译文
After GPT-Image2 was opened, AI drawing has become convenient, but it is extremely costly to stabilize and control the effect, and to reproduce it consistently. It's hard to adjust a satisfactory image, and changing the scene makes it completely different.
There is a project on GitHub called awesome-gpt-image-2, which reached 1700+ Stars in 3 days, specifically to solve this problem.
The core idea is to reverse scattered prompt word cases into structured templates, decomposing 329 high-quality cases into six dimensions: subject, light and shadow, material, layout, color, and atmosphere.
Each case can be decomposed into an atomized Schema, allowing you to quickly find similar cases and copy the structure rather than just copying style words.
14 industrial-grade templates cover 13 categories such as UI, infographic, poster, and photography, following the structure of "task type → structural constraints → style and material". Fill in business variables, and you can quickly generate images that meet expectations.
It also supports JSON templates, and the Agent can understand the calls, enabling batch image generation and automated realization of serialized brand visuals. This allows AI image generation to move from a craft workshop to an industrial assembly line.
GitHub:
桃子AI画廊Tao Image Gallery
