Common Pitfalls and Antipatterns in GPT-Image2 Prompts: A Complete Guide

Avoid vague instructions, unambiguous text constraints, and missing negative prompts to prevent GPT-Image2 from generating garbled, unfocused, or compositionally broken images.

The freestylefly/awesome-gpt-image-2 repository ships an Industrial Prompt Templates & Pitfalls Guide that documents how ambiguous instructions and missing constraints cause GPT-Image2 to hallucinate layouts, scramble text, or blend incompatible visual styles. Understanding these antipatterns is essential for generating production-ready images that match your exact specifications.

General Antipatterns That Break Every Image Type

Several failure modes apply across all visual categories in GPT-Image2. These errors stem from leaving too much interpretation to the model rather than locking down specific constraints.

Vague High-Level Instructions

Providing generic commands like "Generate a poster" without specifying platform, aspect ratio, or layout forces the model to fill gaps with its own assumptions. According to the UI "避坑指南" (pitfall guide) in docs/templates.md, this often produces junk output or incorrect compositions that require complete regeneration.

Missing Explicit Text Constraints

Forgetting to lock the exact wording, language, or typography requirements leads to garbled characters, missing glyphs, or unrelated filler text. The guide emphasizes "强制文字锁定" (mandatory text locking) as a critical requirement in UI templates to ensure readability.

Failure to Specify Negative Prompts

Not stating what must not appear—such as "no cartoon style" or "no modern elements"—allows unwanted visual styles to sneak into the output. The repository's general guidelines repeatedly stress negative constraints as a required component of robust prompt engineering.

Unfixed Composition and Overloaded Prose

Leaving composition to the model (e.g., requesting "a poster" only) results in unfocused, crowded images missing a primary visual anchor. Conversely, inserting long paragraphs where a concise module list is required causes the model to treat the entire text as a single block, destroying hierarchy and readability.

Category-Specific Pitfalls in GPT-Image2 Prompts

Each visual domain handled by GPT-Image2 carries unique failure modes documented in specific "避坑指南" sections of docs/templates.md.

UI and Interface Mockups

The most frequent errors in user interface generation include providing only "platform + layout" without structural details, which produces scrambled screens. Mixing platform-specific traits—such as using Twitter-style verification icons in a TikTok mockup—creates unrealistic hybrid UIs that do not exist in production.

Infographics and Data Visualization

Uncontrolled module counts generate overly dense, illegible charts. Inserting long prose inside infographic modules destroys readability, while failing to specify graph types results in ambiguous visuals. The template requires strict "文案克制" (copy restraint) to maintain clarity.

Posters and Typography

Forgetting to lock exact title wording allows the model to alter spelling or replace text entirely. Using generic word-art or 3-D lettering sacrifices readability, and including moodboard references causes the model to return multi-panel outputs instead of a single cohesive poster.

Product and E-Commerce Imagery

Omitting material and lighting details produces flat, stock-photo aesthetics. Overcrowding promotional copy creates visual clutter, while failing to anchor the product's pose leads to inconsistent perspectives across generated assets.

Brand Identity and Logos

Skipping the pure-white background requirement makes asset extraction difficult later. Failing to restrict logo usage contexts or omitting "never-do" rules results in inconsistent brand systems and downstream usage violations.

Architectural Visualization

Missing eye-level perspective specifications cause distorted spatial relationships. Forgetting to specify cold-warm lighting contrast produces flat, unrealistic lighting environments.

Photography and Realism

Generic outputs result from omitting specific camera specifications (lens, aperture) or failing to request realistic imperfections such as skin pores or film grain.

Illustration and Character Design

Without stroke or medium constraints, illustrations default to a plastic-looking art style. For character sheets, omitting grid layouts for action poses creates cluttered outputs, while failing to lock facial features and clothing produces inconsistent character iterations.

How the Repository Structures Pitfall Prevention

The templates.md file at the repository root contains 21 core prompt templates, each terminating with a dedicated "避坑指南" section. The project's agent configuration in agents/skills/gpt-image-2-style-library/SKILL.md explicitly mandates that generated prompts include these pitfalls as a required component of every generation step.

The system locates these guidelines through data/style-library.json, which indexes the template document and maps each category to its corresponding pitfall section. This structured approach ensures that automated agents and human users alike reference the same antipattern database before executing generation tasks.

Production-Ready Prompt Checklist

Before submitting any GPT-Image2 prompt, verify the following constraints derived from the repository's industrial templates:

• Be explicit – Define platform, aspect ratio, layout, and primary visual anchor
• Lock text – Specify exact wording, language, and readability requirements
• Specify negative constraints – List styles, elements, and artifacts to avoid
• Limit modules – Keep chart modules, typography blocks, or UI components to reasonable counts
• Add realism cues – Include lens specs, lighting direction, material descriptors, and subtle imperfections
• Avoid style-only requests – Always pair style descriptors with clear composition instructions
• Request single-output – Include "single poster only" or "single-image output" to block moodboards and process sheets

Before and After: Prompt Refactoring Examples

The following examples from the repository demonstrate how applying pitfall guidelines transforms broken prompts into production-ready specifications.

# ❌ Bad Prompt (violates multiple antipatterns)

生成一张海报，主题是“未来城市”。要很酷，颜色好看。

This prompt fails on vagueness, missing text constraints, and lacks composition fixity.

# ✅ Good Prompt (structured UI template)

{
  "type": "UI Screenshot",
  "platform": "iOS",
  "product": "Smart Home App",
  "layout": "Top navigation + Card feed",
  "style": {
    "theme": "Dark Mode",
    "primary_color": "Neon Green",
    "typography": "Clean sans-serif"
  },
  "content": {
    "header": "Today's Energy Usage",
    "cards": [
      {"title": "Living Room", "data": "2.3 kW"},
      {"title": "Kitchen", "data": "1.7 kW"}
    ]
  },
  "constraints": "High fidelity, readable text, 9:16 aspect ratio"
}

This structured version locks the aspect ratio, specifies exact colors, and mandates text readability—directly addressing the UI "避坑指南" requirements.

# ✅ Good Infographic Prompt

生成一张信息图，主题为“老年人日常健康管理指南”。  
结构：标题区 + 4个模块（图标、短标题、1句说明），使用流程图风格。  
主色：柔和蓝，背景浅灰。  
约束：不使用长段文字，模块数量必须为4，避免重复视觉层级。

This example demonstrates "文案克制" by limiting prose and explicitly constraining module count to prevent density issues.

Summary

• Vague instructions cause GPT-Image2 to hallucinate layouts and compositions that do not match production requirements
• Text constraints must be explicit and locked to prevent garbled characters or spelling alterations
• Negative prompts are mandatory for preventing unwanted visual styles and anachronistic elements
• Category-specific pitfalls documented in docs/templates.md provide targeted guidance for UI, infographics, posters, and 13 other visual domains
• Structured templates with fixed composition and limited modules produce higher fidelity results than prose-heavy descriptions

Frequently Asked Questions

What is the most common mistake when writing GPT-Image2 prompts?

The most frequent error is providing vague high-level instructions without fixing composition or layout specifics. According to the repository's UI "避坑指南", commands like "generate a poster" force the model to assume aspect ratios, platform conventions, and content hierarchy, often resulting in unfocused or compositionally broken images.

How do I prevent text from becoming garbled in GPT-Image2 images?

Explicitly lock all text constraints including exact wording, language, and typography requirements. The guide's "强制文字锁定" (mandatory text locking) principle requires specifying readable fonts and exact character strings, particularly critical for UI mockups and poster designs where legibility is paramount.

Where are the pitfall guidelines documented in the repository?

All antipatterns and "避坑指南" sections reside in docs/templates.md, which contains 21 category-specific templates. The agent skill definition in agents/skills/gpt-image-2-style-library/SKILL.md references these guidelines, while data/style-library.json provides the machine-readable index linking each template to its corresponding pitfall section.

Why do my infographic prompts produce cluttered, unreadable results?

This typically stems from uncontrolled module counts and inserting long prose where concise data points are required. The infographic template requires strict "文案克制" (copy restraint) and explicit limits on module numbers to prevent density issues that reduce visual hierarchy and readability.

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