A comprehensive custom node pack for ComfyUI that enables creative manipulation and "bending" of neural network models, including both image and audio models. Inspired by circuit bending techniques in electronic music, this package allows you to creatively corrupt, modify, and blend neural networks to create unique and experimental effects.

🎯 Overview

Network Bending brings the concept of circuit bending to AI models. Just as circuit bending involves creatively short-circuiting electronic devices to create new sounds, network bending involves modifying neural network weights and architectures to create unexpected and artistic outputs. This package supports both Stable Diffusion image models and Stable Audio models.

📁 Project Structure

network_bending/
├── src/network_bending/
│   ├── nodes.py                    # Core network bending nodes
│   ├── audio_nodes/               # Audio-specific nodes
│   │   ├── audio_latent_nodes.py # Audio VAE and latent manipulation
│   │   ├── audio_style_transfer.py # Audio style transfer capabilities
│   │   └── README.md              # Audio nodes documentation
│   ├── audio_workflows/           # Pre-built audio workflows
│   └── js/                        # UI components
│       └── network_bending.js     # Custom UI with visual feedback
├── examples/
│   └── basic_workflow.json        # Example ComfyUI workflow
├── tests/                         # Test suite
├── requirements-audio.txt         # Audio processing dependencies
└── pyproject.toml                 # Modern Python packaging

🚀 Features

🎛️ Core Network Bending Nodes

Network Bending Node

The main node for applying various network bending operations to image models:

Add Noise: Inject Gaussian noise into model weights for subtle variations
Scale Weights: Scale model weights up or down to amplify or dampen features
Prune Weights: Remove small weights for sparsification and efficiency
Randomize Weights: Replace portions of weights with random values for chaos
Smooth Weights: Apply spatial smoothing to weight matrices for softer outputs
Quantize Weights: Reduce weight precision to discrete levels for lo-fi effects

Network Bending Advanced Node

Advanced operations for more complex manipulations:

Layer Swap: Swap weights between similar layers
Activation Replace: Replace activation functions
Weight Transpose: Transpose weight matrices
Channel Shuffle: Randomly shuffle channels in convolutional layers
Frequency Filter: Apply frequency domain filtering
Weight Clustering: Group similar weights together

Model Mixer Node

Mix two models together with various blending modes:

Linear Interpolation: Simple weighted average of weights
Weighted Sum: Weighted sum with normalization
Layer-wise Mix: Different mix ratios for different layers
Frequency Blend: Mix models in frequency domain
Random Mix: Randomly select weights from either model

🎵 Audio Processing Nodes

The package includes comprehensive audio processing capabilities for Stable Audio models:

Audio VAE Nodes

Audio VAE Encode: Encode audio waveforms into latent space
Audio VAE Decode: Decode latent representations back to audio

Audio Latent Manipulation

Audio Latent Interpolate: Smoothly interpolate between audio latents
Audio Latent Blend: Blend multiple audio latents with various modes
Audio Latent Manipulator: Direct manipulation of audio latent features
Audio Feature Extractor: Extract specific features from audio latents

Audio Style Transfer

Audio Style Transfer: Transfer style from one audio to another
Audio Latent Guidance: Guide generation with reference audio
Audio Reference Encoder: Encode reference audio for style transfer

🎨 UI Features

The package includes custom JavaScript UI components that provide:

Visual Feedback: Real-time display of modified layers and operation results
Help System: Built-in help buttons with detailed information
Color Coding: Different node types have distinct colors for easy identification
Operation Preview: See which layers will be affected before applying
Progress Tracking: Monitor long-running operations

📦 Installation

Basic Installation

Clone this repository into your ComfyUI custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/DavidPiazza/network_bending.git

Restart ComfyUI

For Audio Features

If you want to use the audio processing nodes, install additional dependencies:

cd ComfyUI/custom_nodes/network_bending
pip install -r requirements-audio.txt

Dependencies

Core dependencies (automatically installed):

PyTorch >= 2.0.0
NumPy >= 1.21.0

Audio dependencies (optional):

torchaudio >= 2.0.0
librosa >= 0.10.0
scipy >= 1.7.0

Optional audio dependencies for extended format support:

soundfile >= 0.12.0
audioread >= 3.0.0
resampy >= 0.4.0

📖 Usage Guide

Basic Network Bending for Images

Load a model checkpoint using the standard ComfyUI loader nodes
Add a "Network Bending" node to your workflow
Connect the MODEL output to the Network Bending input
Configure parameters:
- Operation: Select the type of bending operation
- Intensity: Control the strength (0.0 = no effect, 1.0 = maximum)
- Target Layers: Specify which layers to modify (e.g., "conv", "attention", or "all")
- Seed: Set for reproducible results (-1 for random)

Audio Network Bending

Load an audio model (e.g., Stable Audio VAE)
Add audio processing nodes from the network_bending/audio category
Connect audio inputs either from file loaders or generated audio
Apply bending operations to the audio latents
Decode back to audio using Audio VAE Decode

Example audio workflow:

Audio Input → Audio VAE Encode → Audio Latent Manipulator → Audio VAE Decode → Audio Output

Advanced Techniques

Layered Bending

Chain multiple bending operations with different intensities:

Model → Smooth (0.1) → Add Noise (0.05) → Quantize (0.3) → Output

Apply image model bending techniques to audio models or vice versa for experimental results.

Targeted Corruption

Use specific layer patterns to corrupt only certain aspects:

"transformer" - Target transformer blocks
"resnet" - Target ResNet blocks
"unet" - Target U-Net components

🔧 Technical Details

Architecture

The package is built with a modular architecture:

Core Bending Engine: Base operations that work on any PyTorch model
Model Adapters: Specific handling for ComfyUI model wrappers
Operation Library: Extensible set of bending operations
UI Integration: Real-time feedback through ComfyUI's server

Implementation Notes

All operations clone the model to avoid modifying the original
Supports both CPU and GPU operations
Memory-efficient implementations for large models
Thread-safe for parallel processing
Preserves model metadata and configurations

Performance Considerations

Operations scale with model size
GPU recommended for real-time feedback
Batch processing supported for efficiency
Automatic memory management for large models

🧪 Development

Running Tests

pytest tests/

Code Quality

The project uses:

ruff for linting
mypy for type checking
pre-commit hooks for code quality
black for code formatting

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch
Add tests for new features
Ensure all tests pass
Submit a pull request

📝 Examples and Workflows

Check the examples/ directory for pre-built workflows:

basic_workflow.json: Simple network bending setup
Audio workflows available in src/network_bending/audio_workflows/

🐛 Troubleshooting

No effect visible: Increase intensity or check target_layers pattern
Model breaks completely: Reduce intensity or target fewer layers
Out of memory: Operations clone the model, ensure sufficient VRAM

Audio-Specific Issues

No audio output: Check audio dependencies are installed
Format not supported: Install optional dependencies (soundfile, audioread)
Memory issues with long audio: Process in chunks or reduce sample rate

📄 License

This project is licensed under the GNU General Public License v3 (GPL-3.0) - see the LICENSE file for details.

🙏 Acknowledgments

ComfyUI team for the excellent framework
Inspired by circuit bending and glitch art techniques
Stable Audio team for audio model support
The creative coding and AI art community

📬 Contact

Author: David Piazza
Email: david.piazza@umontreal.ca
GitHub: https://github.com/DavidPiazza/network_bending
Issues: https://github.com/DavidPiazza/network_bending/issues

Network Bending: Where AI meets glitch art 🎨🤖

README.md

Network Bending for ComfyUI