What Kind of Images Can Be Generated with Awesome-GPT-Image-2: 13 Visual Categories Explained

Awesome-GPT-Image-2 enables the generation of more than 500 distinct image types across 13 curated categories—including UI interfaces, product shots, architectural renders, and historical scroll artwork—using structured prompt templates for GPT-Image-2 and GPT-Image-2.5 models.

Awesome-GPT-Image-2 is an open-source repository that organizes more than 500 real-world image generation cases into a reusable framework. By combining structured prompt templates with OpenAI's GPT-Image-2 (and 2.5) models, this library allows developers and designers to programmatically create specific visual content types ranging from mobile app interfaces to historical artwork. The repository maintains all case definitions, templates, and agent skills necessary to understand exactly what kind of images can be generated with awesome-gpt-image-2 and how to produce them consistently.

The 13 Visual Categories in Awesome-GPT-Image-2

The repository organizes its case library into 13 primary taxonomies documented in README.md and indexed in docs/gallery.md. Each category contains numerous specific templates referenced by case numbers.

Interface and Data Visualization

• UI & Interfaces: Generate mobile app screens, dashboards, website mock-ups, and social media snapshots. Case #2 demonstrates a "Social Media Interface Screenshot" using the template defined in the UI category.
• Charts & Infographics: Create data visualizations, knowledge maps, and technical diagrams. The "Urban Metabolism Atlas" (Case #1) exemplifies complex infographic generation capabilities.

Marketing and Commercial Assets

• Posters & Typography: Design event posters, type-driven layouts, and bold visual headlines. Case #539 features a "Raw Sketchy Portrait Poster" template.
• Products & E-commerce: Produce product shots, packaging visuals, detail-page imagery, and advertisement banners. Case #332 showcases a "Tea Pi Product Poster".
• Brand & Logos: Develop logos, brand identity systems, and campaign graphics. The "Travel Souvenir Enamel Pin Badge" (Case #543) demonstrates brand asset creation.

Artistic and Photorealistic Content

• Photography & Realism: Generate portraits, phone-camera style images, and film-texture shots. Case #6 includes a "Japanese Fantasy Illustration" rendered with illustrative realism.
• Illustration & Art: Produce artistic styles ranging from brushwork to fantasy worlds and stylized paintings.
• Characters & People: Create character designs, pose sheets, and 3-D toy renders. Case #166 presents a "Twelve Gold Saints Card Set".
• Scenes & Storytelling: Build storyboards, livestream frames, and world-building scenes. Case #330 offers a "Moonlit Livestream Scene" template.

Architectural and Historical Content

• Architecture & Spaces: Generate interior renders, city maps, and architectural concepts. Case #331 provides a "Hand-Drawn Xi'an Watercolor Map".
• History & Classical Chinese Themes: Create scroll-style narrative art and historical figure portraits. The "Red Cliff Classical Scroll" (Case #338) represents this specialized category.

Documentation and Experimental

• Documents & Publishing: Produce white-papers, manuals, and encyclopedic plates. Case #539 includes black-and-white typographic portrait layouts suitable for document covers.
• Other Use Cases: Support creative experiments and mixed-workflow outputs. Case #540 features a "Dreamlike Futuristic World Poster" for experimental applications.

How the Prompt-as-Code System Works

Unlike simple prompt collections, awesome-gpt-image-2 implements a structured template system defined in docs/templates.md. These templates break prompts into reusable components including subject definitions, lighting parameters, material specifications, and layout constraints.

The master data resides in data/style-library.json, which catalogs every available style and template entry. This JSON file is consumed by both the website frontend and the agent skill system. According to the repository source code, the style-library skill (agents/skills/gpt-image-2-style-library/SKILL.md) exposes these templates to LLM agents, enabling programmatic retrieval of exact prompt structures for any supported visual category.

Three Methods to Generate Images

Command Line Interface

The gpt-image-2-style-library npm package provides command-line access to the template system. After installation, you can request category-specific prompts with custom variables:

# Install the skill globally

npm install -g gpt-image-2-style-library
gpt-image-2-style-library install all

# Generate a poster prompt with custom variables

gpt-image-2-style-library prompt \
  --category poster \
  --subject "Eco-friendly travel app UI" \
  --variables "primaryColor=#00A86B, secondaryColor=#FFD700"

JavaScript API Integration

For web applications, the src/apimartClient.js module offers a direct interface to the image generation backend:

import { generateImage } from './apimartClient.js';

// Construct prompt using the UI category template
const prompt = `
UI & Interfaces – Mobile App
Subject: Eco-friendly travel app
Layout: Card-based dashboard with map preview
Colors: #00A86B primary, #FFD700 accent
Typography: Clean sans-serif
`;

generateImage(prompt)
  .then(url => console.log('Generated image URL:', url))
  .catch(err => console.error('Generation failed:', err));

React Web Interface

The hosted interface at https://gpt-image2.canghe.ai/ provides visual browsing capabilities:

1. Navigate to the gallery and select a category (e.g., UI & Interfaces).
2. Click "View Cases" to browse the 500+ case library.
3. Select a specific case and press "Generate Image" to execute the prompt against the APIMart service.

This workflow requires authentication but offers immediate visual feedback on template selection.

Summary

• Awesome-GPT-Image-2 supports 13 distinct visual categories ranging from technical UI mockups to historical Chinese scroll artwork.
• The repository contains over 500 specific case templates documented in docs/gallery.md and stored in data/style-library.json.
• Generation occurs through a Prompt-as-Code system defined in docs/templates.md, enabling consistent, reproducible outputs.
• Three integration methods exist: CLI skill, JavaScript API via src/apimartClient.js, and React web interface.
• All templates are compatible with both GPT-Image-2 and GPT-Image-2.5 models through the APIMart backend service.

Frequently Asked Questions

Can Awesome-GPT-Image-2 generate mobile application interfaces?

Yes. The UI & Interfaces category specifically supports mobile app screens, dashboards, and website mockups. Case #2 demonstrates a "Social Media Interface Screenshot" template, and the docs/templates.md file defines structured components for layout, color schemes, and typography suitable for app prototyping.

How many real-world cases are documented in the repository?

The repository catalogs more than 500 real-world cases (specifically 544+ as indexed in docs/gallery.md). Each case represents a tested, reproducible prompt template for the GPT-Image-2/2.5 models, covering all 13 visual categories from product photography to classical artwork.

Where is the style library data stored for programmatic access?

All style definitions reside in data/style-library.json, which serves as the master database consumed by the CLI tool, web frontend, and agent skills. The programmatic interface is further defined in agents/skills/gpt-image-2-style-library/SKILL.md, which specifies how LLM agents can query and utilize these templates.

Does the library support GPT-Image-2.5?

Yes. According to the repository source code, the library maintains compatibility with both GPT-Image-2 and GPT-Image-2.5 models. The structured prompt templates in docs/templates.md and the APIMart client implementation in src/apimartClient.js support generation requests for both model versions.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}