Pitfalls in GPT Image Prompt Templates: A Complete Guide from awesome-gpt-image-2

The awesome-gpt-image-2 repository documents critical pitfalls in prompt templates across 11 categories, requiring explicit platform cues, exact camera parameters, and strict layout constraints to prevent low-quality or inaccurate image generation.

Managing prompt templates for GPT-image models demands more than creative descriptions. The docs/templates.md file in the awesome-gpt-image-2 repository serves as the central source for "industrial-grade" templates, each accompanied by a "避坑指南" (pitfall guide) that identifies common errors causing model hallucinations or generic outputs. These guidelines emphasize technical specificity over vague adjectives to ensure consistent, high-fidelity results.

Interface and Information Design Pitfalls

UI and infographic templates fail when designers omit platform-specific constraints or overload layouts with excessive modules.

Mobile UI and Platform Cues

Vague instructions trigger "乱排版" (chaotic layout) in generated interfaces. The source code in docs/templates.md mandates specifying platform + aspect ratio + layout upfront. For example, X (Twitter) interfaces require a blue-check indicator, Douyin feeds need a music disc overlay, and Xiaohongshu demands waterfall-style columns. Omitting these cues produces 混搭 (mixed-style) images that combine incompatible platform elements.

Live-stream interfaces must first lock the stream type (commerce versus talent) before adding details. Special screen ratios—such as car displays requiring 21:9—must be stated explicitly; otherwise, the model defaults to standard mobile 9:16 proportions.

Infographic Module Control

Information visualization templates require explicit module count limits to prevent overcrowding. The pitfall guide warns against exceeding the specified number of modules or chart types. Additionally, copy must remain concise using short sentences only; long text blocks overflow the generated layout and render the visualization unreadable.

Marketing and Brand Visual Pitfalls

Product and poster templates degrade when prompts lack material specifications or enforce generic styling.

Poster Typography Requirements

Posters fail when titles are sloppy or vague. The template documentation requires titles to be exact, dominant, and readable to avoid "word art" aesthetics. Specifically, prompts must exclude requests for glossy 3D lettering, random icons, stock-photo realism, or mood-board styles that generate multiple drafts instead of a single cohesive poster.

Product Photography Standards

E-commerce images appear as "摊位货" (stall-quality goods) when prompts omit material + lighting specifications. The pitfall guide restricts promotional copy to 1-2 core lines only; flooding the image with text degrades visual quality. For beauty recommendations, prompts must first analyze skin tone, personality, and lip-base; otherwise, the model generates unrelated color swatches instead of relevant product visualizations.

Brand Identity Constraints

Brand templates require reduction first—defining keywords before requesting visuals prevents unfocused results. The documentation enforces a pure white background specification to enable easy cut-out later. Mixed-style moodboards are prohibited; prompts must request a single poster only to maintain visual coherence.

Technical Photography and Style Parameters

Photorealistic and artistic outputs fail without precise technical parameters or intentional imperfections.

Camera Parameters and Imperfections

Photography templates require exact camera parameters (e.g., "f/1.4", "50mm") rather than vague adjectives. The pitfall guide recommends adding imperfections such as skin pores, film grain, and slight blur to prevent over-polished, fake-looking images. Over-clean and over-composed shots trigger unrealistic generation; the model produces better results when prompts specify natural imperfections and avoid perfect symmetry.

Architectural Perspective Control

Architecture templates demand controlled perspective specifications, specifically "Eye-level perspective," to avoid distorted drawings. Lighting balance requires explicit cold versus warm temperature definitions to achieve premium spatial rendering.

Brush Style and Master References

Illustration templates must lock brush style explicitly; without this constraint, outputs default to "塑料风" (plastic-style) aesthetics. When referencing master artists, prompts should cite style traits rather than artist names to avoid direct copying while maintaining the desired aesthetic.

Character and Narrative Consistency

Character and historical templates produce errors when facial details or era constraints remain unspecified.

Facial Detail Specification

Character templates fail with vague descriptions like "很美的女孩" (very beautiful girl). The pitfall guide requires specifying eye shape, nose structure, and eyebrow type explicitly. Clothing material (silk, tech-fabric) must be stated to add depth and realism. For action sheets, prompts must lock the grid layout and repeat character specifications across frames; otherwise, the model alters faces or outfits between frames.

Historical Era Accuracy

Historical templates require fixing the specific era (唐, 宋, 明) to prevent anachronistic mixing. Prompts must include the explicit prohibition "No modern elements" to ensure historical accuracy and prevent contamination by contemporary objects or styles.

Scene Narrative Requirements

Scene templates require a verb/action specification to prevent static landscape generation. Camera language (low angle, dutch angle) must be defined to inject drama and narrative momentum into the composition.

Implementing Pitfall Guards in Prompts

The docs/templates.md file demonstrates embedding these constraints directly into template structures:

# Example: UI for a Douyin feed screenshot

为[产品类型]生成一张[平台，如 iOS/Android/Web]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/科技/拟物]，主色[颜色]，强调色[颜色]。
布局：[顶部导航/双栏/卡片流]，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[9:16]。

# Pitfall guards

- 明确平台特征：X(蓝勾)、抖音(音乐碟片)、小红书(双列瀑布流)。
- 强制文字锁定：文字必须绝对可读，禁止乱码。
- 中空屏比例锁定：若为车机，写明21:9。

{
  "type": "Poster",
  "title": "【标题】",
  "subtitle": "【副标题】",
  "constraints": [
    "No generic word art",
    "No glossy 3D lettering",
    "No moodboard"
  ]
}

# Example: Historical Tang dynasty scene

生成[朝代/古风设定]题材画面，主题为[主题]。
人物：[身份/服饰/器物]，场景：[宫廷/市井/山水]。
美术风格：[工笔/写意/影视写实]，色调：[色调]。
文化细节：[纹样/礼制/建筑要素]。
约束：不要出现现代元素（No modern elements）。

These examples from the repository demonstrate how technical constraints prevent the common errors documented in the "避坑指南" sections.

Summary

• The "避坑指南" in docs/templates.md provides category-specific checklists for 11 template types including UI, product, photography, and historical scenes.
• Platform specificity prevents mixed-style outputs; always specify exact ratios (9:16, 21:9) and platform cues (blue-check for X, music disc for Douyin).
• Technical precision overrides creative description; use exact camera parameters (f/1.4, 50mm) and material specifications rather than vague adjectives.
• Layout constraints require explicit module counts, grid locks for characters, and concise copy to prevent overflow or chaotic composition.
• Historical accuracy demands era fixation (唐, 宋, 明) and explicit prohibitions against modern elements.

Frequently Asked Questions

What is the "避坑指南" in awesome-gpt-image-2?

The "避坑指南" (pitfall guide) is a standard section following each prompt template category in the repository's docs/templates.md file. It documents common mistakes that cause GPT-image models to generate low-quality, inaccurate, or stylistically inconsistent results, providing preventive constraints for each template type.

Why do GPT-image prompts fail without platform-specific cues?

Without explicit platform cues—such as the blue-check for X (Twitter), music disc for Douyin, or waterfall columns for Xiaohongshu—the model produces 混搭 (mixed-style) images that combine incompatible visual elements from different platforms. The repository emphasizes locking these cues first, before adding content details, to ensure the generated interface matches the intended platform's native design language.

How do you prevent character inconsistency in animation prompt templates?

To maintain character consistency across animation frames, the template documentation requires locking the grid layout and repeating exact facial specifications (eye shape, nose, eyebrows) and clothing materials (silk, tech-fabric) for every frame. Vague descriptions like "beautiful girl" cause the model to alter facial features or outfits between frames, while explicit material and morphological details ensure coherent character rendering.

Where are the prompt template pitfalls documented in the repository?

All prompt template pitfalls are documented in docs/templates.md, which serves as the central source file for the awesome-gpt-image-2 project. Supporting implementation examples appear in shared/apimart.js, while the README.md provides navigation links to the full template documentation.

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    "instagit": {
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}