ComfyUI-RandomSeedGenerator

🎲 Advanced Seed Generator - A professional-grade custom node for ComfyUI that provides comprehensive seed generation capabilities with multiple modes, state persistence, cross-library synchronization, and enterprise-level reliability features.

✨ Features

• 🎯 Multiple Generation Modes: Fixed, Random, Increment, and Decrement modes for various workflows
• 🔄 State Persistence: Maintains seed state across executions for increment/decrement modes
• 🔗 Cross-Library Sync: Synchronizes seeds across Python, NumPy, and PyTorch for consistent results
• ⚡ Performance Optimized: Intelligent backend selection (Python random vs PyTorch) based on batch size
• 🛡️ Thread-Safe Operations: Concurrent access protection with threading.RLock()
• 🔧 Configurable Overflow: Wrap, clamp, or error handling for boundary conditions
• 📊 Batch Generation: Generate up to 100,000 seeds efficiently in batch mode
• 🎮 CUDA Support: Full GPU acceleration support with deterministic mode options
• 🐛 Comprehensive Logging: Configurable debug logging for troubleshooting
• ✅ Input Validation: Robust error handling and parameter validation

🚀 Installation

Method 1: Manual Installation

1. Navigate to your ComfyUI custom_nodes directory
2. Clone this repository:

   git clone https://github.com/Limbicnation/ComfyUI-RandomSeedGenerator.git
   

3. Restart ComfyUI
4. The node will appear under utils category as "🎲 Advanced Seed Generator"

Method 2: ComfyUI Manager (Recommended)

1. Install ComfyUI Manager
2. Search for "Random Seed Generator" in the manager
3. Install and restart ComfyUI

Method 3: ComfyUI Registry

comfy node install randomseedgenerator

📖 Usage Guide

Node Parameters

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | mode | Dropdown | "fixed" | Generation mode: fixed, increment, decrement, random | | seed | Integer | 0 | Base seed value (0 to 18,446,744,073,709,551,615) | | sync_libraries | Boolean | True | Synchronize seed across Python, NumPy, PyTorch | | deterministic | Boolean | False | Enable full deterministic mode (may impact performance) | | overflow_behavior | Dropdown | "wrap" | Overflow handling: wrap, clamp, error | | use_torch_backend | Dropdown | "auto" | Backend selection: auto, random, torch | | batch_count | Integer | 1 | Number of seeds to generate (1-100,000) |

Generation Modes

🔒 Fixed Mode

Returns the exact seed value you specify. Perfect for reproducible generations.

Input: seed=12345 → Output: 12345 (always)

🎲 Random Mode

Generates a new random seed on each execution (0 to 2^64-1).

Input: any seed → Output: 4831672946, 9573821047, ... (random)

⬆️ Increment Mode

Increments from the last generated seed by 1. State persists across workflow executions.

First run: 42 → Second run: 43 → Third run: 44 ...

⬇️ Decrement Mode

Decrements from the last generated seed by 1. State persists across workflow executions.

First run: 42 → Second run: 41 → Third run: 40 ...

Overflow Behavior Options

• 🔄 Wrap (Default): Cycles around boundaries (MAX → MIN, MIN → MAX)
• 🛑 Clamp: Stops at boundaries (stays at MAX/MIN when limit reached)
• ❌ Error: Raises exception when overflow would occur

Backend Selection

• 🤖 Auto (Recommended): Optimal backend selection based on batch size
  • Single seeds: Python random (fastest)
  • Batches ≥100: PyTorch CPU
  • Batches ≥1000: PyTorch GPU (if available)
• 🐍 Random: Force Python random module (good for small operations)
• 🔥 Torch: Force PyTorch backend (better for large batches)

💡 Usage Examples

Basic Seed Generation

1. Add "🎲 Advanced Seed Generator" to your workflow
2. Set mode to "random" for exploration or "fixed" for reproducibility
3. Connect the output to any node requiring a seed (KSampler, etc.)

Batch Exploration Workflow

1. Set mode to "increment"
2. Set batch_count to 10
3. Use with batch processors to generate variations systematically

Professional Reproducibility Setup

1. Set mode to "fixed"
2. Enable sync_libraries and deterministic
3. Document your seed values for exact reproduction

🔧 Advanced Configuration

Environment Variables

# Set logging level for debugging
export COMFYUI_SEED_LOG_LEVEL=DEBUG  # Options: DEBUG, INFO, WARNING, ERROR

Performance Tuning

• Small batches (1-99): Use "random" backend for minimal overhead
• Medium batches (100-999): Use "auto" for optimal CPU performance
• Large batches (1000+): Use "auto" with CUDA for GPU acceleration

📋 Requirements

• ComfyUI: Latest version recommended
• Python: 3.8 or higher
• Dependencies:
  • torch (PyTorch)
  • numpy
  • threading (built-in)
  • logging (built-in)

🔍 Troubleshooting

Common Issues

Node not appearing in menu:

• Restart ComfyUI completely
• Check console for import errors
• Verify all dependencies are installed

Increment/Decrement not working:

• State persists at class level - normal behavior
• Use reset_state() method in console if needed
• Check overflow_behavior setting

Performance issues with large batches:

• Set backend to "torch" for batches >1000
• Enable GPU if available for CUDA acceleration
• Monitor memory usage with very large batches

Debug Logging

Enable detailed logging to diagnose issues:

export COMFYUI_SEED_LOG_LEVEL=DEBUG
# Restart ComfyUI and check console output

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• ComfyUI community for the amazing platform
• Contributors and testers
• Enhanced and maintained with Claude Code

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⭐ If this node helps your workflow, please consider starring the repository!

For issues, feature requests, or questions, please visit our GitHub Issues page.