Accelerating Rectified Flow Models via Trajectory-Aware Caching

Diffusion and rectified flow (RF) models generate high-fidelity images and videos, but their iterative velocity-field evaluations are computationally expensive. Existing caching methods accelerate sampling by skipping timesteps, yet their coarse approximations introduce accumulated errors over long skip intervals and degrade quality under aggressive acceleration. We propose \textbf{TACache (Trajectory-Aware Cache)}, a training-free acceleration framework following a \textbf{skip-then-compensate} paradigm. TACache performs an orthogonal decomposition of discrete velocity acceleration along the RF trajectory into parallel and orthogonal components, isolating the magnitude and directional sources of per-step approximation error. The framework operates in two stages: \emph{offline}, cumulative variation thresholds on the magnitude and direction indicators yield the skipping schedule and bound how far each skipped interval may extend; \emph{online}, at each skipped step the offline statistics are combined with the sample's historical orthogonal direction to reconstruct the skipped velocity without additional forward passes. Experiments on BAGEL, FLUX.1-dev, and Wan2.1-1.3B show that TACache achieves up to $4.14\times$ speedup on text-to-image generation and $2.11\times$ speedup on text-to-video generation, with consistent improvements over prior cache-based methods on all reference-based fidelity metrics. Code will be released soon.