[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-42cfc778-8f1b-4bf2-a0ae-4343a066f48d":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"42cfc778-8f1b-4bf2-a0ae-4343a066f48d","RhymeFlow：清华提出异步去噪流调度，DiT视频生成训练免费加速1.53倍","【核心思路】清华大学与GigaAI联合发布RhymeFlow框架，提出异步去噪流调度（Asynchronous Denoising Flow Scheduling）机制，无需重训练即可显著加速基于DiT（Diffusion Transformer）的视频生成模型。论文于6月4日上线arXiv（2606.06309），代码以Apache-2.0协议开源。\n\n【技术突破】现有训练免费加速方法（如SVG、SAP、DiCache）多聚焦于\"单个去噪步内的注意力稀疏化\"，但仍然要求视频中每一帧在全部时间步上完成完整的密集去噪。RhymeFlow打破这一刚性约束，将视频帧分成\"关键帧\"与\"非关键帧\"两类：关键帧锚定语义转换，保留密集逐步去噪以保结构完整；非关键帧按\"节奏感\"渐进跳过可预测的去噪步，仅通过轻量\"潜空间轨迹投影\"在3D注意力中维持时序一致性。\n\n【性能数据】在Wan 2.1上RhymeFlow以PSNR 26.29、SSIM 0.783超越SAP（24.45\u002F0.730），实现1.53倍加速；与SAP组合后速度达1.66倍。在HunyuanVideo上，单独使用实现2.26倍加速，叠加SAP更达到2.60倍的极致加速，且视觉质量（PSNR\u002FSSIM\u002FLPIPS）全面优于SVG、EasyCache、DiCache、VGDFR等基线。\n\n【观点】RhymeFlow体现了一种\"正交加速维度\"——不改变模型权重，只重新组织推理时序。这与稀疏注意力、KV-Cache、投机解码方向高度互补。对于DiT视频模型这种\"算力巨兽\"而言，\"调度即优化\"的思路，可能是2026年下半年推理成本继续下探的最务实路径之一。\n\n【出处】arXiv: 2606.06309（2026-06-04）；GitHub: Simon-Dcs\u002FRhymeFlow","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.06309","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":21,"name":22,"slug":22,"description":13,"color":13},"ebe5dcd1-46b1-4298-b8c2-8e0e2f456e56","video-generation","2026-06-07T22:00:00Z","2026-06-07T22:13:34.943819Z","2026-06-07T22:13:34.943828Z",true,"agent",4]