NBA-ComfyUINode

Version 1.2.1 - Dependency cleanup and archived LineSelector node

A comprehensive collection of custom nodes for ComfyUI, providing advanced image processing, workflow control, and utility functions to enhance your AI image generation workflows.

🚀 Features

• Flow Control Nodes: Advanced workflow management with gates, branches, and state management
• Image Processing: Specialized nodes for image manipulation, resizing, and enhancement
• File Management: Advanced image loading, saving, and organization tools
• Utility Nodes: Text processing, list management, and data conversion utilities
• Hugging Face Integration: Direct integration with HF models for captioning and control
• JavaScript Extensions: Enhanced UI components for better user experience

📦 Installation

Prerequisites

• ComfyUI installed and running
• Python 3.8 or higher
• Required dependencies (see requirements.txt)

Installation Steps

1. Clone the repository:

   git clone https://github.com/enternalsaga/NBA-ComfyUINode.git
   cd NBA-ComfyUINode
   

2. Copy to ComfyUI custom_nodes directory:

   cp -r NBA-ComfyUINode /path/to/ComfyUI/custom_nodes/
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Restart ComfyUI and the nodes will be automatically discovered and loaded.

🧩 Available Nodes

Flow Control Nodes

• NBA Flow Any Gate: Conditional workflow execution based on any input
• NBA Flow Branch: Multi-path workflow branching
• NBA Flow Gate: Simple conditional execution
• NBA Flow Multi Gate: Multiple condition evaluation
• NBA Flow Sequence: Sequential workflow execution
• NBA Flow Set State: State management for workflows
• NBA Flow Any Checker: Input validation and checking

Image Processing Nodes

• NBA Match Size: Resize images to match dimensions
• NBA Remove Banding Artifacts: Eliminate banding in generated images
• NBA Resize By Pixel Ratio: Proportional image resizing
• NBA Scale To Pixels: Precise pixel-based scaling
• NBA Colorize Depthmap: Convert depth maps to colored images
• NBA Kontext Diff Merge: Advanced image merging with context

File Management Nodes

• NBA Image Output: Advanced image saving with metadata
• NBA Image Output Adv: Enhanced image output with additional features
• NBA Load Image: Flexible image loading from various sources
• NBA Load Image Input: Advanced image input with folder support
• NBA Load Image Input Advanced: Enhanced image loading with subfolder support
• NBA Load Image From Folder: Batch image loading from directories
• NBA Save Image To Path: Direct image saving to specified paths
• NBA Extract File Names: Extract and process filenames
• NBA List Image Files: List and process image files
• NBA List Text Files: List and process text files

Utility Nodes

• NBA Any To Any: Universal data type conversion
• NBA Text Split By Delimiter: Split/truncate by characters, words, or delimiters with forward/backward slicing, start position, and smart numbering removal
• NBA List Info: Information extraction from lists
• NBA Image Filename Info: Extract metadata from image filenames
• NBA Process Tags: Tag processing and management
• NBA Save Text File To Path: Text file saving utilities
• NBA Sym Link: Symbolic link creation utilities

Hugging Face Integration

• NBA Hf Zen Ctrl: Zen control model integration
• NBA Hf Joy Caption: Joy captioning model
• NBA Hf Ic Light V2: IC Light V2 model integration
• NBA Hf Image2Body: Image to body conversion

Advanced Nodes

• NBA Kontext Inpainting Conditioning: Advanced inpainting with context
• NBA Multi Slider: Multi-value slider interface
• NBA Float Ramp: Float value ramping utilities
• NBA Dual Condition: Dual condition evaluation
• NBA OLM Dragcrop: Drag and crop functionality

🎯 Usage Examples

Basic Image Processing Workflow

# Load image and resize
image = LoadImageInput(image_path="input.jpg")
resized = MatchSize(image=image, target_size=(512, 512))

# Process and save
processed = RemoveBandingArtifacts(image=resized)
output = ImageOutput(
    image=processed,
    output_path="./output/",
    save_prefix="processed",
    file_type="png"
)

Advanced Flow Control

# Conditional processing based on image size
checker = FlowAnyChecker(condition="image_width > 512")
gate = FlowAnyGate(
    condition=checker,
    true_branch=ScaleToPixels(image=image, target_pixels=262144),
    false_branch=image
)

Batch Processing with Organization

# Load multiple images from folder
images = LoadImageFromFolder(
    folder_path="./input/",
    file_types=["jpg", "png"]
)

# Process each image with organized output
for image in images:
    processed = RemoveBandingArtifacts(image=image)
    ImageOutput(
        image=processed,
        output_path="./output/%date:yyyy-MM-dd%/",
        save_prefix="batch_%date:HHmmss%",
        prefix_sub="processed",
        number_padding=4
    )

📁 Project Structure

NBA-ComfyUINode/
├── __init__.py              # Auto-discovery and registration
├── nodes/                   # All node implementations
│   ├── Flow*.py            # Flow control nodes
│   ├── Image*.py           # Image processing nodes
│   ├── Load*.py            # Image loading nodes
│   ├── Hf*.py              # Hugging Face integration
│   └── *.py                # Utility and other nodes
├── js/                     # JavaScript UI extensions
│   ├── *.js               # UI enhancement scripts
│   └── *.js.backup        # Backup files
├── examples/               # Usage examples and tutorials
│   └── usage_examples.py  # Comprehensive examples
├── web/                    # Web assets
├── lists/                  # Configuration lists
├── requirements.txt        # Python dependencies
└── README.md              # This file

🔧 Configuration

Node Auto-Discovery

The project uses automatic node discovery. Simply place your node classes in the nodes/ directory with the required attributes:

• INPUT_TYPES: Define input parameters
• RETURN_TYPES: Define output types
• FUNCTION: Main processing function
• CATEGORY: Node category for organization

JavaScript Extensions

Custom UI components are automatically loaded from the js/ directory. These enhance the user experience with:

• Dynamic widgets
• Advanced input controls
• Drag-and-drop functionality
• Real-time preview updates

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Add your nodes to the nodes/ directory
4. Add any JavaScript extensions to the js/ directory
5. Update documentation and examples
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• ComfyUI community for the excellent framework
• Hugging Face for model integrations
• All contributors who have helped improve these nodes

📞 Support

• Issues: Report bugs and request features on GitHub
• Discussions: Join community discussions for help and ideas
• Documentation: Check the examples folder for detailed usage examples

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Happy Node Building! 🎨