[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-wavespeed-diffusion-vs-ar-video-2026":3,"news-related-592c40ec-97f9-4b49-92f9-4dd417199459":36},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"592c40ec-97f9-4b49-92f9-4dd417199459","扩散模型 vs 自回归：视频生成架构的 2026 路线之争","2026 年，视频生成模型的技术路线分化得愈发清晰。一边是以 Sora 2、Veo 3、Kling 为代表的扩散变换器（DiT）阵营，另一边是以 Pyramid Flow 为代表的自回归（AR）路线。两条路线的竞争，本质上是两种截然不同的「如何生成一帧」的哲学。\n\nDiT 架构在 2026 年处于绝对主导地位。这个技术路径的核心是将视频切分成时空 patches（类似大语言模型处理 token 的方式），然后通过 transformer 去噪生成。2023 年 Peebles & Xie 的论文奠定了这个方向的基础，几乎所有主流商业视频模型都在 DiT 基础上演进。\n\nDiT 的优势在于生成质量高、长视频一致性相对可控。但瓶颈同样明显：二次方注意力复杂度导致生成时间随时长急剧增长，长程时间一致性仍是痛点。\n\n自回归路线（AR）的核心是逐帧生成，下一帧 conditioning 上一帧。Pyramid Flow 用了金字塔式流匹配来优化，可以在 10 秒视频上做到不错的一致性。AR 的理论优势是长视频下的一致性天花板更高，但实践中错误会累积，推理速度慢到无法接受。\n\n两条路线都在互相借鉴。DiT 模型在加入更长时间建模模块，AR 模型在借助 diffusion 的训练技巧。从工程角度，DiT 仍会是商业落地的主流选择。对于开发者来说，Kling、Seedance、Veo 3 都是 DiT，选择哪个取决于场景：追求一致性选 Veo 3，追求速度选 Kling，追求开源选 Wan\u002FCogVideoX。AR 路线短期更适合作为补充研究，而不是生产选型。","https:\u002F\u002Fwavespeed.ai\u002Fblog\u002Fposts\u002Fai-video-generation-models-2026\u002F","90791fcd-1d9f-4f06-a676-0673fd491bce",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":21,"name":22,"slug":22,"description":13,"color":13},"ebe5dcd1-46b1-4298-b8c2-8e0e2f456e56","video-generation",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"97c604d1-667b-43e7-af2c-b93dcb52427a","en","Diffusion versus autoregression: video generation's 2026 fork","Wavespeed AI published an analysis of the 2026 architectural battle between diffusion and autoregressive video generation. The two paradigms have different strengths — diffusion is better for high visual quality, autoregressive is better for long and streaming video. The article argues that the \"winning\" architecture will depend on the use case, not on a single benchmark.","wavespeed-diffusion-vs-ar-video-2026","2026-06-01T22:05:00Z","2026-06-01T22:05:49.330872Z","2026-08-19T02:08:40.142862Z",true,"agent",144,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"2f01f1ec-b078-4aca-afa2-654dc48cc784","Video-Mirai：自回归视频扩散的「远见」机制，零推理成本打破长程漂移","video-mirai-foresight-ar-diffusion-zero-cost","2026-06-08T12:15:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"18d2aa73-7244-4b10-b611-46475e17327e","ForgeWM开源:一步去噪72FPS的可玩世界模型,8张卡复现全流程","forgewm-few-step-playable-world-model","2026-08-24T21:10:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"a818c807-2131-4950-8f51-62847a57db41","VideoRAE 把 frozen 视频基础模型改造成生成器 latent:UCF-101 gFVD 40\u002F93,收敛提速 5×","videorae-frozen-video-generator","2026-07-20T04:15:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"b5909ee4-586c-494a-9353-4d10dee93227","Reward Lightning:把「打分器」和「蒸馏器」焊进同一根骨干,1-4 步视频生成的同源解法","reward-lightning-video","2026-07-20T00:15:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"0599b775-ac17-49d2-aebd-a16f531c7168","腾讯混元 MeanFlowNFT：把 RL 接进「平均速度生成器」，Wan 2.1 4 步反超 50 步 LongCat-Video RL","tencent-hunyuan-meanflownft","2026-07-16T12:00:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"7c769930-c404-4ef6-a7c2-29d45d8209d2","腾讯混元 MixGRPO 入选 ECCV 2026：滑动窗口把 Flow-GRPO 训练开销砍到三成","tencent-mixgrpo-flow-grpo","2026-07-06T22:09:00+00:00"]