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

> Master Awesome-GPT-Image-2 prompts. Discover common failure modes and learn essential mitigation rules with this complete engineering guide. Enhance your prompt engineering skills now.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: how-to-guide
- Published: 2026-09-08

---

**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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md#tpl-ui) 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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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:**

```text
生成一张 UI 界面截图。

```

*Result:* Random platform, vague layout, unreadable text.

**Mitigated Prompt** (following `tpl-ui` constraints):

```text
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](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md#tpl-ui)

### Structuring Infographic JSON

**Bad JSON:**

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

```

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

**Mitigated JSON** (per `tpl-infographic` schema):

```json
{
  "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](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md#tpl-infographic)

### Locking Typography Poster Constraints

**Bad Prompt:**

```text
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):

```text
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](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/docs/templates.md#tpl-poster)

## Source Code Architecture

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

- **[`docs/templates.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/SKILL.md) exposes these rules for agent-based implementations, while [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/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.