Common Failure Modes and Mitigation Rules for Awesome-GPT-Image-2 Prompts: A Complete Engineering Guide

Awesome-GPT-Image-2 eliminates stochastic image generation failures through a dual-layer prompt architecture that enforces platform-specific constraints, deterministic JSON schemas, and explicit "avoid-pitfall" (避坑指南) guardrails across 21 template families.

The freestylefly/awesome-gpt-image-2 repository provides a structured prompt library designed to prevent the ambiguity that plagues text-to-image generation. By embedding mitigation rules directly into human-readable templates and machine-readable JSON schemas, the codebase addresses 14 recurring failure modes that degrade GPT-Image-2 output quality when constraints are unspecified.

The Dual-Layer Prompt Architecture

The repository splits its prompt library into two deterministic layers to ensure type safety and explicit constraint enforcement.

Human-Readable Text Templates

These "fill-in-the-blank" prompts serve immediate visual description needs. Each template in docs/templates.md includes a dedicated pitfall-guide section (避坑指南) that enumerates specific failure modes for that category. For example, UI templates contain strict mandates such as 文字必须绝对可读，必须显示指定的中文 (text must be absolutely readable and must display the specified Chinese characters).

Machine-Readable JSON Templates

The JSON schema layer provides deterministic field ordering and type safety for agent consumption. As implemented in the API client (src/apimartClient.js), these schemas prevent structural ambiguity by requiring fields like platform, aspect_ratio, and constraints at the root level, ensuring the generated prompt adheres to strict validation before reaching the image generation endpoint.

Critical Failure Modes and Mitigation Strategies

The repository documents 14 common failure modes across its template families. These failures fall into four architectural categories, each with specific mitigation rules enforced through the pitfall-guide sections in docs/templates.md.

Content Specification Failures

Failures occur when prompts lack explicit boundaries. Vague or under-specified instructions—such as "make a poster" without platform or layout details—produce random outputs. The mitigation rule requires locking platform, aspect-ratio, and layout first (e.g., "iOS, 9:16, card-based feed").

Unbounded module counts in infographics create cluttered diagrams. The mitigation caps modules at 3-5 items and fixes the diagram type (flow, comparison, timeline) in the structure.layout field. Overly verbose copy overwhelms visual hierarchy; mitigate by limiting body text to 1-2 sentences and locking the headline text while keeping the layout 单张海报 (single poster).

Uncontrolled text rendering generates garbled characters or missing Chinese text. The mitigation in the UI template mandates explicit directives: 文字必须绝对可读，必须显示指定的中文 alongside exact string specifications.

Platform and Context Integrity Errors

Cross-platform feature bleed occurs when UI prompts mix platform-specific elements (e.g., Twitter check-marks in TikTok screenshots). Mitigate by specifying platform-specific UI cues: "X has blue-check badge; 抖音 shows 音乐碟片" as documented in the tpl-ui avoid-pitfall list.

Historical anachronisms introduce modern objects into era-specific scenes (e.g., smartphones in Tang-dynasty settings). The mitigation explicitly bans modern elements and locks era-specific clothing and architecture references.

Perspective distortion in architectural renders collapses when viewpoints are unspecified. The mitigation fixes the viewpoint using Eye-level perspective and specifies lighting contrasts (冷暖光对比).

Narrative static scenes result from missing action verbs, causing the model to render static landscapes instead of scenes with conflict. Mitigate by including explicit verbs and conflict descriptors such as 正在崩塌 (collapsing) or 刚点燃火把 (just lit torch).

Aesthetic and Material Consistency Failures

Mismatched style versus subject occurs when concept-font prompts receive generic illustrations instead of typographic focus. The mitigation enforces title dominance: 标题必须是主视觉结构，必须完整拼写 (the title must be the main visual structure and must be fully spelled out).

Inconsistent lighting and materials produce flat product shots lacking sheen or rim light. Mitigate by stacking material and lighting keywords (e.g., 柔光 + 轮廓光).

Over-perfect realism creates synthetic-looking photography with glossy, artificial faces. The mitigation adds imperfection cues: skin pores, freckles, film grain, slight blur.

Brand identity inconsistency manifests through color clashes or logos without context. The mitigation mandates a brand-handbook approach including color HEX codes and a "never-do" list for the brand system.

Mixed-media confusion blends unrelated styles (e.g., cartoon overlays on scientific posters). Mitigate by stating single output only and listing disallowed styles in the constraints field.

Technical Constraint Violations

Missing resolution and aspect constraints produce low-resolution or incorrectly oriented outputs. The mitigation, present in various template constraint lines (e.g., UI JSON and Photo JSON), requires explicit declarations like 8K, 9:16 in the output parameters.

The Three-Step Guardrail Pattern

All awesome-gpt-image-2 prompts follow a mandatory three-step architectural pattern to prevent the failure modes above:

1. Define the core visual subject (type, platform, layout) — This anchors the generation and prevents vague outputs.
2. Specify stylistic modifiers (theme, colors, lighting, material) — These drive the aesthetic direction.
3. Lock output constraints (readable text, resolution, aspect ratio, no-extra-elements) — These keep results usable and deterministic.

When any step is omitted, the model's stochastic nature triggers the documented failure modes. The "avoid-pitfall" sections in docs/templates.md essentially validate that each step is present and precise.

Practical Implementation Examples

The following examples demonstrate bad prompts versus mitigated prompts using the repository's constraint system.

Fixing Vague UI Prompts

Bad Prompt:

生成一张 UI 界面截图。

Result: Random platform, vague layout, unreadable text.

Mitigated Prompt (following tpl-ui constraints):

为[产品类型]生成一张[平台，如 iOS]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[蓝色]，强调色[橙色]。  
布局：[顶部导航]，信息层级清晰，留白充足。  
**强制文字锁定**：文字必须绝对可读，必须显示指定的中文。  
输出：高保真 UI 截图，文字清晰可读，比例[9:16]。  

Reference: UI Template – Avoid Pitfalls

Structuring Infographic JSON

Bad JSON:

{
  "type":"Infographic",
  "topic":"健康"
}

Result: Model decides layout arbitrarily, potentially producing crowded text with unlimited modules.

Mitigated JSON (per tpl-infographic schema):

{
  "type":"Infographic",
  "topic":"老年人日常健康管理指南",
  "audience":"65‑80 岁中国城市老年人",
  "structure":{
    "title_area":"健康管理总览",
    "layout":"流程图",
    "modules":[
      {"title":"饮食", "icon":"fork-knife", "text":"每日三餐均衡"},
      {"title":"运动", "icon":"run", "text":"每天30分钟快走"},
      {"title":"用药", "icon":"pill", "text":"按时服药"}
    ]
  },
  "style":{
    "aesthetic":"专业报告",
    "colors":"低饱和蓝绿",
    "background":"浅色纸纹"
  },
  "constraints":"模块数量 ≤5，文字必须可读，禁止杂乱背景"
}

Reference: Infographic Template – Pitfall Guidance

Locking Typography Poster Constraints

Bad Prompt:

设计一张海报，标题是“未来”。  

Result: Generic word-art, potentially illegible or with mismatched style.

Mitigated Prompt (per tpl-poster concept-font rules):

Create ONE finished premium conceptual typography poster for the exact title:  
"[未来]"  

单张海报，禁止 moodboard、网格排版、说明文字、过程稿。  
**标题必须是主视觉结构**：巨大、可读、拼写完全正确。  
**锁定视觉风格**：高端编辑海报，黑白配色，使用 4‑6 色调系统。  
**避免**：通用字效、3D 字体、随机图标、杂乱拼贴。  

Reference: Concept-Font Poster – Avoid Pitfalls

Source Code Architecture

Understanding the repository structure is essential for implementing these mitigation rules effectively:

• docs/templates.md — Central repository containing all prompt templates and associated avoid-pitfall (避坑指南) sections that enumerate specific failure modes and their mitigations.
• src/apimartClient.js — Implements the API client that transmits generated prompts to APIMart; ensures output formats match server expectations for JSON schema validation.
• agents/skills/gpt-image-2-style-library/SKILL.md — Exposes the prompt library as an agent skill, reinforcing deterministic JSON requirements for automated systems.
• data/style-library.json — Runtime representation of style categories referenced by the website and skill implementations; useful for debugging template name mismatches.

Summary

• Awesome-GPT-Image-2 prevents generation failures through a dual-layer architecture of human-readable templates and deterministic JSON schemas documented in docs/templates.md.
• The 14 common failure modes span specification vagueness, uncontrolled text rendering, cross-platform UI bleed, historical anachronisms, and aesthetic mismatches.
• Mitigation rules enforce platform-specific constraints, readable text requirements (文字必须绝对可读), bounded module counts (3-5 items for infographics), and explicit aspect ratio locks.
• The three-step pattern (core subject → stylistic modifiers → output constraints) provides the architectural backbone for reliable prompts.
• Implementation requires referencing specific template sections (e.g., #tpl-ui, #tpl-infographic, #tpl-poster) to apply the correct guardrails.

Frequently Asked Questions

What causes text rendering failures in GPT-Image-2 prompts?

Text rendering fails when prompts lack explicit readability constraints. According to the UI template avoid-pitfall clause in docs/templates.md, you must add the directive 文字必须绝对可读，必须显示指定的中文 and list exact strings that must appear. Without this forced text lock, the model generates garbled characters, missing Chinese, or generic placeholders.

How does the JSON schema prevent infographic clutter?

The schema caps the module count and fixes the diagram type. As specified in the tpl-infographic section, the mitigation rule requires setting "layout" to a specific type (flow, comparison, timeline) and including the constraint "模块数量 ≤5" in the constraints field. This prevents the model from craming unlimited elements into a single diagram, ensuring visual clarity.

Where are the mitigation rules documented in the repository?

All mitigation rules reside in docs/templates.md, where each template family (UI, Poster, Product, Architecture, etc.) contains an "avoid-pitfall" (避坑指南) subsection. Additionally, agents/skills/gpt-image-2-style-library/SKILL.md exposes these rules for agent-based implementations, while data/style-library.json provides the runtime style categories referenced by the validation logic.

What is the "three-step pattern" for prompt safety?

The three-step pattern is the architectural guardrail system used across all awesome-gpt-image-2 prompts: 1) Define the core visual subject (platform, layout) to anchor the generation, 2) Specify stylistic modifiers (colors, lighting, materials) to drive aesthetics, and 3) Lock output constraints (resolution, readable text, aspect ratio) to ensure usability. Omitting any step triggers the stochastic failure modes documented in the repository's pitfall guides.

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