Network Bending for ComfyUI

A custom node pack for ComfyUI that enables creative manipulation and "bending" of neural network models. Perform various operations on loaded model checkpoints to create unique and experimental effects.

Features

🎛️ Network Bending Node

The main node for applying various network bending operations:

• Add Noise: Inject Gaussian noise into model weights
• Scale Weights: Scale model weights up or down
• Prune Weights: Remove small weights (sparsification)
• Randomize Weights: Replace portions of weights with random values
• Smooth Weights: Apply spatial smoothing to weight matrices
• Quantize Weights: Reduce weight precision to discrete levels

🔧 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 conv 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

Installation

1. Clone this repository into your ComfyUI custom_nodes directory:

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

2. Install dependencies (if any):

cd network_bending
pip install -r requirements.txt  # if requirements.txt exists

3. Restart ComfyUI

Usage

Basic Network Bending

1. Load a model checkpoint using the standard ComfyUI loader nodes
2. Add a "Network Bending" node to your workflow
3. Connect the MODEL output to the Network Bending input
4. 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)

Layer Targeting

You can target specific layers using patterns:

• all - Modify all layers
• conv - Target convolutional layers
• attention - Target attention layers
• linear - Target linear/dense layers
• norm - Target normalization layers
• embedding - Target embedding layers
• Combine patterns with commas: conv,attention

Model Mixing

1. Load two model checkpoints
2. Add a "Model Mixer" node
3. Connect both models to the mixer inputs
4. Set the mix mode and ratio
5. Connect the output to your generation pipeline

Parameters

Network Bending Node

| Parameter | Type | Description | Range | |-----------|------|-------------|-------| | operation | Select | Type of bending operation | See operations list | | intensity | Float | Strength of the operation | 0.0 - 1.0 | | target_layers | String | Layer patterns to target | Comma-separated patterns | | seed | Int | Random seed | -1 to 2^63-1 |

Model Mixer Node

| Parameter | Type | Description | Range | |-----------|------|-------------|-------| | mix_mode | Select | Blending mode | See mix modes list | | mix_ratio | Float | Blend ratio | 0.0 - 1.0 |

UI Features

• Visual Feedback: Operations display information about modified layers
• Help Buttons: Click "Layer Patterns Help" or "Operation Info" for detailed information
• Color Coding: Different node types have distinct colors for easy identification
• Real-time Updates: See which layers were modified after each operation

Examples

Subtle Model Variation

• Operation: add_noise
• Intensity: 0.05
• Target Layers: attention
• Creates subtle variations while preserving model behavior

Aggressive Glitch Art

• Operation: randomize_weights
• Intensity: 0.3
• Target Layers: conv
• Creates glitchy, unpredictable outputs

Model Hybridization

• Use Model Mixer with linear_interpolation
• Mix Ratio: 0.5
• Creates a balanced hybrid of two models

Tips

1. Start with low intensity values (0.01-0.1) and increase gradually
2. Target specific layers for more controlled effects
3. Use seeds for reproducible results
4. Combine operations by chaining multiple bending nodes
5. Save interesting results as new model checkpoints

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

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.

License

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

Acknowledgments

• ComfyUI team for the excellent framework
• Inspired by circuit bending and glitch art techniques