比Nano Banana还夯的生图模型泄露，截图不再是证据了 | 附提示词

marsbit2026-04-19 tarihinde yayınlandı2026-04-19 tarihinde güncellendi

Özet

近日，LM Arena评测平台短暂出现了三个匿名图像模型，据推测为OpenAI尚未正式发布的GPT Image 2。该模型在文字渲染、指令跟随、真实感和世界知识等方面表现突出，尤其在多语言文字（包括中文）的生成上准确率大幅提升，能生成高度逼真的证件、界面和产品标签图像。这一进步使得截图作为证据的可信度受到挑战，但也为设计、产品原型制作及内容配图等场景带来便利。与此同时，GPT Image 2在多项测试中表现优于Google的Nano Banana Pro及Midjourney等模型。目前该模型仍处于A/B测试阶段，预计将在5月随DALL-E系列服务退役后正式发布。用户可尝试在LM Arena平台体验，并参考社区推荐提示词生成高质量图像。

你对文生图的印象还停留在 Nano Banana 吗?

可是孩子,时代又变了。

@johnAGI168 https://x.com/johnAGI168/status/2044781168151724067

@0115hippo https://x.com/0115hippo/status/2044722124611539160

4 月初,LM Arena 评测平台上出现了三个匿名图像模型,代号分别是 maskingtape-alpha、packingtape-alpha、gaffertape-alpha。几小时后它们消失了。

OpenAI 官方还没有正式宣布这个模型,但根据 API 返回的元数据和用户侧的测试记录,它已经有了一个被广泛接受的名字:GPT Image 2。

截图不能再当证据了

过去几年,AI 生图模型最明显的短板之一就是图片里的文字。DALL-E 3 时代,你让它在图里写「Hello」,出来的可能是「Hellp」甚至「Hl10」,字母像喝醉了一样东倒西歪。GPT Image 1 好了很多,能处理简单的英文标签。到 GPT Image 1.5 ,其对英文文字的渲染准确率已经接近 95%,但在中文、日文、韩文等非拉丁字母体系上仍有明显缺陷。

而 GPT Image 2 的泄露样图改变了这个印象。

@MrLarus https://x.com/MrLarus/status/2044824800909054181

@akokoi1 https://x.com/akokoi1/status/2044789531615056175

图片里的文字,该是什么就是什么。中文清晰,字形准确,笔画完整。有人测试生成一张身份证样式的图片,姓名、地址、证件号码全部正确渲染,排版规整,初看像是真实文件的照片。

这是个好消息。文字渲染的进步,意味着生成信息图、海报、产品包装、排版复杂的图表,都变得更可靠了。

但硬币总有另一面。一个能生成以假乱真的证件样式图、精确渲染 UI 截图的模型,自然也让「截图可以作为证据」这件事变得越来越可疑。

对比来看,这也是 GPT Image 系列和其他模型的核心差异所在。Midjourney 至今在文字渲染上毫无建树,Stable Diffusion 系列也是老问题。根据泄露的 Arena 测试结果,GPT Image 2 在文字渲染、指令跟随、照片真实感和世界知识四个维度上均超过 Midjourney,后者的优势主要保留在艺术风格和美学控制上。

它真的知道这个世界长什么样吗

有测试者让模型生成一个假想的 GPT-8 产品定价页面,结果出来的图,排版确实是 OpenAI 官网的风格,按钮位置和字体选用像是从真实界面截取的,价格表格的层级逻辑也是对的。

GPT Image 2 能生成与真实软件界面极为相似的图像,包括浏览器窗口、移动端应用界面、数据可视化图表,保真度是上一代产品无法比拟的。

@johnAGI168 https://x.com/johnAGI168/status/2044781168151724067

@levelsio https://x.com/levelsio/status/2040333489476681758

这将带来一些很有意思的实际用途。设计师在做产品原型的时候,不需要先打开 Figma 画一堆框架,直接用文字描述想要的界面,出来的就是一张可以用来和团队讨论的参考图。做投资人 Deck 时,不需要等工程师写代码就能展示一个「产品截图」。写文档的时候,用来配图的示例界面可以直接生成,不用对着空白页面想截图从哪里找。

@marmaduke091 https://x.com/marmaduke091/status/2040338311873515597

生图这件事,已经不只是「生图」了

OpenAI 已经宣布 DALL-E 2 和 DALL-E 3 将于 2026 年 5 月 12 日正式停止服务。Azure OpenAI 的 DALL-E 3 已经在 2 月提前退役了。

DALL-E 是很多人第一次接触 AI 生图的地方,从那些模糊的早期作品到今天,才短短几年。

与此同时,2026 年初刚刚凭借 Nano Banana Pro 确立行业地位的 Google,或许将感受到压力。早期测试报告显示,GPT Image 2 在真实感、文字渲染和世界知识三个维度上同时超越了 Nano Banana Pro,这种三连胜并不常见。

对于创作者来说,感受是复杂的。插图师、平面设计师、摄影师,已经不是第一次面对这个话题了。自 GPT Image 1 发布以来,自由职业平面设计职位数量下降了约 18%。AI 在某些场景下确实取代了「我要雇一个人做这件事」的决策,但它也在创造新的工作方式,让一个人能做的事变多了。

生图模型的进化速度,已经不再给人留出太多适应时间了。GPT Image 1 从上线到 1.5,不过几个月。1.5 到 2,大概也就半年。每一代都在解决上一代的核心短板,同时打开新的可能性。

GPT Image 2 现在还处于 A/B 测试阶段,部分 ChatGPT 用户已经随机获得了访问权限。正式发布的时间窗口,普遍预测就在 5 月 DALL-E 退役前后。想要提前体验的话,目前可以在 LM Arena 评测平台碰碰运气。

Test Address: https://arena.ai

根据社区反馈和该模型的已知优势,以下提示模板可以最大限度地提高你的成功几率:

UI/截图提示:一张照片级逼真的手机银行应用截图,清晰显示交易记录,其中日期、金额和商户名称清晰可辨。iPhone 16 屏幕,自然手持手机,咖啡店背景。

产品标签提示:一张照片级精酿啤酒瓶产品照片,标签细节清晰,显示酒厂名称「Oakridge Brewing Co.」,酒精度 6.8%,山脉标志及配料表。棚内布光,白色背景。

标识提示:一张东京夜间巷道的街景照片,可见多处日英双语霓虹灯招牌,包括写有「Ichiban Ramen — Est. 1987」的拉面店招牌、卡拉 OK 酒吧招牌以及各种发光的广告牌。雨后湿滑的人行道上映着灯光。

界面/世界知识提示:一张照片级真实的 YouTube 视频截图,展示了一段名为「如何在 2026 年组装电脑」的视频,该视频拥有 230 万次观看,配有逼真的评论区、侧边栏推荐视频以及频道信息。桌面浏览器视图。

宽屏触发提示:这是一张电影般的宽银幕照片,拍摄了宜家门店黄昏时分的外观,展示了发光的宜家招牌、停车场里有逼真的汽车,以及进进出出的购物者。黄金时刻灯光,格式 16:9。

未标注图片来源及参考:https://miraflow.ai/blog/how-to-use-duct-tape-ai-model-arena-gpt-image-2-guide

本文来自微信公众号“APPSO”,作者:发现明日产品的

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İlgili Sorular

QGPT Image 2相比前代模型在文字渲染方面有哪些显著提升？

AGPT Image 2在文字渲染方面实现了显著突破，能够准确生成中文、英文等多种语言的文字，字形和笔画完整清晰。例如，它可以生成排版规整的身份证样式图片，甚至能正确渲染复杂的UI界面文字，解决了早期模型（如DALL-E 3）文字错乱的问题。

QGPT Image 2的泄露对‘截图作为证据’的可靠性有何影响？

AGPT Image 2能够生成高度逼真的证件、UI界面等图像，且文字渲染极其准确，这使得伪造的截图难以被肉眼识别。因此，传统上依赖截图作为证据的可靠性大幅降低，因为AI生成的虚假截图可能以假乱真。

QGPT Image 2在哪些实际应用场景中具有优势？

AGPT Image 2在生成信息图、产品原型设计、投资演示、文档配图等场景中优势明显。例如，设计师可以直接用文字描述生成界面参考图，无需手动绘制；投资者能快速生成产品截图用于Deck展示，提高效率。

QGPT Image 2与Google的Nano Banana Pro相比如何？

A根据早期测试，GPT Image 2在真实感、文字渲染和世界知识三个关键维度上均超越了Google的Nano Banana Pro，实现了全面领先。而Nano Banana Pro仅在艺术风格控制上可能保留部分优势。

Q如何提前体验GPT Image 2模型？

A目前GPT Image 2处于A/B测试阶段，部分ChatGPT用户可能随机获得访问权限。用户也可以尝试通过LM Arena评测平台（https://arena.ai）碰运气，但需注意该模型尚未正式发布，OpenAI计划在2026年5月DALL-E退役前后推出。

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