# Prompt Templates for GPT-Image2: A Complete Guide to Image Generation Patterns

> Explore prompt templates for GPT-Image2 in the awesome-gpt-image-2 repository. Find fill-in-the-blank and JSON formats for diverse visual design needs. Master image generation patterns.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: deep-dive
- Published: 2026-08-24

---

**The awesome-gpt-image-2 repository maintains a comprehensive collection of prompt templates for GPT-Image2 in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md), providing both plain-text fill-in-the-blank formats and structured JSON variants across eleven distinct visual design domains.**

The `awesome-gpt-image-2` project delivers a production-ready prompt engineering system that standardizes how developers and designers interact with GPT-Image2. All templates are cataloged in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) and implement a dual-format architecture: human-readable plain-text for manual iteration and machine-readable JSON schemas for autonomous agent integration. This design ensures consistent, high-quality image generation whether you are prototyping UI mockups or batch-processing e-commerce assets.

## Template Architecture and Design Philosophy

The prompt template system in `awesome-gpt-image-2` follows a modular, substitution-based architecture that separates content from presentation logic.

### Placeholder-Driven Plain-Text Design

Every plain-text template uses bracketed placeholders such as `[产品类型]`, `[平台]`, and `[功能点A]` to define variable injection points. The frontend component in [`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx) parses these templates and substitutes placeholders with user-provided values before dispatching requests to the `/api/generate-image` endpoint. This approach allows non-technical users to generate complex prompts by filling out simple form fields while maintaining the linguistic nuance required for high-fidelity image generation.

### JSON Schema for Programmatic Control

For automated workflows and AI agents, each template offers a JSON variant that replaces ambiguous natural language with explicit key-value pairs. These schemas include structured fields such as `type`, `platform`, `style`, `constraints`, and `content`, enabling deterministic prompt construction in [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js). The JSON format eliminates parsing ambiguity and allows backend systems to validate inputs against specific schema requirements before forwarding requests to OpenAI's image models.

### Avoid-Pitfalls Validation System

Each template section concludes with a "避坑指南" (avoid-pitfalls guide) that documents common failure modes, such as missing Chinese character support or incompatible aspect ratios. The frontend surfaces these warnings as inline validation messages during template substitution, significantly reducing generation failure rates by preempting malformed prompts.

## Available Template Categories in docs/templates.md

The [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) file organizes prompt templates into eleven visual-design domains, each accessible via anchored links for direct programmatic reference.

### UI and Interface Design

Located at `#tpl-ui`, this template generates high-fidelity interface screenshots for specified platforms. It accepts placeholders for product type, platform (iOS/Android/Web), feature modules, visual style, and output resolution, producing mockups with clear information hierarchy and readable typography.

### Data Visualization and Charts

The `#tpl-infographic` section targets information design, accepting parameters for topic, target audience, data modules, chart types, and stylistic constraints. The JSON variant allows agents to pass exact hierarchical data structures, ensuring generated infographics accurately represent complex datasets.

### Posters and Typography

Under `#tpl-poster`, this template drives promotional material creation by specifying activity descriptions, visual motifs, color palettes, and compositional layouts. It optimizes for focal-point hierarchy, ensuring headlines remain legible at various output resolutions.

### E-commerce and Product Photography

The `#tpl-product` template standardizes marketplace asset generation by accepting product names, selling points, scene descriptions, camera angles, and optional iconography. It produces hero images optimized for e-commerce platforms with consistent lighting and background separation.

### Brand Identity and Logos

Found at `#tpl-brand`, this domain generates comprehensive brand kits including logo variations, color systems, and application mock-ups. It interprets brand keywords and directional guidance to maintain consistency across identity collateral.

### Architecture and Spatial Design

The `#tpl-architecture` template produces realistic interior and exterior renders by specifying space types, functional requirements, material palettes, and lighting conditions. It often leverages Unreal Engine-style rendering cues to achieve photorealistic architectural visualization.

### Photography and Realism

Under `#tpl-photo`, this category yields hyper-realistic photographs through detailed camera specifications, depth-of-field settings, lighting moods, and texture constraints. It respects photographic physics to generate images indistinguishable from camera capture.

### Illustration and Art

The `#tpl-illustration` section drives stylized artwork creation by defining subjects, artistic movements, line qualities, color palettes, and background treatments. It supports diverse aesthetics from flat vector art to textured painterly styles.

### Characters and Roles

Located at `#tpl-character`, this template generates concept art and reference sheets by detailing character traits, poses, expressions, and world-building context. It maintains consistency across multiple character views for production pipelines.

### Scenes and Narrative

The `#tpl-scene` template creates cinematic storytelling imagery by describing narrative context, actions, camera angles, and atmospheric conditions. It captures specific moments in visual narratives with appropriate emotional tone.

### Other Applications

The `#tpl-other` section provides a flexible catch-all using a generic "objective + input constraints + output constraints" pattern, accommodating custom use-cases that fall outside the predefined categories.

## Implementing Templates in Production Code

Integrating these templates requires handling both substitution logic for plain-text and schema validation for JSON variants.

### Plain-Text Substitution Workflow

The following pattern demonstrates how [`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx) processes UI templates before API submission:

```javascript
// Template from docs/templates.md (UI section)
const uiTemplate = `
为[产品类型]生成一张[平台]界面图。
核心功能：[功能点A]、[功能点B]、[功能点C]。
视觉风格：[极简]，主色[颜色]，强调色[颜色]。
布局：[顶部导航]，信息层级清晰，留白充足。
输出：高保真UI截图，文字清晰可读，比例[16:9]。`;

// Placeholder substitution logic
const prompt = uiTemplate
  .replace('[产品类型]', '金融理财App')
  .replace('[平台]', 'iOS')
  .replace('[功能点A]', '资产概览')
  .replace('[功能点B]', '交易记录')
  .replace('[功能点C]', '风险分析')
  .replace('[极简]', '极简')
  .replace('[颜色]', '深蓝')
  .replace('[顶部导航]', '顶部导航')
  .replace('[16:9]', '9:16');

// Dispatch to backend handler
fetch('/api/generate-image', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ prompt })
});

```

### Structured JSON Requests

For agent-based systems, the JSON variant provides explicit schema control:

```json
{
  "type": "UI Screenshot",
  "platform": "iOS",
  "product": "金融理财App",
  "layout": "Top navigation, clear hierarchy, plenty of whitespace",
  "style": {
    "theme": "Minimalist",
    "primary_color": "Deep blue",
    "accent_color": "Blue"
  },
  "content": {
    "features": ["资产概览", "交易记录", "风险分析"]
  },
  "constraints": "High-fidelity UI screenshot, readable Chinese text, aspect ratio 9:16"
}

```

### API Integration Points

The [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) serverless function accepts both formats, normalizing plain-text prompts and validating JSON schemas before forwarding to OpenAI's image generation models. This dual-format support ensures compatibility with both manual user interfaces and automated batch processing pipelines.

## Core Files in the Prompt Template Ecosystem

Four primary files constitute the prompt template subsystem in `freestylefly/awesome-gpt-image-2`:

- **[`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md)** – The master template catalog containing all eleven domain categories with both plain-text and JSON variants, organized with anchored headers for direct linking.

- **[`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx)** – The React frontend component that renders the template selection interface, executes placeholder substitution, and manages state for user input values.

- **[`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js)** – The server-side Vercel function that receives normalized prompts and handles authentication and communication with OpenAI's GPT-Image2 API endpoints.

- **`scripts/generate-site-data.mjs`** – A build-time script that parses [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md) to generate static navigation data, populating the documentation sidebar with categorized template links.

## Summary

- The `awesome-gpt-image-2` repository centralizes all **prompt templates for GPT-Image2** in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md), organized into eleven visual design domains.

- Templates follow a **dual-format architecture**: plain-text with bracketed placeholders for manual use, and structured JSON for programmatic agent integration.

- **Placeholder substitution** occurs in [`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx) for user interfaces, while [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js) handles backend validation and API forwarding.

- Each template includes **avoid-pitfalls guidance** ("避坑指南") to preempt common generation failures related to aspect ratios, text rendering, and cultural specificity.

- The **JSON schema** provides deterministic control over image generation parameters, making it ideal for automated workflows and batch processing systems.

## Frequently Asked Questions

### How do I choose between plain-text and JSON template formats?

**Use the plain-text format when manually crafting prompts through the web interface, as it preserves natural language flow and is easier to edit in [`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md).** Choose the JSON variant when building automated agents or batch processing scripts that require strict parameter validation and deterministic prompt construction without ambiguity.

### What are the bracketed placeholders like `[产品类型]` in the templates?

**These are variable injection points that the frontend replaces with user-specific values before sending requests to [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js).** The system uses Chinese bracket notation to clearly distinguish template variables from static prompt content, enabling reliable string substitution in [`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx).

### Can I modify existing templates or add new categories to docs/templates.md?

**Yes, the repository structure supports extension by following the established anchor link pattern (e.g., `#tpl-custom`) and maintaining both plain-text and JSON variants.** New templates should include the standard "避坑指南" section documenting potential failure modes, and you must regenerate the site data using `scripts/generate-site-data.mjs` to update the navigation sidebar.

### Where does the actual image generation happen in the codebase?

**Image generation occurs in [`api/generate-image.js`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/api/generate-image.js), the serverless function that receives the final prompt string—whether derived from plain-text substitution or JSON normalization—and forwards the authenticated request to OpenAI's GPT-Image2 API.** The frontend ([`src/main.jsx`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/src/main.jsx)) never directly accesses the image models, ensuring API keys remain secure on the server side.