[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-qwen-image-2-1-turbo-8-step":3,"topics-all":38,"news-related-59d14de3-0e17-4202-8e7d-ad0bc51e3471":57},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":24,"news_slug":31,"published_at":32,"created_at":33,"modified_at":34,"is_published":35,"publish_type":36,"image_url":14,"view_count":37},"59d14de3-0e17-4202-8e7d-ad0bc51e3471","Qwen-Image-2.1-Turbo开源:8步去噪出图","阿里千问开源 Qwen-Image-2.1-Turbo:同为 7B 架构,40 步去噪压到 8 步,文生图与修图都支持,2K 输出、透明背景全保留。同日 API 上线,Turbo 每张 0.1 元。权重走 Qwen Research License,商用需授权;社区实测皮肤纹理过度锐化,画质待独立评测。","10 月 9 日,千问团队在 Qwen-Image-2.1 的 GitHub 仓库更新了两条消息:Turbo 检查点开放下载,Pro 与 Turbo 的 API 同步上线阿里云 Model Studio。距离基础版 2.1 开源(9 月 20 日)过去不到三周,迭代节奏明显在加快。\n\n## 40 步到 8 步,砍掉的是什么\n\nTurbo 沿用 2.1 的 7B 视觉生成架构,变化集中在采样效率:原版官方示例走 40 步去噪,Turbo 压到 8 步,步骤减少 80%。需要说明的是,步骤减少 80% 不等于实际耗时同比例缩短,但官方编辑示例在 2048×2048 分辨率下一次通过,文生图示例也支持到 1680×2512 的竖版输出。\n\n工程细节做得比较贴心:检查点自带推荐采样表,Diffusers 加载时自动应用;单独设置 num_inference_steps 不会覆盖这份内置调度,默认 CFG=1。也就是说,开发者不需要手动调参,拿到的就是官方标定的 8 步配置。\n\n能力面上,Turbo 保留了 2.1 的全部特性:2K 图像输出、文字指令修图、最多 10 张参考图、原生透明背景。生态支持也延续了下来——Diffusers、ComfyUI、vLLM-Omni、SGLang 在基础版发布时就已提供原生支持。\n\n## 本地能跑,商用要谈\n\n按阿里云百炼北京地域原价,Turbo 每张 0.1 元,Pro 版 0.25 元,Turbo 比 Pro 便宜 60%。如果不想碰 API,权重可以直接下载到本地跑;但要注意协议:Qwen-Image-2.1 整个仓库走的是 Qwen Research License,免费使用仅限非商业研究与评估,商业开发需要单独申请授权。「开权重」和「随便用」是两回事。\n\n社区反馈目前褒贬不一:有用户报告生成速度明显加快,也有人在 RTX 5090 上实测发现人物皮肤纹理过度锐化。目前还缺少足够的独立评测证明 Turbo 画质与原版完全相当,追求稳定的用户可以再等等。\n\n## 所以呢\n\nTurbo 的意义不只是一个加速检查点。图像生成的竞争正在从「谁画得好」转向「谁出图快且便宜」——当 API 定价压到一毛一张、本地 8 步出图时,电商批量出图、素材初稿这类落地场景的成本结构就变了。但 Research License 也提醒我们:看开源模型,先读协议再下权重。8 步和 40 步的画质差距到底多大,等独立评测说话。\n\n参考:[QwenLM\u002FQwen-Image-2.1(GitHub)](https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen-Image-2.1) · [Turbo 权重(HuggingFace)](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen-Image-2.1-Turbo) · [网易报道](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL8UQS3EP05566SCS.html)","https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen-Image-2.1","c36a21ac-2a77-421b-9519-1e150695732a",[11,15,18,21],{"id":12,"name":13,"slug":13,"description":14,"color":14},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":19,"name":20,"slug":20,"description":14,"color":14},"c187600e-804c-4697-b828-1e4330e0eb10","qwen",{"id":22,"name":23,"slug":23,"description":14,"color":14},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image",[25],{"id":26,"lang":27,"title":28,"summary":29,"content":30},"4f5d9567-338f-4642-8f93-93818fdda018","en","Qwen-Image-2.1-Turbo Open-Sourced: 8-Step Image Generation","Qwen ships Qwen-Image-2.1-Turbo: same 7B architecture, denoising cut from 40 steps to 8. APIs cost 0.1 RMB per image; weights stay research-licensed.","On October 9, the Qwen team updated the Qwen-Image-2.1 GitHub repository with two announcements: the Turbo checkpoint is available for download, and the Pro and Turbo APIs are now live on Alibaba Cloud Model Studio. That is less than three weeks after the base 2.1 open-weight release on September 20 — a notably fast cadence.\n\n## From 40 Steps to 8\n\nTurbo keeps the same 7B visual generation architecture as Qwen-Image-2.1; the change concentrates on sampling efficiency. The original model's official examples run 40 denoising steps, while Turbo cuts that to 8 — an 80% reduction in steps. Worth noting: an 80% cut in steps does not translate to an 80% cut in wall-clock time. Still, the official editing example completes in one pass at 2048×2048, and the text-to-image example supports portrait output up to 1680×2512.\n\nThe engineering details are considerate. The checkpoint ships with its recommended sampling schedule, which Diffusers loads automatically; setting `num_inference_steps` alone does not override the built-in schedule, and the default CFG is 1. In other words, developers get the officially calibrated 8-step configuration without manual tuning.\n\nCapability-wise, Turbo retains everything from 2.1: 2K image output, text-instruction editing, up to 10 reference images, and native transparent backgrounds. Ecosystem support carries over too — Diffusers, ComfyUI, vLLM-Omni, and SGLang have offered native support since the base release.\n\n## Runs Locally, But Read the License\n\nAt Alibaba Cloud Bailian's Beijing-region list price, Turbo costs 0.1 RMB per image and Pro costs 0.25 RMB — Turbo is 60% cheaper. If you would rather skip the API, the weights can be downloaded and run locally. But mind the license: the entire Qwen-Image-2.1 repository is under the Qwen Research License. Free use is limited to non-commercial research and evaluation; commercial development requires separate authorization. \"Open weights\" and \"use freely\" are two different things.\n\nCommunity feedback is mixed so far. Some users report clearly faster generation, while one tester on an RTX 5090 found that skin textures on generated people come out over-sharpened. There are not yet enough independent evaluations to confirm that Turbo's image quality matches the original — users who need stability may want to wait.\n\n## So What\n\nTurbo is more than an accelerated checkpoint. Competition in image generation is shifting from \"who draws best\" to \"who generates fastest and cheapest\" — once API pricing drops to about 0.1 RMB per image and local inference finishes in 8 steps, the economics of high-volume use cases like e-commerce batch imagery and draft assets change fundamentally. The Research License is also a reminder: when a model is announced as open-source, read the agreement before downloading the weights. As for how big the quality gap between 8 steps and 40 steps really is, let independent benchmarks answer that.\n\nReferences: [QwenLM\u002FQwen-Image-2.1 (GitHub)](https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen-Image-2.1) · [Turbo weights (HuggingFace)](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen-Image-2.1-Turbo) · [NetEase report](https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL8UQS3EP05566SCS.html)","qwen-image-2-1-turbo-8-step","2026-10-11T15:05:00Z","2026-10-11T15:10:46.237919Z","2026-10-11T15:10:46.237934Z",true,"agent",8,[39,48],{"slug":40,"tag_slug":40,"title_zh":41,"title_en":42,"intro_zh":43,"intro_en":44,"id":45,"is_active":35,"created_at":46,"modified_at":47},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":49,"tag_slug":49,"title_zh":50,"title_en":51,"intro_zh":52,"intro_en":53,"id":54,"is_active":35,"created_at":55,"modified_at":56},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":58},[59,64,69,74,79,84],{"id":60,"title":61,"news_slug":62,"published_at":63},"473ca44d-b179-4c19-a7a6-916dd8b90e4a","FLUX 3 Image 发布:画框控图,逐框编辑","flux-3-image-bounding-boxes","2026-10-02T23:06:31+00:00",{"id":65,"title":66,"news_slug":67,"published_at":68},"88a287ca-c7c4-4f31-aff9-8a5116d02e5e","Qwen-Image-2.1:7B 轻量模型把生成+编辑焊进一套权重","qwen-image-2-1-open-source-7b","2026-09-20T16:00:00+00:00",{"id":70,"title":71,"news_slug":72,"published_at":73},"c480d2d0-9156-4aa7-826f-fba2f252b6b7","蚂蚁开源 LLaDA-Image:6B 参数生成编辑一体,Turbo 版 4 步出图","ant-llada-image-open-generation-editing","2026-09-04T13:09:21+00:00",{"id":75,"title":76,"news_slug":77,"published_at":78},"6082cd23-0eca-40e0-9315-67318dc818ee","NovelAI Diffusion V5 发布:规模翻倍、32 通道 VAE,单次生成整页漫画","novelai-diffusion-v5-release","2026-08-22T13:10:00+00:00",{"id":80,"title":81,"news_slug":82,"published_at":83},"74464bd0-01e0-45a9-bc27-f94b1f5966a1","Qwen-Image-3.0 上线千问 AI 平台：0.18 元\u002F张起步、Pro 与 Standard 双档开放 API","qwen-image-3-0-qianwen-ai-platform-pricing","2026-08-05T03:00:00+00:00",{"id":85,"title":86,"news_slug":87,"published_at":88},"095917eb-02ae-4fd2-a1cb-17d0805442ee","微软 Mage-Flow 用 4B 跑赢 32B：原生分辨率 + 三件套协同设计把生成编辑都塞回单卡","microsoft-mage-flow-4b","2026-07-23T03:30:00+00:00"]