Trajectory-Consistent Calibration for Cache-Accelerated Diffusion Models

Diffusion Transformers require repeated denoiser evaluations during iterative sampling, making inference computationally expensive. Cache-based acceleration reduces this cost by reusing intermediate representations across denoising steps, but the resulting cache-side features can deviate from those produced by fresh computation and degrade generation quality. We identify two coupled calibration challenges. First, a reused or approximately computed feature can differ from its full-computation counterpart at the current calibration site. Second, applying an earlier correction changes the subsequent cache-side trajectory, so a later calibration operator may encounter a feature distribution different from the one used to fit it. We refer to these challenges as direct cache mismatch and trajectory-induced calibration mismatch, respectively. To address them, we propose Trajectory-Consistent Calibration (TCC), which fits closed-form, site-specific calibration operators from paired cache-side and full-computation features without modifying the denoiser parameters or the base cache strategy. Rather than fitting all operators on a single uncorrected cache trajectory, TCC processes selected denoising timesteps sequentially: it probes cache-side features, fits the corresponding operators, and replays and commits the calibrated step before estimating any later operators. Experiments on PixArt-α and DiT-XL/2 report lower FID values across the evaluated cache-based acceleration settings. In a representative PixArt-α setting based on FORA, TCC reduces FID from 29.83 to 27.35, slightly surpassing the corresponding full-computation baseline in terms of FID.