偷參考美學做真感寫真

提示詞

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

中文翻譯

如何用 gpt-image 2.5 生成看起來 100% 真實的 AI 照片

2.5 比 2 更進一步，但它依然帶着同樣的破綻

直接開箱、不給參考圖，你得到的仍然是那股 AI 味

所以別再費勁描述美學了，直接去“偷”

找一張已經具備你想要那種質感的照片

把它丟進 gpt-6 astra，然後這樣問

“分析這張照片，給我一份極其詳細的 json prompt 來描述它的視覺風格。拆解色彩分級、光線和準確的色值，讓我能把這種質感套用到自己的圖像上”

astra 會把參考圖反向工程成 prompt

那種調色你本來就描述不出來

把這份 json 拿進 gpt-image 2.5

附上你的產品圖和 prompt

“以這份 json 爲參考，生成一個手裏拿着我產品的人”

然後再不加 json 生成同樣的內容，把兩者並排放在一起對比

光線和調色就是全部的差別所在

把你喜歡的角色存下來

以後每次生成都附上那張照片，並要求它保持同樣的面部特徵

這樣你在整個產品素材庫裏就有了一個一致的人物

想要不同的情緒氛圍
回到 astra，迭代那份 json，再跑一次

那些破綻幾乎總是同樣兩樣東西

平淡的默認色彩和那股塑料顆粒感

所以要有意識地調色，並且優先檢查這兩點

json 控制光線
角色控制一致性
你控制產品

這就是一套可複製的 UGC 流水線，而不是每次都靠擲骰子

溯源

原作者

sixugc

發佈日期

2026-09-10

原始出處

查看原帖 ↗

收錄來源

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