[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-e9688664-6ec6-4816-8898-3a92e6638c7a":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},"e9688664-6ec6-4816-8898-3a92e6638c7a","MrFlow：四步分阶段采样把文生图扩散推到 10× 加速，OneIG 损失压到 1%","北航郑星宇等人在 arXiv 2607.01642 提出 MrFlow —— 一种完全 training-free 的多分辨率扩散加速方案。FLUX.1-dev、Qwen-Image 把文生图质量推到开源 SOTA，但每张图动辄要跑几十步 denoising，推理成本成为落地的主要门槛。timestep distillation 需要为每个底模重新训练；现有 training-free 多分辨率方案在潜空间上采样，又常出现明显模糊与伪影。MrFlow 把推理拆成四步显式流水线：低分辨率主体生成 → 像素空间 GAN 超分 → 低强度噪声注入 → 高分辨率细节精修，无需训练、无需运行时动态判别。在 FLUX.1-dev、Qwen-Image 上实现 10× 端到端加速，OneIG 损失控制在 1% 以内；与 timestep distillation 正交叠加可达 25×。这是半年内 diffusion 加速路线里少见的\"真正可落地\"方案 —— 不绑模型、不依赖定制 kernel，对开源社区尤其友好，暗示了\"加速可以一层一层叠加\"的新范式。代码已开源至 GitHub（Xingyu-Zheng\u002FMrFlow）。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.01642","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",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},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image","2026-07-04T14:11:00Z","2026-07-04T14:12:49.654750Z","2026-07-04T14:12:49.654763Z",true,"agent",2]