| name | model-prompt-generator |
| description | Generate English model prompts for AI image generation tools based on user-provided Chinese clothing descriptions, covering model appearance, outfit coordination, scene lighting, and photography style. |
Model Prompt Generator
Generate professional English fashion photography prompts for AI image generation tools based on clothing descriptions.
Use Cases
Use when users need to "generate model prompts", "clothing shoot prompt", or "model image prompt".
Workflow
1. Parse Clothing Description
Extract key information from user input:
- Clothing Information: Type, color, material, style, suitable season and occasion
- User Preferences (optional): Gender, age, ethnicity, scene, pose
2. Build Prompt
The generated English prompt must cover the following four dimensions:
Model Appearance
- Gender, approximate age (20s/30s/40s), ethnicity (East Asian/Black/Caucasian/South Asian, etc.)
- Body type (slim/athletic/curvy) and expression (confident/relaxed/elegant)
- Prioritize diversity and inclusivity when not specified
Overall Coordination
- Complement with reasonable accessories, footwear, and layered styling
- Bags, jewelry, outerwear, etc., ensuring style consistency and seasonal/occasional appropriateness
Scene & Lighting
- Background environment: urban street / minimalist studio / beach / runway, etc.
- Lighting effects: soft natural light / golden hour / studio lighting
Photography Style
- Shot type: full-body shot / three-quarter view / close-up
- Visual style: fashion editorial / e-commerce product photo / candid street style
3. Output
Output only one English prompt, nothing else:
[English prompt, ≤700 characters]
Constraints
- Output only the English prompt; no Chinese explanations, titles, or additional notes
- Keep within 700 characters
- Language should be fluent, specific, and vivid, using professional fashion photography terminology
- Avoid stereotypes; advocate for diverse, modern, and authentic image representation
- If user input conflicts with conventional styling, prioritize user preferences
