Diffusion Transformer (DiT) models have achieved unprecedented quality in image and video generation, yet their iterative sampling process remains computationally prohibitive. To accelerate inference, feature caching methods have emerged by reusing intermediate representations across timesteps. However, existing caching approaches treat all feature components uniformly. We reveal that DiT feature spaces contain distinct principal and residual subspaces with divergent temporal behavior: the principal subspace evolves smoothly and predictably, while the residual subspace exhibits volatile, low-energy oscillations that resist accurate prediction. Building on this insight, we propose SVD-Cache, a subspace-aware caching framework that decomposes diffusion features via Singular Value Decomposition (SVD), applies exponential moving average (EMA) prediction to the dominant low-rank components, and directly reuses the residual subspace. Extensive experiments demonstrate that SVD-Cache achieves near-lossless across diverse models and methods, including 5.55$\times$ speedup on FLUX and HunyuanVideo, and compatibility with model acceleration techniques including distillation, quantization and sparse attention. Our code is in supplementary material and will be released on Github.

翻译：扩散Transformer（DiT）模型在图像和视频生成领域取得了前所未有的质量，但其迭代采样过程在计算上仍然代价高昂。为加速推理，特征缓存方法应运而生，通过在时间步间复用中间表示。然而，现有的缓存方法对所有特征分量进行统一处理。我们发现，DiT特征空间包含具有不同时序行为的主成分子空间和残差子空间：主成分子空间平滑且可预测地演化，而残差子空间则表现出波动剧烈、能量较低且难以准确预测的振荡。基于这一洞察，我们提出了SVD-Cache，一个子空间感知的缓存框架。该框架通过奇异值分解（SVD）分解扩散特征，对主导的低秩分量应用指数移动平均（EMA）预测，并直接复用残差子空间。大量实验表明，SVD-Cache在包括FLUX和HunyuanVideo在内的多种模型和方法上实现了接近无损的加速效果（例如5.55倍的加速），并且与包括蒸馏、量化和稀疏注意力在内的模型加速技术兼容。我们的代码位于补充材料中，并将在Github上发布。