发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高分辨率图像和视频扩散模型推理成本高的问题，提出OmniCache框架，利用中间扩散特征冗余，通过多种缓存实现多维特征重用，减少推理延迟，在多模型上取得良好效果，且无需重新训练。
AI 中文摘要
高分辨率图像和视频扩散模型，如SD3、FLUX和近期的视频扩散Transformer，虽提升了生成质量，但推理时成本高，因多次评估注意力密集的去噪器。本文通过利用中间扩散特征的冗余来解决效率问题，而非改变模型权重或重新训练。识别出图像和视频生成中的四种互补冗余源，提出OmniCache框架，通过多种缓存实现多维特征重用。与平均匹配特征的基线不同，它用相似性匹配选择可缓存特征，跳过冗余计算并恢复一致激活。在多个模型上减少推理延迟，保持视觉保真度和运动连贯性。
英文摘要
High-resolution image and video diffusion models, including SD3, FLUX, and recent video diffusion transformers, have substantially improved generative quality but remain expensive at inference time because they repeatedly evaluate attention-heavy denoisers over many sampling steps. We address this inefficiency by exploiting redundancy in intermediate diffusion features rather than changing model weights or retraining. We identify four complementary redundancy sources in image and video generation: intra-frame, inter-frame, motion, and denoising-step redundancy. Based on this analysis, we propose OmniCache, a unified hierarchical caching framework that performs multidimensional feature reuse through Token Cache, Frame Cache, Block Cache, and Layered Cache. Unlike token-merging baselines that average matched features, OmniCache uses similarity matching to select cacheable features, skips redundant computation, and restores positionally consistent cached activations, preserving feature order and spatial-temporal structure. The resulting framework reuses spatial features in temporal layers and temporal features in spatial layers, while Layered Cache captures cross-step redundancy at the model-layer level. Across SD3, SVD-XT, and Latte, OmniCache reduces inference latency by up to 35%, 25%, and 28%, respectively, while maintaining visual fidelity and motion coherence in a training-free setting.
