Terminal-Sensitivity-Aware Feature Caching for Efficient Diffusion Inference

Feature caching accelerates diffusion inference by reusing intermediate features across denoising steps, but existing adaptive methods typically make reuse decisions from estimated local denoiser-output differences. The same local difference can produce different terminal errors because its effect depends on the scheduler step size and the sensitivity of the remaining denoising trajectory. To account for these factors, we introduce Terminal Sensitivity Caching (TSenCache), a training-free method that estimates the terminal impact of each reuse decision. TSenCache guides reuse decisions by combining a sample-adaptive local-error estimate with the target scheduler step size and a calibrated sensitivity of the remaining trajectory. Keeping the scheduler step size separate also allows the calibrated sensitivity profiles to transfer across sampling settings through noise-level interpolation. Experiments on FLUX.1-dev and Wan2.1 demonstrate that TSenCache preserves higher reconstruction fidelity than the evaluated caching methods at comparable inference speed.