Alibaba open-sources Qwen-Image-2.1, a 7B image model with unified generation and editing
Alibaba's Qwen team open-sourced Qwen-Image-2.1, a 7B-parameter image generation model that unifies text-to-image and image editing in a single pipeline. It natively supports transparent image generation and accepts up to ten reference images, with enhanced local editing and portrait/product fidelity. According to the team, it scores 60.28 on the Qwen-Image-Bench, the highest among open-source models and ahead of Nano Banana 2.0 (59.82) and GPT Image 1.5 (59.65).
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编辑|泽南 AI 写代码的能力经常能让开发者「双手离开键盘」,但对于设计师而言,AI 生成的图片距离能交稿总是差了好几步。 在图像设计的工作流中,人物需要抠图、素材需要改字、各种商品的摆放要时不时调整,但包装上的字、瓶身的形状不能跟着变。如果需求不停调整,还得继续修改局部,让整张图保持协调。 这些细节决定了 AI 生图能否进入真正的创作流程。对设计师、电商运营和内容创作者来说,如今图像模型的瓶颈,在于每一次提出的修改指令能否准确完成。 本周日,阿里千问 正式开源图片生成模型 Qwen-Image-2.1 ,以小模型的体量解决了大部分生产力难题:它实现了文生图与图像编辑一体化,原生支持透明图生成,最高可以接收 10 张参考图,还进一步强化了局部编辑、人像与商品保真。 它成为了千问图像系列当前最平衡、性价比最高开源生图模型。公开评测结果显示,Qwen-Image-2.1 取得开源模型第一,得分甚至超过了 Nano Banana 2.0。要知道,这个模型的视觉生成部分参数量仅为 7B。 在 X 等平台上,已经有提前获得体验资格的用户放出了生成结果,大家一阵好评。有包含大量文字内容的效果图: 生成出真实照片质感图片的: 也有尝试各种不同滤镜、打光风格的: 人们认为 Qwen-Image-2.1 在指令遵循、抽卡成功率以及实际效果上都令人印象深刻。基于新模型的能力,我们已经可以串起素材生成、组合与修改的工作流,用 AI 来解决很多复杂的修图问题。 能力与效率 据介绍, Qwen-Image-2.1 的视觉生成部分采用 20 层 Single-Stream DiT,参数量为 7B,原生支持 2K 。该模型在评测基准中实现了超越体量的水平:Qwen-Image-2.1 的总分为 60.28。相比之下,Nano Banana 2.0 为 59.82,GPT Image 1.5 为 59.65。它已经能够与多款闭源图像模型同场竞争。 Qwen-Image-Bench 评测图表。 视觉生成部分仅有 7B 参数,也让这样的能力水平更值得关注:其在较小的生成模块中同时容纳了生图、透明素材生成与多图编辑能力(生成和编辑统一管线),为开发者部署和定制图像工具提供了更轻量的起点。 性能不错的同时,Qwen-Image-2.1 还对模型的推理过程进行了优化,核心思路是减少不必要的重复计算。 在一次

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Qwen : Alibaba releases Qwen-Image-2.1, a 7B open-weight model it says outperforms most closed-source models, with native transparency and up to ten reference images — · QwenTeam丨Translations:.体中文 — We are excited to open-source Qwen-Image-2.1 …

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