Lookahead Drifting Model

This paper proposes a "lookahead drifting model" for distribution mapping, which enhances image generation performance via one-step neural functional evaluation. The model computes a set of drifting terms sequentially at each training iteration, utilizing positive samples and model outputs to capture higher-order gradient information.