When to Recompute: Error-Regulated Caching for Diffusion Transformers

Diffusion transformers have become the dominant backbone for high-fidelity image and video generation, yet their inference remains prohibitively expensive: each sample requires dozens of full network evaluations under strict temporal dependencies that preclude parallelization. Feature caching alleviates this cost by reusing or predicting intermediate features across denoising steps, but existing methods rely on fixed or heuristic caching schedules that are not explicitly regulated by the actual prediction error, providing limited capability to detect when cached predictions drift from the full-computation trajectory. As the acceleration ratio increases, such accumulated errors can eventually lead to severe quality degradation. We present Error-Regulated Adaptive Caching (ERAC), an acceleration framework that regulates cache reuse through an explicit error budget, adapting to the current sample and timestep while bounding trajectory drift. Our method organizes the denoising trajectory into computation segments that begin with a full evaluation and continue with cached predictions, while a lightweight, time-aware autoregressive estimator monitors the cumulative drift of the cached trajectory and triggers a full computation whenever the estimated error exceeds a prescribed threshold. Because the trigger adapts to individual samples and timesteps, computation is reallocated to the steps that actually require full evaluation, keeping expensive forward passes sparse while maintaining trajectory error within a controllable tolerance. On FLUX.1-dev, our method achieves an ImageReward above 0.9736 at a 6.04× acceleration, reducing the deviation from full evaluation by more than a factor of three compared with the strongest fixed-schedule baseline, while maintaining performance at 7.08× acceleration where such baselines break down. On Wan2.1 1.3B, ERAC achieves the best PSNR, LPIPS, and SSIM among the compared methods at speedups of up to 3.04×.