偷参考美学做真感写真

提示词

how to generate ai photos that look 100% real with gpt-image 2.5

2.5 is a step up from 2 but it carries the same tells

straight out of the box with no reference you still get that ai look

so stop describing the aesthetic and start stealing it

find a photo that already has the look you want

drop it into gpt-6 astra and ask this

"analyze this photo and give me a very detailed json prompt describing its visual style. break down the color grading, the lighting and the exact colors so i can apply that look to my own images"

astra reverse engineers the reference into a prompt

you were never going to describe that grade yourself

take the json into gpt-image 2.5

attach your product and prompt

"using this json as reference, generate a person holding my product"

then generate the same thing without the json and put them side by side

the lighting and the grade are the whole difference

save the character you like

attach that photo on every future generation and ask it to preserve the same facial features

now you have a consistent person across your entire product library

want a different mood
go back to astra, iterate the json, run it again

the tells are almost always the same two things

flat default color and that plastic grain

so grade on purpose and check those first

the json controls the light
the character controls consistency
you control the product

that is a repeatable ugc pipeline instead of rolling the dice every time

中文译文

如何用 gpt-image 2.5 生成看起来 100% 真实的 AI 照片

2.5 比 2 更进一步，但它依然带着同样的破绽

直接开箱、不给参考图，你得到的仍然是那股 AI 味

所以别再费劲描述美学了，直接去“偷”

找一张已经具备你想要那种质感的照片

把它丢进 gpt-6 astra，然后这样问

“分析这张照片，给我一份极其详细的 json prompt 来描述它的视觉风格。拆解色彩分级、光线和准确的色值，让我能把这种质感套用到自己的图像上”

astra 会把参考图反向工程成 prompt

那种调色你本来就描述不出来

把这份 json 拿进 gpt-image 2.5

附上你的产品图和 prompt

“以这份 json 为参考，生成一个手里拿着我产品的人”

然后再不加 json 生成同样的内容，把两者并排放在一起对比

光线和调色就是全部的差别所在

把你喜欢的角色存下来

以后每次生成都附上那张照片，并要求它保持同样的面部特征

这样你在整个产品素材库里就有了一个一致的人物

想要不同的情绪氛围
回到 astra，迭代那份 json，再跑一次

那些破绽几乎总是同样两样东西

平淡的默认色彩和那股塑料颗粒感

所以要有意识地调色，并且优先检查这两点

json 控制光线
角色控制一致性
你控制产品

这就是一套可复制的 UGC 流水线，而不是每次都靠掷骰子

溯源

原作者

sixugc

发布日期

2026-09-10

原始出处

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收录来源

xianyu110/awesome-gpt-image2.5 · 2098047399264469048 · MIT