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Guide · Open source

How to use awesome-gpt-image-2: browse, copy and adapt GPT Image 2 prompts

Updated · written with AI from the project's own documentation and checked against our catalogue data

awesome-gpt-image-2 is an open-source prompt library and example gallery for OpenAI's GPT Image 2 model. It stores prompts as structured, reusable templates called 'Prompt as Code', alongside example images and a companion website. This guide shows you how to get the repository, find a case that fits your goal, reuse its template and understand what the project does not do.

awesome-gpt-image-2freestylefly/awesome-gpt-image-2 · ★ 35.4k · MIT

Quick start

  1. 1Clone the repository. Run the git clone command to download awesome-gpt-image-2 to your machine.
  2. 2Read the README. Open the README to see the case index, template sets and the 2.5 spotlight section.
  3. 3Browse the gallery. Visit the companion website to filter cases by style or scenario and open large previews.
  4. 4Pick a case. Choose a case whose subject and style are close to the image you want to make.
  5. 5Copy and adapt the prompt. Copy the full prompt and replace the subject and details while keeping its structure.
  6. 6Generate with your own tool. Run the adapted prompt in a GPT Image 2 tool you have access to, since the library does not generate images itself.

What awesome-gpt-image-2 is and what it is not

The README describes a prompt library, not a standalone image generator. It collects reverse-engineered GPT-Image2 cases, 20+ industrial template sets and reusable Skills, and each case is presented with its prompt and generation record.

The project also includes a GPT Image 2.5 comparison section. According to the README, this update does not add live 2.5 image generation, so you will need your own access to a generation tool to produce images.

  • Prompt library and template collection: yes
  • Companion website for browsing the gallery: yes (gpt-image2.canghe.ai)
  • Standalone app that generates images: no, according to the README

Get the repository and open the library

The project is distributed on GitHub and is MIT licensed. Clone the repository to keep a local copy of the prompts, images and documentation. The README lists no separate prerequisites for reading the material.

If you prefer not to clone, the README also points to the live gallery website, where you can browse cases without any setup.

  • Read the README first; it has the full case index and section links.
  • The README is also available in Simplified Chinese and Japanese.
  • Use the website if you want a visual, filterable view of the gallery.
git clone https://github.com/freestylefly/awesome-gpt-image-2

Find a case that matches your goal

Cases are grouped by style and scenario on the website, where you can filter them. The website also lets you open large previews and copy the full prompt for any case.

If you are comparing models, the 2.5 spotlight shows four recreations that use the same full gallery prompt as the original, with a draggable divider and side-by-side view. The README notes that original generation conditions and exact tool model IDs for these recreations remain unverified.

  • Filter by style or scenario on the website.
  • Open a case to see its prompt and original image.
  • Open the 2.5 spotlight to compare outputs from the same prompt.

Adapt a prompt template for your own images

The project's core idea is 'Prompt as Code': prose prompts are split into structured parts such as subject, lighting, materials, layout and visual details. The README says these parts are designed to be composable, which makes them easier to reuse for batch generation.

Start with a template close to your use case, replace the subject and details with your own, and keep the structure intact so the output stays consistent across a set of images.

  • Keep the labelled parts (subject, lighting, materials, layout) and change their contents.
  • Change one variable at a time when testing, so you can see what caused a difference.
  • Record your results; the project itself keeps prompts alongside generation records.

Use it for batch workflows and agents

The README frames the project as a way to turn community examples into assets that agents and automation workflows can reuse. Structured templates are meant to be more useful than isolated examples when you need batch generation or repeatable production.

The project also mentions reusable Skills. Check the repository for the Skill files and their documentation before relying on them in your own pipeline, since this guide does not describe their internals.

  • Use templates as the input format for a generation script you write yourself.
  • Check the Skills documentation in the repo before integrating them.

What people use it for

  • Find a working prompt for a poster, icon or illustration and adapt it to your brand.
  • Reuse a structured template to keep a series of images visually consistent.
  • Compare GPT Image 2 and 2.5 outputs from the same prompt using the spotlight section.
  • Build a batch image workflow that feeds structured templates into your own script.

Frequently asked questions

Is awesome-gpt-image-2 free to use?

The repository is released under the MIT licence, as listed on its GitHub page. The MIT licence generally permits reuse and modification, but read the LICENSE file in the repo for the exact terms.

How do I install awesome-gpt-image-2?

Run git clone https://github.com/freestylefly/awesome-gpt-image-2 in a terminal. The README lists no other prerequisites for reading the prompts and documentation.

Does awesome-gpt-image-2 generate images?

No. The README describes a prompt library and website, and says the 2.5 update does not add live 2.5 image generation. You need a separate GPT Image 2 tool to generate images.

What is the difference between GPT Image 2 and 2.5 in this project?

The 2.5 spotlight recreates four cases using the same full gallery prompt, so you can compare outputs. The README says the original generation conditions and exact tool model IDs remain unverified for these recreations.

Is there an alternative if I need an image generator app?

The project is a prompt library, so it is not a replacement for an image generation app. The README does not name comparable projects, so look for a dedicated image generator if you need one.

Why does my output not match the example image?

The README does not describe the original generation settings for most cases, and it notes that some conditions are unverified. Differences in tool, model version or settings can change the result, so test small changes to the prompt one at a time.

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Our verdict

Use awesome-gpt-image-2 if you want working GPT Image 2 prompts and structured templates to copy, adapt and reuse, especially for batch or consistent image work. Pick a different tool if you need a standalone app that generates images or if you need verified original generation settings.

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