AdaCorrection: Adaptive Offset Cache Correction for Efficient Diffusion Transformers
The recently introduced AdaCorrection framework seeks to enhance the performance of Diffusion Transformers (DiTs) for generating high-quality images and videos. While DiTs are recognized for their cutting-edge capabilities, they face challenges with costly inference linked to iterative denoising processes. Previous acceleration techniques that relied on static cache reuse or broad heuristics often resulted in temporal drift and misaligned caches, compromising quality. AdaCorrection offers an innovative adaptive offset cache correction, which preserves generation quality while facilitating efficient cache reuse among Transformer layers. It evaluates cache validity at each timestep using lightweight spatio-temporal signals and seamlessly integrates cached and new activations without extra supervision or retraining. This method delivers excellent generation quality with minimal computational cost. The research can be found on arXiv with the identifier 2602.13357, categorized as 'replace-cross'.
Key facts
- AdaCorrection is an adaptive offset cache correction framework for Diffusion Transformers.
- It addresses expensive inference in DiTs caused by iterative denoising.
- Prior methods using static reuse schedules or coarse-grained heuristics lead to temporal drift and cache misalignment.
- AdaCorrection estimates cache validity with lightweight spatio-temporal signals at each timestep.
- It adaptively blends cached and fresh activations.
- The correction is computed on-the-fly without additional supervision or retraining.
- The approach achieves strong generation quality with minimal computational overhead.
- The paper is available on arXiv (2602.13357) with announcement type 'replace-cross'.
Entities
Institutions
- arXiv