PROMPT RECORD图像记录

大多数 AI 图像工作流在提示词还谈不上糟糕之前，就已经浪费了算力。 真正的瓶颈是从一个空白框开始。 awesome-gpt-image-2-prompts 把 GPT-Image-2 提示词...

中文说明

大多数 AI 图像工作流在提示词还谈不上糟糕之前，就已经浪费了算力。 真正的瓶颈是从一个空白框开始。 awesome-gpt-image-2-prompts 把 GPT-Image-2 提示词变成一种可参考的工作流： 覆盖人像、海报、产品广告、UI 模型图、角色设定集、对照测试以及社区实验的提示词与生成样例。 过去：猜测一种风格，生成，删除，再重写一遍相同的光照/构图描述。 现在：从已经生成出可见图像的案例出发，再调整其结构。 在你需要以下场景时使用它： - 在不凭空发明镜头语言的前提下，搭建人像/摄影提示词 - 在消耗下一轮生成之前，研究海报与广告的版式 - 把 UI 模型图的取景模式复制到自己的产品图中 - 将角色设定集与 3x3 一致性提示词放在手边随时取用 - 当 GPT-Image-2 的输出出现漂移...

原始 Prompt

Most AI image workflows waste credits before the prompt is even bad.

The real bottleneck is starting from a blank box.

awesome-gpt-image-2-prompts turns GPT-Image-2 prompting into a reference workflow:

prompt + output examples across portraits, posters, product ads, UI mockups, character sheets, comparison tests, and community experiments.

Before:guess a style, generate, delete, rewrite the same lighting/composition words again.
After:start from cases that already produced visible images, then adapt the structure.

Use it when you need to:

- build a portrait/photo prompt without inventing camera language from scratch
- study poster and ad layouts before burning another generation loop
- copy UI mockup framing patterns into your own product shots
- keep character-sheet and 3x3 consistency prompts close at hand
- compare community examples when GPT-Image-2 output drifts

The repo is already at 3.9k+ stars and keeps adding curated cases.

The expensive part was never writing one prompt.

It was accepting blind trial-and-error as the normal way to make images.

Read the examples before your next generation session.