VideoEditor-MoE：开源混合专家架构视频编辑模型，最高56B参数
VideoEditor-MoE: Scaling Complex Video Editing with Mixture of Experts
一篇讲视频编辑模型怎么用 MoE 省算力的论文：56B 参数但每 token 只激活 8B，还开源了框架，做视频生成方向可以看看路由分析。
arXiv 论文 VideoEditor-MoE 是首个面向视频编辑的开源混合专家框架。该模型从一个 5B 视频编辑模型初始化，扩展到 14B 和 56B 两种规模，每个 token 仅激活约 8B 参数以控制计算成本。架构上除稀疏 MLP-MoE 层外，MoE-Attention 通过路由的 key-value 专家扩展注意力容量。论文还与 Explicit-MoE 和 MLP-MoE 两个基线对比，并发现专家路由更多响应样本内容和扩散噪声水平，而非人工定义的编辑类别。
VideoEditor-MoE: Scaling Complex Video Editing with Mixture of Experts
Complex video editing requires understanding complex instructions while jointly fulfilling multiple editing requests. Meeting these demands through dense model scaling substantially increases computational cost. We introduce VideoEditor-MoE, the first open-source mixture-of-experts framework for video editing. Beyond sparse MLP-MoE layers, MoE-Attention expands attention capacity through routed key--value experts while preserving global interactions among video tokens. Initialized from a 5B video editing model, VideoEditor-MoE scales to 14B and 56B parameters, with approximately 8B parameters activated per token in both variants. Comparisons with existing methods and two complementary baselines, Explicit-MoE and MLP-MoE, assess its benefits over explicit expert guidance and MLP-only expert architectures. Further analyses demonstrate that all experts remain active and reveal that learned routing responds more strongly to sample content and diffusion noise levels than to human-defined editing categories. Spatial analysis further indicates sensitivity to object-level semantic features. We also identify decoupled weight decay as a strategy for stabilizing expert scaling.