A comprehensive collection of utility nodes for ComfyUI designed to simplify complex workflow development without requiring numerous nodes for basic operations. This node pack focuses on providing essential utilities and advanced video generation capabilities, particularly for Wan2.1 models.

📋 Table of Contents

Installation
Node Categories
- PrzewodoUtils/Wan - Advanced Video Generation
- PrzewodoUtils - General Utilities
Node Reference
Features
Requirements
Contributing
License

🚀 Installation

Method 1: ComfyUI Manager (Recommended)

Install ComfyUI Manager
Search for "ComfyUI-Przewodo-Utils" in the manager
Install directly from the interface

Method 2: Manual Installation

cd ComfyUI/custom_nodes
git clone https://github.com/przewodo/ComfyUI-Przewodo-Utils.git

Method 3: ComfyUI Registry

Available on the ComfyUI Registry for easy installation.

📂 Node Categories

PrzewodoUtils/Wan - Advanced Video Generation

The PrzewodoUtils/Wan category contains specialized nodes for advanced video generation, particularly optimized for Wan2.1 models with cutting-edge features and optimizations.

🎬 Core Video Generation

WanImageToVideoAdvancedSampler

Purpose: Advanced image-to-video generation with comprehensive quality preservation
Key Features:
- Multi-Chunk Generation: Generate videos longer than model limits by sequencing multiple chunks
- Quality Preservation: Advanced temporal coherence and latent space continuity between chunks
- Dual Sampler Support: High CFG + Low CFG sampling for superior quality
- TeaCache Integration: Accelerated inference with intelligent caching
- SageAttention: Optimized attention computation for memory efficiency
- Skip Layer Guidance: Enhanced generation quality through selective layer processing
- TAESD Preview: Fast latent space previews during generation
- Model Support: GGUF and Diffusion models with multiple weight types
Quality Preservation Features:
- Temporal overlap frames for smooth transitions
- Latent space blending between chunks
- Artifact reduction with gaussian blur
- Color matching for consistency
- Multi-frame averaging for continuity
Advanced Options:
- CausVid LoRA integration
- Block swap optimization
- Model shift for stability
- CFG Zero Star optimization

🔧 Video Processing & Enhancement

WanVideoEnhanceAVideo

Purpose: Video enhancement and post-processing
Features: Temporal consistency, quality improvement, artifact reduction

WanVideoVaeDecode

Purpose: Specialized VAE decoding for video latents
Features: Optimized for video sequences, memory efficient processing

WanFirstLastFirstFrameToVideo

Purpose: Convert start/end frames to video sequences
Features: Intelligent frame interpolation, smooth transitions

🎯 Specialized Selectors & Utilities

WanModelTypeSelector

Purpose: Smart model type selection (GGUF vs Diffusion)
Features: Automatic optimization based on model type

WanVideoGenerationModeSelector

Purpose: Choose between different video generation modes
Options: Start image, end image, start+end, start+end+start modes

WanGetMaxImageResolutionByAspectRatio

Purpose: Calculate optimal resolutions for Wan models
Features: Aspect ratio preservation, model-specific sizing

📊 Advanced Workflow Tools

WanVideoLoraStack

Purpose: Manage multiple LoRAs for video generation
Features: Stack management, strength control, selective application

WanPromptChunkStacker

Purpose: Advanced prompt management for multi-chunk generation
Features: Chunk-specific prompts, automatic prompt cycling, LoRA integration per chunk

PrzewodoUtils - General Utilities

The PrzewodoUtils category provides essential utility nodes for streamlined workflow development.

🔢 Logic & Comparison

CompareNumbersToCombo

Purpose: Compare two numbers and return different strings based on result
Comparisons: ==, !=, <, >, <=, >=
Use Cases: Conditional logic, dynamic prompt selection

SwapAnyCondition

Purpose: Conditionally swap any two values
Features: Universal type support, boolean-based switching

SwapAnyComparison

Purpose: Swap values based on comparison results
Features: Numeric comparison with value swapping

SwapImageComparison

Purpose: Specialized image swapping based on conditions
Features: Image-specific comparison and swapping

🖼️ Image Processing

ImageScaleFactor

Purpose: Calculate scale factors for image resizing
Features: Maintain aspect ratios, precise scaling calculations

ImageSizer

Purpose: Advanced image sizing with multiple modes creating empty latent for each type of model
Features: Controls the aspect ratio of the empty latent to be generated.

BatchImagesFromPath

Purpose: Load multiple images from filesystem paths
Features: Pattern matching, recursive directory scanning, batch processing

🔧 Data Management

AppendToAnyList

Purpose: Dynamically append items or merge arrays to lists
Features:
- Single item appending
- Array merging
- List extension
- Type-agnostic operation

DebugLatentShapes

Purpose: Debug and display latent tensor dimensions
Features: Shape analysis, memory usage reporting

🎛️ Control Flow

IsInputDisabled

Purpose: Check if workflow inputs are disabled
Features: Conditional execution, input validation

FloatIfElse

Purpose: Conditional float value selection
Features: Boolean-based float switching

HasInputValue

Purpose: Verify if inputs contain valid values
Features: None checking, input validation

✨ Features

🎥 Advanced Video Generation

Multi-Chunk Support: Generate videos longer than model limits
Quality Preservation: Maintain consistency across video chunks
Temporal Coherence: Smooth transitions between sequences
Model Optimization: Support for both GGUF and Diffusion models

⚡ Performance Optimizations

TeaCache: Intelligent caching for faster inference
SageAttention: Memory-efficient attention computation
Block Swap: Dynamic memory management
TAESD Preview: Fast latent previews

🔄 Workflow Efficiency

Universal Type Support: any_type compatibility
Smart Selectors: Intelligent model and mode selection
Batch Processing: Efficient multi-item operations
Debug Tools: Built-in debugging and analysis

🎯 User-Friendly Design

Advanced Tooltips: Comprehensive parameter descriptions
Organized Categories: Logical node grouping
Error Handling: Robust error reporting and recovery
Flexible Configuration: Extensive customization options

📋 Requirements

Essential Dependencies

ComfyUI: Latest version recommended
PyTorch: GPU support recommended
Python: 3.8+ required

Required Enhancements

TeaCache: teacache>=1.7.0 for accelerated inference
ComfyUI-KJNodes: comfyui-kjnodes>=1.0.0 for enhanced functionality
ComfyUI-GGUF: comfyui-gguf>=1.0.0 for GGUF model support

🎯 Use Cases

Video Generation Workflows

Long-form Video Creation: Multi-chunk generation for extended sequences
Image-to-Video Conversion: Transform static images into dynamic videos
Quality-Focused Generation: Maintain consistency across long sequences

Utility Workflows

Conditional Logic: Dynamic workflow behavior based on comparisons
Batch Processing: Handle multiple images or data efficiently
Data Management: Organize and manipulate workflow data

Advanced Workflows

Model Optimization: Leverage different model types efficiently
Memory Management: Handle large-scale generations with optimization
Debug & Analysis: Troubleshoot and optimize workflow performance

📄 License

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

🔗 Links

Repository: GitHub
ComfyUI Registry: Registry Page
Issues: Bug Reports & Feature Requests
Discussions: Community Discussions

🙏 Acknowledgments

ComfyUI team for the excellent framework
Community contributors for feedback and testing
Wan2.1 model developers for advanced video generation capabilities

Made with ❤️ for the ComfyUI community