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[2602.13357] AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers
Summary
The paper introduces AdaCorrection, a framework that enhances the efficiency of Diffusion Transformers by correcting cache misalignment, improving image and video generation quality while reducing computational costs.
Why It Matters
As Diffusion Transformers are at the forefront of generative AI, optimizing their performance is crucial for applications in computer vision. AdaCorrection addresses significant challenges in inference speed and output fidelity, making it relevant for researchers and practitioners in AI and machine learning.
Key Takeaways
- AdaCorrection improves cache reuse in Diffusion Transformers.
- The framework maintains high generation quality with minimal overhead.
- It adapts cache validity checks using spatio-temporal signals.
- Experimental results show consistent performance improvements.
- This approach does not require additional supervision or retraining.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13357 (cs) [Submitted on 13 Feb 2026] Title:AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers Authors:Dong Liu, Yanxuan Yu, Ben Lengerich, Ying Nian Wu View a PDF of the paper titled AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers, by Dong Liu and Yanxuan Yu and Ben Lengerich and Ying Nian Wu View PDF HTML (experimental) 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 ...
