P-Guide: Parameter-Efficient Prior Steering for Single-Pass CFG Inference

Classifier-Free Guidance (CFG) is widely used for conditional generation in flow matching, yet its standard dual-pass form requires two backbone evaluations at each sampling step. We introduce P-Guide, a framework that applies guidance to the mean of a learned initial latent distribution and then uses one backbone evaluation per step. The resulting prior shift induces a local first-order trajectory response without explicit velocity-field extrapolation during sampling. We consider both homoscedastic and heteroscedastic priors; their comparable ImageNet performance indicates that learning the variance is secondary to mean steering in this setting. Experiments on class-conditional generation show that P-Guide reduces guided sampling GFLOPs by approximately 50% at the same step count while adding only a 1.31M-parameter prior module; on ImageNet, prior guidance reduces FID from 27.78 to 22.33 (U-Net) and from 32.84 to 24.61 (DiT-B/2) while remaining single-pass, offering a cost-effective balance between sampling efficiency and generation quality at moderate guidance scales.