How to Ensure Exact Text Generation in GPT‑Image2 Prompts: 5 Proven Methods

Treat GPT‑Image2 prompts as immutable code and include explicit "must‑show" constraints to force the model to render specified Chinese text verbatim, eliminating hallucinated characters and placeholder gibberish.

The freestylefly/awesome-gpt-image-2 repository demonstrates that GPT‑Image2 interprets prompts as executable instructions rather than mere suggestions. By applying constraint patterns defined in the template files, you can guarantee exact text generation in GPT‑Image2 prompts without random character substitutions or omissions.

Why GPT‑Image2 Requires Code‑Level Constraints

Unlike standard image generation models that interpret text loosely, GPT‑Image2 follows a prompt‑as‑code philosophy where constraints act as strict compilers. The repository's templates in docs/templates.md implement guardrails that prevent the model from "interpreting" text content, instead forcing literal rendering of every specified character.

When you omit these constraints, the model may generate visually plausible but textually incorrect output—swapping characters with similar-looking glyphs or inserting placeholder boxes. The solution lies in treating your text content as locked variables within a programmatic structure.

Five Methods to Guarantee Verbatim Text Rendering

1. Explicit Must‑Show Constraints

The most direct method involves stating that text must display exactly as written. According to the repository's UI screenshot templates in docs/templates.md (lines 64‑66), you should include the phrase "文字必须准确显示指定的中文，禁止乱码和占位文本" (The text must accurately display the specified Chinese, prohibiting garbled code and placeholder text).

This constraint functions as a compiler directive, telling the model to treat the given phrase as immutable visual content rather than a semantic suggestion.

2. Typography‑Centric Title Locking

For concept‑type posters, the title must dominate the visual hierarchy while maintaining exact spelling. The templates in docs/templates.md (lines 81‑84) specify that the title must be spelled exactly without translation, shortening, or misspelling, and must remain the dominant visual element: "巨大、可读、有力量、拼写完全正确" (huge, readable, powerful, spelled exactly correctly).

This approach combines visual prominence constraints with textual immutability, reducing the chance of character hallucination by making the text the primary compositional anchor.

3. JSON‑Structured Prompts with Constraints Fields

Structured prompts provide the most reliable enforcement mechanism. The repository's JSON templates in docs/templates.md (lines 24‑30) include a dedicated constraints field where you embed the exact‑text directive as a machine‑readable rule.

By separating content from constraints within the JSON schema, you create a clear contract that the model must follow, rather than mixing instructions with content in ambiguous natural language.

4. Avoid‑Pitfall Guidelines (避坑指南)

The repository includes specific "avoid‑pitfall" sections (避坑指南) in docs/templates.md (lines 87‑90) that reinforce exact‑text rules. These guidelines remind you to 强制文字锁定 (force text lock) and to specify platform, aspect ratio, and layout explicitly.

Adhering to these bullet points reduces ambiguity that might otherwise cause the model to generate placeholder text when uncertain about spacing or character placement.

5. Visual Layout Directives

Combine text constraints with specific visual parameters—aspect ratio, font size, and positioning—so the model understands precisely where the locked text should appear. When the model knows the text occupies a specific rectangular region at a specific size, it cannot substitute the content with abstract visual noise.

Implementation Examples

Plain‑Text UI Screenshot Prompt

For Chinese interface mockups, combine platform specification with the must‑show constraint:

生成一张[平台，如 iOS]界面图，文字必须准确显示指定的中文，禁止乱码和占位文本，比例 9:16。
核心功能：登录、搜索、个人中心。
视觉风格：极简，主色蓝，强调色橙。
输出：高保真 UI 截图，文字清晰可读。

The critical constraint "文字必须准确显示指定的中文，禁止乱码和占位文本" appears immediately after the generation command, establishing the immutability rule before describing content.

JSON‑Structured Infographic Prompt

For data visualizations requiring precise headings, use the structured template:

{
  "type": "Infographic",
  "topic": "健康管理指南",
  "audience": "中老年人",
  "structure": {
    "title_area": "健康管理",
    "layout": "流程图",
    "modules": [
      {"title": "饮食", "icon": "food", "text": "均衡营养"},
      {"title": "运动", "icon": "run", "text": "每日30分钟"}
    ]
  },
  "style": {
    "aesthetic": "专业报告",
    "colors": "低饱和蓝绿",
    "background": "浅色"
  },
  "constraints": "文字必须准确显示指定的中文，禁止乱码和占位文本"
}

The constraints field at the root level applies to all nested text elements, ensuring that "饮食" and "运动" render exactly without character substitution.

Concept Poster with Title Lock

For artistic posters where the title must remain untouched:

设计一张概念字体海报，标题必须是“未来城市”。  
标题必须是海报的主视觉结构：巨大、可读、有力量、拼写完全正确。不要翻译、不要缩短、不要拼错标题。  
输出：单张海报，文字必须准确显示指定的中文。

This example demonstrates redundant constraint layering: stating the title requirement twice with specific prohibitions against translation and abbreviation.

Critical Source Files

Understanding the repository architecture helps you locate and customize these constraints:

• docs/templates.md — Contains the central repository of prompt templates and "avoid‑pitfall" guidelines that enforce exact text constraints (lines 24‑30, 64‑66, 81‑84, 87‑90).

• agents/skills/gpt-image-2-style-library/SKILL.md — Demonstrates how the style‑library skill consumes the same templates, enabling programmatic enforcement of exact‑text rules across automated workflows.

• api/generate-image.js — The backend endpoint that receives prompts (plain or JSON) and forwards them to the GPT‑Image2 model; constraints pass through this layer unchanged to the inference engine.

Summary

• Treat prompts as code: GPT‑Image2 requires explicit constraints rather than suggestions to guarantee text accuracy.
• Use the must‑show mantra: Always include "文字必须准确显示指定的中文，禁止乱码和占位文本" or equivalent constraints.
• Leverage JSON structures: The constraints field in JSON templates provides the most reliable enforcement mechanism.
• Lock titles explicitly: For posters, state that titles cannot be translated, shortened, or misspelled.
• Reference the templates: The docs/templates.md file in freestylefly/awesome-gpt-image-2 provides authoritative pattern implementations.

Frequently Asked Questions

What causes hallucinated or garbled text in GPT‑Image2 outputs?

Hallucinated text typically occurs when the model treats your input as a semantic description rather than a verbatim string to render. Without explicit constraints in docs/templates.md format, GPT‑Image2 may substitute characters with visually similar glyphs or generate placeholder boxes when uncertain about spacing, especially with Chinese characters that require precise Unicode rendering.

Can these exact‑text constraints work for languages other than Chinese?

Yes, while the repository examples focus on Chinese constraints like "文字必须准确显示指定的中文", the underlying mechanism applies to any language. You should adapt the constraint phrase to specify your target language (e.g., "text must display exactly as specified in English/French/Japanese") while maintaining the same forceful tone and placement within the prompt structure shown in docs/templates.md.

How do JSON prompts improve exact text generation compared to plain text?

JSON prompts provide a dedicated constraints field that separates formatting rules from content, reducing ambiguity. As shown in docs/templates.md (lines 24‑30), this structured approach ensures the model processes constraints as metadata rather than mixing them with descriptive content, resulting in higher compliance rates for verbatim text rendering.

Where should I store my custom templates to ensure the style library recognizes them?

Place custom templates in the docs/templates.md file or extend the agents/skills/gpt-image-2-style-library/SKILL.md skill definitions. The api/generate-image.js endpoint processes prompts from these sources, passing your constraints directly to GPT‑Image2 without modification, ensuring your exact‑text rules reach the model intact.

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