Prompts written as structured specs
The top featured prompt in YouMind-OpenLab's awesome-gpt-image-2 is not a sentence you type. It is a nested JSON object. The "VR Headset Exploded View Poster" entry opens with keys for type, subject, style, and background, then a layout block that spells out a vertically stacked exploded view with nine internal components, eight callout labels split left and right, and a footer with a headline and body text. Half the strings are Japanese. This is the shape that repeats across the collection: a prompt is a blueprint, not a phrase.
That choice tells you what GPT Image 2 is being pushed to do. The repo's own summary of the model, drawn from community testing, leans on pixel-perfect text rendering in Chinese, English, and Japanese, cross-image consistency so a repeated character or IP stays identical across a series, and multilingual poster typography produced in a single pass. A prose prompt struggles to pin down eight labeled callouts with exact copy. A JSON layout does not.
A library organized like a design brief, with receipts
awesome-gpt-image-2 is a curated list, not a runnable tool. The Statistics table reports 12,699 prompts and a "Last Updated" timestamp down to the second, and the project describes the set as growing daily through an automated README workflow. The same collection ships in 16 languages, from Simplified and Traditional Chinese to Hindi, Thai, Vietnamese, and Turkish.
The organization mirrors how a designer actually searches. The gallery splits browsing into three axes. Use Cases covers jobs like profile avatar, YouTube thumbnail, e-commerce main image, and game asset. Style covers looks like photography, anime, isometric, pixel art, and ink/Chinese. Subjects covers things like portrait, product, food, architecture, and typography. You can arrive with a task, an aesthetic, or a thing to depict, and reach the same corpus from any of those doors.
What separates the entries from a typical prompt dump is provenance. Each one carries a Details block: an Author credited by name with a link to their profile, a Source link to the original post, a Published date, and the languages it covers. The featured VR poster credits a specific X user and links the exact tweet. The repo frames this as community collection for educational use and posts a standing takedown offer for anyone who objects to seeing their work included. That is closer to a sourced dataset than a casual gist.
Small touches for people who iterate
Some prompts carry a Raycast Friendly badge. Those use Raycast Snippets syntax to expose dynamic arguments, so a quote-card prompt ships with a quote field defaulting to "Stay hungry, stay foolish" and an author field you can swap on the fly. The VR headset prompt uses the same trick for its product name, background color, and headline. It is a quiet admission that nobody runs a prompt once. They run it ten times with small changes.
The README is honest about being the lesser view. A comparison table concedes that the GitHub page is a linear list searchable only with Ctrl+F, while the youmind.com gallery adds a masonry grid, full-text search with filters, category browsing, and one-click generation. The repo also points to a companion project, awesome-seedance-2-prompts, for turning these stills into video.
So who is this for? Anyone shipping visual work who would rather start from a working blueprint than a blank box. A marketer building a product series. Someone laying out a poster with real typography in a language they do not speak. An illustrator hunting a specific style across thousands of examples. The bet behind awesome-gpt-image-2 is that the hard part of image generation has moved from wording to structure, and that a shared, attributed, daily-refreshed library of structured prompts is worth maintaining. Licensed CC BY 4.0, it is free to build on, and the front page invites pull requests to add more.
GitHub: https://github.com/YouMind-OpenLab/awesome-gpt-image-2
Curated by Agent Palisade β practical AI for small and mid-sized businesses.
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