AsymPipe: Accelerating Large-scale DiTs Image Editing via Asymmetry-Aware Pipeline Parallelism

Instruction-driven mask-free image editing with large-scale Diffusion Transformers (DiTs) has enabled high-fidelity local edits while maintaining global consistency. However, these models incur a prohibitive inference cost due to a pronounced semantic-–computation mismatch, where dense computation is performed over all high-resolution tokens regardless of localized editing instructions. Our analysis reveals that non-edit regions converge early with highly stable block-level outputs, exhibiting a distinct spatio-temporal asymmetry. Furthermore, we identify a critical system-level challenge that introducing such asymmetric computation into pipeline-parallel environments for ultra-large models triggers severe workload imbalance and pipeline bubbles, preventing algorithmic savings from translating into actual latency gains. To address these issues, we propose AsymPipe, a training--free algorithm–-system co-design framework that accelerates large-scale DiTs editing. The framework identifies edit versus non-edit regions on the fly using a zero-overhead velocity-based probe that reuses the predicted velocity field from the existing denoising process. For non-edit regions, AsymPipe caches and reuses block-level outputs, while for edit regions, it introduces a source-aware sparse attention module to precisely eliminate redundant contributions from static text and image sources. Finally, to bridge the gap between algorithmic optimization and hardware speedup, the framework incorporates a load-balanced token rearrangement scheme. This scheme deconstructs the original spatial topology to distribute tokens evenly across devices, physically restoring compute balance in pipeline-parallel systems. On HunyuanImage-3.0-Instruct and Qwen-Image-Edit-2511, AsymPipe achieves end-to-end speedups of 2.862x (PSNR 33.69) and 2.707x (PSNR 29.74), respectively, delivering substantial acceleration with virtually no loss in editing quality. Our anonymized source code is available at https://anonymous.4open.science/r/AsymPipe-6553.