按区域复杂度动态分配计算，让图像生成更快更省算力。
SDiT: Semantic Region-Adaptive for Diffusion Transformers
- 根据语义区域复杂度动态调整计算资源
- 无需重训练，最高提速3.0倍且质量几乎不变
- 适合追求高效生成的视觉任务开发者
扩散变换器（DiTs）在文本到图像生成中表现优异，但因去噪迭代和全局注意力的二次计算开销而效率较低。本文观察到：背景区域去噪快，边缘与纹理区变化更活跃。基于此，提出SDiT——一种语义区域自适应扩散变换器，根据区域复杂度分配计算。其采用无训练框架：(1) 快速Quickshift分割实现语义感知聚类，(2) 复杂度驱动的区域调度，仅更新关键区域，(3) 边界感知精修保持空间一致性。无需模型重训练或结构修改，最多实现3.0倍加速，同时保持与全注意力推理几乎相同的感知与语义质量。
原文摘要 · Abstract (English)
Diffusion Transformers (DiTs) achieve state-of-the-art performance in text-to-image synthesis but remain computationally expensive due to the iterative nature of denoising and the quadratic cost of global attention. In this work, we observe that denoising dynamics are spatially non-uniform-background regions converge rapidly while edges and textured areas evolve much more actively. Building on this insight, we propose SDiT, a Semantic Region-Adaptive Diffusion Transformer that allocates computation according to regional complexity. SDiT introduces a training-free framework combining (1) semantic-aware clustering via fast Quickshift-based segmentation, (2) complexity-driven regional scheduling to selectively update informative areas, and (3) boundary-aware refinement to maintain spatial coherence. Without any model retraining or architectural modification, SDiT achieves up to 3.0x acceleration while preserving nearly identical perceptual and semantic quality to full-attention inference.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。