also train at the pure-noise endpoint used for one-step inference. On class-conditional ImageNet $256\times 256$, FCD adapts pretrained SiT models into one-step generators, achieves an FID of 1.38, and its performance improves consistently with model size. Ablations show that flow-coupled training accelerates convergence and remains effective with a batch size of 16. The code will be made publicly available.