AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers

TL;DR

AdaCorrection enhances diffusion transformer inference by adaptively correcting cache reuse, maintaining high fidelity and efficiency without retraining or supervision.

Contribution

It introduces an adaptive offset cache correction framework that improves cache reuse accuracy during diffusion inference without additional training.

Findings

Maintains near-original FID scores with moderate acceleration.

Consistently improves generation performance on image and video benchmarks.

Achieves high fidelity with minimal computational overhead.

Abstract

Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive inference due to their iterative denoising structure. While prior methods accelerate sampling by caching intermediate features, they rely on static reuse schedules or coarse-grained heuristics, which often lead to temporal drift and cache misalignment that significantly degrade generation quality. We introduce \textbf{AdaCorrection}, an adaptive offset cache correction framework that maintains high generation fidelity while enabling efficient cache reuse across Transformer layers during diffusion inference. At each timestep, AdaCorrection estimates cache validity with lightweight spatio-temporal signals and adaptively blends cached and fresh activations. This correction is computed on-the-fly without additional supervision or retraining. Our approach achieves strong generation quality with minimal computational overhead, maintaining near-original FID while providing moderate acceleration. Experiments on image and video diffusion benchmarks show that AdaCorrection consistently improves generation performance.