What Scene Tags Can I Use for Image Generation in Awesome‑GPT‑Image‑2?

Awesome‑GPT‑Image‑2 provides ten predefined scene tags—creative, tech, commerce, education, social, fashion, food, travel, story, and history—defined in data/style-library.json to categorize visual context and guide AI image generation.

The scene tag system in Awesome‑GPT‑Image‑2 allows developers and prompt engineers to classify image generation requests by narrative context and functional purpose. These tags act as metadata filters that help the style library select appropriate templates and guide the generation engine toward contextually relevant compositions.

Where Scene Tags Are Defined

The canonical list of scene tags lives in data/style-library.json within the scenes array. Each entry contains an identifier (id), a short label (value), and localized titles for multilingual support.

According to the Awesome‑GPT‑Image‑2 source code, the scenes array maps functional categories to prompt templates, enabling the system to filter cases by narrative context or suggest appropriate styling for specific domains.

Complete List of Available Scene Tags

The repository currently exposes ten distinct scene tags. Each tag includes both English and Chinese localized titles to support international workflows:

Tag ID	Label	English Title	Chinese Title
creative	Creative	Creative	创意
tech	Tech	Tech	科技
commerce	Commerce	Commerce	商业
education	Education	Education	教育
social	Social	Social	社媒
fashion	Fashion	Fashion	时尚
food	Food	Food	食品饮品
travel	Travel	Travel	旅行
story	Story	Story	叙事
history	History	History	历史

Selecting travel signals the engine to favor city maps, street views, or landscape compositions, while fashion triggers portrait-oriented templates and style references.

How to Use Scene Tags in Code

Scene tags integrate directly into the gpt-image-2-style-library skill and API requests. You pass the scene parameter alongside style and additional tags to contextualize generation.

Style Library Skill Integration

When calling the style library skill, specify the scene to filter template selection:

// Requesting a travel-scene infographic
await gptImage2StyleLibrary({
  style: "infographic",
  scene: "travel",          // scene tag identifier
  tags: ["Map", "Travel"],
});

// Generating a fashion-scene portrait
await gptImage2StyleLibrary({
  style: "photography",
  scene: "fashion",         // scene tag identifier
  tags: ["Portrait", "Fashion"],
});

API Query Filtering

You can also filter existing case galleries by scene tag via the REST API:

// Filtering the gallery for education-scene cases
fetch("https://gpt-image2.canghe.ai/api/cases?scene=education")
  .then(r => r.json())
  .then(cases => console.log(cases));

Technical Implementation Details

The scene tag system spans three critical files in the repository:

• data/style-library.json – Stores the master scenes array containing all tag definitions, localized strings, and metadata mappings.
• agents/skills/gpt-image-2-style-library/SKILL.md – Documents the agent skill contract that consumes scene tags to route prompts to appropriate generation pipelines.
• docs/templates.md – Contains full prompt template specifications where scene tags trigger context-specific styling instructions and composition guides.

When the skill processes a request, it cross-references the provided scene tag against the style library to retrieve predefined aesthetic parameters and negative prompts associated with that narrative context.

Summary

• Awesome‑GPT‑Image‑2 defines 10 fixed scene tags in data/style-library.json: creative, tech, commerce, education, social, fashion, food, travel, story, and history.
• Each tag includes bilingual support (English and Chinese) via the value and localized title fields.
• Pass scene tags via the scene parameter in gptImage2StyleLibrary() calls or as query parameters in API requests.
• The gpt-image-2-style-library skill uses these tags to select contextually appropriate prompt templates and styling guides.

Frequently Asked Questions

How do I add a custom scene tag to Awesome‑GPT‑Image‑2?

You cannot add custom scene tags without modifying the source. The repository uses a fixed enumeration defined in data/style-library.json. To extend the system, submit a pull request updating the scenes array in that file and regenerate the style library cache.

Do scene tags affect the image generation model directly?

No, scene tags are metadata filters used by the style library skill to select templates and guide prompt construction. They do not pass through as raw tokens to the diffusion model; instead, they trigger pre-configured prompt templates and negative prompts defined in docs/templates.md.

Can I use multiple scene tags in a single request?

The current implementation in SKILL.md suggests single-scene selection. Requests typically accept one scene string value. For hybrid contexts, use the tags array to supply additional descriptive keywords while keeping the primary scene as the dominant narrative category.

What is the difference between scene and tags parameters?

The scene parameter accepts one of the 10 predefined categorical tags (creative, tech, etc.) that drive template selection. The tags array accepts arbitrary descriptive strings that refine the subject matter (e.g., "Portrait", "Map") but do not trigger specific style library routing logic.

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