A comprehensive collection of custom ComfyUI nodes for generating culturally diverse AI image prompts. This project features a fully data-driven architecture with gender-aware attire filtering, comprehensive body part coverage, and intelligent prompt generation optimized for AI image models.

✨ Key Features

🌍 Cultural Diversity & Authenticity

Multi-Country Support: Dynamic nodes for countries with extensive cultural data
Regional Variations: Detailed appearance options for different regions within countries
Cultural Context: Activities, festivals, landmarks, and traditional colors
Authentic Attire: Culturally accurate clothing options with detailed descriptions

👕 Advanced Attire System

Gender-Aware Filtering: Clothing options automatically filter based on selected gender
Comprehensive Body Coverage: Support for 18+ body parts including:
- Arms: Forearm, Hands, Palms, Upper_Arm, Wrists
- Head: Head, Chin, Ears, Forehead, Hair, Nose
- Legs: Legs, Ankles, Feet
- Upper_Body: Upper_Body, Chest, Shoulders
- Waist: Waist area clothing
Rich Descriptions: Each attire item includes detailed descriptions for AI prompt generation
Smart Randomization: Intelligent random selection with contextual awareness

🤖 AI-Optimized Prompt Generation

Multiple Prompt Styles: AI-friendly, creative, artistic, technical, and balanced modes
Smart Defaults: Intelligent parameter combinations that work well together
Contextual Suggestions: Weather-activity and emotion-setting pairings
Technical Enhancements: Quality keywords, composition rules, and lighting optimization
LLM Integration: Optional prompt rewriting with local language models

🎛️ Intuitive Controls

Simplified UI: Clear, logical control grouping with helpful tooltips
Universal Options: Every dropdown includes "none" and "random" options
Smart Randomization: Four levels of randomization (off, light, moderate, full)
Reproducible Results: Seed-based consistency for repeatable outputs
Gender-Aware Interface: Attire options automatically filtered by gender selection

📊 Data-Driven Architecture

Fully Modular: All data stored in organized JSON files
Zero Hardcoding: No attire, cultural, or prompt data in Python code
Easy Extension: Add new countries, body parts, or attire by updating JSON files
Robust Loading: Error-tolerant JSON loading with graceful fallbacks

📁 Project Structure

comfyui-globetrotter/
├── data/                          # Organized data files
│   ├── attire/                    # Clothing and accessories by body part
│   │   ├── arms/                  # Forearm, hands, palms, upper_arm, wrists
│   │   ├── head/                  # Head, chin, ears, forehead, hair, nose
│   │   ├── legs/                  # Legs, ankles, feet
│   │   ├── upper_body/            # Upper_body, chest, shoulders
│   │   └── waist/                 # Waist area clothing
│   ├── appearance/                # Regional appearance data
│   ├── poses/                     # Country-specific poses
│   ├── cultural/                  # Cultural activities, festivals, landmarks
│   ├── config/                    # System configuration
│   │   ├── gender_config.json     # Gender mapping and filtering rules
│   │   └── prompt_config.json     # Prompt generation settings
│   ├── generation/                # AI prompt optimization
│   │   ├── smart_defaults.json    # Intelligent default combinations
│   │   ├── contextual_suggestions.json  # Context-aware suggestions
│   │   └── ai_prompt_structure.json     # AI-optimized prompt structure
│   ├── ui/                        # User interface data
│   │   ├── dynamic_node_options.json    # UI dropdown options
│   │   ├── weather_moods.json           # Atmospheric conditions
│   │   └── complementary_colors.json    # Color palette suggestions
│   ├── styles/                    # Artistic and photographic styles
│   └── prompts/                   # LLM prompt templates
├── globetrotter_nodes/            # Core Python modules
│   ├── dynamic_nodes.py           # Main dynamic node generation
│   ├── ollama_llm_node.py         # LLM integration node
│   └── text_combiner_node.py      # Text utility node
└── requirements.txt               # Python dependencies

🚀 Installation

Clone the repository into your ComfyUI custom_nodes directory:

cd /path/to/ComfyUI/custom_nodes
git clone <repository-url> comfyui-globetrotter

Install dependencies (optional but recommended for full features):
```
cd comfyui-globetrotter
pip install -r requirements.txt
```
Restart ComfyUI to load the new nodes.

💡 Usage

Dynamic Country Nodes

Each country automatically gets its own node (e.g., "India Attire") with comprehensive options:

Core Parameters

Age: Young adult, Adult
Gender: Female, Male (with automatic attire filtering)
Region: Country-specific regions (e.g., Java, Punjab, Rajasthan)
Hair Style: Generic options + region-specific defaults
Emotion: Confident, Serene, Joyful, Contemplative, etc.

Attire Selection (Gender-Filtered)

Individual dropdowns for each body part with culturally appropriate options:

Arms: Forearm decorations, hand accessories, wrist jewelry
Head: Headwear, face decorations, ear accessories
Upper Body: Traditional tops, chest accessories, shoulder pieces
Lower Body: Traditional bottoms, leg wear, foot attire
Waist: Belts, sashes, waist decorations

Cultural Context

Poses: Traditional and cultural poses
Cultural Elements: Festivals, activities, traditions
Settings: Markets, temples, landmarks, urban areas
Atmosphere: Weather, lighting, mood combinations

Advanced Controls

Detail Level: Basic, Detailed, Cinematic, Artistic, Photorealistic
Composition: Rule of thirds, Centered, Close-up, Wide shot, etc.
Prompt Optimization: AI-friendly, Creative, Technical, Artistic, Balanced
Randomization: Off, Light, Moderate, Full (with smart contextual choices)
Custom Elements: LoRA triggers, custom prompts
Experimental: AI-powered prompt rewriting (requires transformers library)

Example Attire JSON Structure

`data/attire/upper_body/in.json`

{
  "country": "IN",
  "body_part": "upper_body",
  "attires": [
    {
      "name": "Saree Blouse",
      "type": "clothing",
      "description": "A fitted upper garment worn under a saree, often short-sleeved or sleeveless, and tailored to match the saree.",
      "material": ["cotton", "silk", "synthetic"],
      "region": ["Nationwide"],
      "gender": ["female"],
      "occasion": ["daily wear", "wedding", "festival"]
    },
    {
      "name": "Kurta",
      "type": "clothing",
      "description": "A loose-fitting, long tunic worn by both men and women, often paired with churidar or jeans.",
      "material": ["cotton", "silk", "linen"],
      "region": ["Nationwide"],
      "gender": ["male", "female", "unisex"],
      "occasion": ["daily wear", "casual", "formal"]
    }
  ]
}

`data/appearance/in.json`

{
  "country": "IN",
  "regions": [
    {
      "name": "Punjab",
      "description": "People from Punjab. Features include wheat-colored to medium brown skin and strong facial structure.",
      "skin_tone": "wheat-colored to medium brown",
      "hair": "black, thick and wavy"
    },
    {
      "name": "South India",
      "description": "People from Tamil Nadu, Kerala, Karnataka, and Andhra Pradesh. Features include dark to very dark skin.",
      "skin_tone": "dark to very dark brown",
      "hair": "black, thick and curly"
    }
  ]
}

Gender-Aware Attire Filtering

The system automatically filters attire options based on the selected gender:

Female: Shows items with gender: ["female"] or gender: ["unisex"]
Male: Shows items with gender: ["male"] or gender: ["unisex"]
Validation: Inappropriate combinations are automatically skipped during prompt generation

Utility Nodes

Text Combiner Node

Combines multiple text inputs into a single formatted string
Useful for complex prompt construction workflows

Ollama LLM Node

Advanced prompt enhancement using local Ollama language models
Includes artistic styles, camera settings, lighting options
Dynamic loading of style configurations from JSON files

Smart Prompt Generation

AI-Optimized Output Examples

AI-Friendly Mode:

Highly detailed, best quality, rule of thirds composition, A young adult female from Punjab, India, with a confident expression, wearing Saree Blouse: A fitted upper garment worn under a saree, often short-sleeved or sleeveless, and Churidar: Traditional fitted trousers, atmosphere: golden hour

Creative Mode:

Rule of thirds composition, Highly detailed, A young adult female from the Punjab region of India, wearing Saree Blouse: A fitted upper garment worn under a saree and Churidar: Traditional fitted trousers, with a confident expression, color palette: warm earth tones, golden natural colors, atmosphere: golden hour

🔧 Adding New Content

Adding a New Country

Create country entry in data/countries.json:

{
  "name": "Country Name",
  "code": "cc",
  "flag": "🇨🇨"
}

Add appearance data in data/appearance/cc.json:

{
  "country": "cc",
  "regions": [
    {
      "name": "Region Name",
      "description": "Physical description",
      "skin_tone": "skin tone description",
      "hair": "hair description"
    }
  ]
}

Create attire files in appropriate body part directories:
- data/attire/head/cc.json
- data/attire/upper_body/cc.json
- data/attire/legs/cc.json
- etc.
Add cultural context (optional):
- data/poses/cc.json
- data/cultural/cc.json
Restart ComfyUI to load the new country node.

Adding New Attire Items

Edit the appropriate JSON file (e.g., data/attire/head/in.json)

Add new attire object:

{
  "name": "Attire Name",
  "type": "clothing",
  "description": "Detailed description for AI prompts",
  "material": ["cotton", "silk"],
  "region": ["Region1", "Region2"],
  "gender": ["male", "female", "unisex"],
  "occasion": ["daily wear", "formal", "festival"]
}

Restart ComfyUI to load the new options.

Extending Body Part Coverage

Create new directory under data/attire/ (e.g., accessories/)
Add country-specific JSON files with the new body_part field
System automatically detects and includes new body parts in UI

⚡ Advanced Features

Experimental LLM Rewriting

Toggle Option: Each node includes experimental_llm_rewrite
Local Models: Uses Hugging Face distilgpt2 model when available
Smart Filtering: Automatic repetition detection and removal
Length Control: Output length limits based on prompt optimization mode
Graceful Fallback: Silently skips if dependencies are missing

Intelligent Randomization

Smart Random Mode: Context-aware random selections
Seed Control: Fixed, increment, decrement, or system random
Contextual Pairing: Weather-activity and emotion-setting combinations
Optimal Defaults: Age-gender combinations that work well together

Prompt Optimization Modes

Mode	Purpose	Style
AI-Friendly	Stable Diffusion, FLUX	Quality keywords first, clear structure
Creative	Artistic generation	Narrative flow, artistic language
Technical	Professional workflows	Precise technical terms
Artistic	Fine art creation	Museum-quality descriptions
Balanced	General purpose	Mix of technical and creative

Configuration System

`data/config/gender_config.json`

{
  "gender_mappings": {
    "male": ["male", "unisex"],
    "female": ["female", "unisex"],
    "non-binary": ["unisex"]
  }
}

`data/config/prompt_config.json`

{
  "technical_enhancements": {
    "detailed": "8k resolution, highly detailed",
    "cinematic": "professional photography, cinematic lighting"
  },
  "detail_prefixes": {
    "ai_friendly": {
      "detailed": "highly detailed, best quality, "
    }
  }
}

🛠️ Technical Details

Dynamic Node Generation

Factory Pattern: Nodes created programmatically for each country
Closure-Based: Input types capture country-specific data
Memory Efficient: Data loaded once and cached
Error Tolerant: Graceful handling of missing files

Gender Validation System

Runtime Filtering: Attire validated during prompt generation
File Discovery: Automatic detection of correct attire file paths
Cross-Reference: Body part mapping across directory structure
Fallback Options: Safe defaults when validation fails

Data Loading Architecture

Lazy Loading: JSON files loaded only when needed
Caching: Frequent data cached in memory
Error Recovery: Default values for missing files
Validation: Schema checking for critical fields

📋 Requirements

Core Dependencies

torch>=1.9.0              # PyTorch for tensor operations
torchvision>=0.10.0        # Computer vision utilities
transformers>=4.0.0        # Hugging Face transformers (for LLM features)
requests>=2.25.0           # HTTP requests for Ollama API

Optional Dependencies

accelerate                 # Faster model loading
safetensors               # Secure tensor serialization

Installation:

pip install -r requirements.txt

System Requirements

Python: 3.8 or higher
ComfyUI: Latest version recommended
Memory: 4GB+ RAM for LLM features
Storage: ~50MB for full dataset

🤝 Contributing

Guidelines

Follow JSON Schema: Maintain consistent data structure
Cultural Sensitivity: Ensure authentic and respectful representation
Test Additions: Verify new content works across gender combinations
Documentation: Update relevant documentation for new features

Code Style

Python: Follow PEP 8 conventions
JSON: Use 2-space indentation
Comments: Document complex logic and cultural context

Pull Request Process

Fork the repository
Create feature branch (git checkout -b feature/new-country)
Add your changes with appropriate tests
Update documentation
Submit pull request with detailed description

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Cultural Consultants: For authentic attire and cultural information
ComfyUI Community: For feedback and feature requests
Open Source Libraries: Transformers, PyTorch, and other dependencies
Contributors: Everyone who has helped expand the cultural database

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: This README and inline code comments

Made with ❤️ for the ComfyUI community