Friction-Augmented Drifting Models for Resource-Efficient Domain Translation

Excerpt

arXiv:2604.18194v2 Announce Type: replace Abstract: Single-step generators promise high-fidelity synthesis at a fraction of the inference and training cost of ordinary differential equation (ODE)-based flow models, a central concern when compute is limited. Drifting Models (DMs) train a one-step generator by evolving samples under a kernel-based drift field, avoiding ODE integration entirely, but a two-particle surrogate of their iteration admits a \emph{locally repulsive} regime in which repuls