# Trajectory-Adaptive Classifier-Free Guidance

Classifier-free guidance for flow-matching models has applied one shared scale to all latent tokens, even though their velocity predictions evolve differently during sampling. TrajCFG maps historical changes in guided velocity direction to bounded per-token scales, giving stronger guidance to larger directional changes and weaker guidance to more consistent directions, and fuses adjacent-step with interval-normalized longer-span feedback without extra model evaluations. Across text-to-video, image-to-video, text-to-audio-video, text-to-image and image-to-3D generation it improves on base samplers; on text-to-video it raises VBench Total by 0.30 to 0.75 points over the strongest evaluated baseline on each of three backbones.

Classifier-free guidance for flow-matching models has applied one shared scale to all latent tokens, even though their velocity predictions evolve differently during sampling. TrajCFG maps historical changes in guided velocity direction to bounded per-token scales, giving stronger guidance to larger directional changes and weaker guidance to more consistent directions, and fuses adjacent-step with interval-normalized longer-span feedback without extra model evaluations. Across text-to-video, image-to-video, text-to-audio-video, text-to-image and image-to-3D generation it improves on base samplers; on text-to-video it raises VBench Total by 0.30 to 0.75 points over the strongest evaluated baseline on each of three backbones.