Distribution-Guided Classifier-Free Guidance

Classifier-free guidance (CFG) is central to conditional generation with diffusion models, but using a constant guidance strength overlooks how the diffusion rate, signal content, and score reliability vary throughout denoising. We introduce Distribution-Guided CFG (DG-CFG), a training-free schedule that reallocates a fixed guidance budget across timesteps to improve the balance among generation quality, diversity, and stability. A path-integral analysis of deterministic CFG identifies the temporal weight that governs how guidance accumulates along sampling trajectories. Combining this principle with signal-content weighting and error-motivated low-noise attenuation yields a middle-focused schedule that emphasizes informative and reliable stages of denoising. Controlled experiments with analytic scores support the path-integral predictions and illustrate the effect of temporal reweighting. Across different backbones and sampling methods, DG-CFG provides a favorable diversity–fidelity trade-off and improves generation quality under strong guidance while controlling saturation. Component ablations and sampling-budget evaluations further support the schedule design and robustness.