AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers

• URL: http://arxiv.org/abs/2602.13357v1
• Date: Fri, 13 Feb 2026 08:11:54 GMT
• Title: AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers
• Authors: Dong Liu, Yanxuan Yu, Ben Lengerich, Ying Nian Wu,
• Abstract summary: Transformer Diffusion (TDis) achieve state-of-the-art in high-fidelity and image generation but suffer from expensive inference due to their iterative denoising.<n>AdaCorrection is an adaptive offset cache correction framework that maintains high generation fidelity while enabling efficient reuse across cache layers during diffusion inference.<n>Our approach achieves strong generation quality with minimal computational overhead, maintaining near-original FID while providing moderate acceleration.
• Score: 37.38708392928324
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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