视频生成模型 Video DeltaNet 提升直播视频生成效率

其线性分支引入的 Video Delta Attention (VDA) 每帧更新一次记忆，同时整合空间令牌。在 MiniMax H3 模型上应用后，VDN-H3 在八块 NVIDIA B200 GPU 上完成 14.

3 秒 768p 视频的去噪仅需 6.70 秒，比 50 步密集 H3 基准快 14.

5 倍。

Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation

Video diffusion models repeatedly process long spatiotemporal token sequences during denoising, making attention a major computational bottleneck. Linear attention offers an appealing alternative and has been widely adopted in recent large language models, but directly applying it to video models often fails to preserve the fine-grained interactions required for high-quality generation. We present Video DeltaNet (VDN), which combines local Softmax attention with bidirectional linear memory for long-range video context. Its linear branch introduces Video Delta Attention (VDA), which updates memory once per frame by jointly incorporating its spatial tokens. Separate output projections and learnable gates calibrate the two branches, while a staged teacher-alignment recipe progressively introduces the new pathway into pretrained models. We instantiate VDN on MiniMax H3, applying the hybrid to video-to-video interactions while retaining Softmax for interactions involving text or audio. With eight-step distillation and an optimized SGLang serving stack, VDN-H3 completes DiT denoising for a 14.3-second, 768p video in 6.70 seconds on eight NVIDIA B200 GPUs, corresponding to a 14.5x speedup over the 50-step dense H3 baseline on the same GPU count.

本条情报归属 AITOP「模型」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Video DeltaNet、MiniMax、视频生成、扩散模型，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Haocheng Xi, Yiming Xie, Hexu Zhao, Yiwen Zhang, Michael Liu, Thomas Creavin, Kurt Keutzer, Xiuyu Li, Zhaoyang Lv, Chenfeng Xu, Haiwen Feng），经 AITOP 多维评分管线结构化提炼。截至目前，全网已有 8 家独立信源对该事件进行交叉印证与跟进报道（含 NVIDIA AI、Gary Marcus、Tech in Asia 等），属于近期高关注度行业信号。