EternalKernel PyTorch Nodes

[](https://www.gnu.org/licenses/agpl-3.0)

[](https://pytorch.org/)

[](https://www.python.org/)

A comprehensive collection of PyTorch nodes for ComfyUI, enabling advanced machine learning workflows with neural network training, inference, and data manipulation capabilities.

🌟 Features

🧠 Neural Network Components

Layer Nodes: Linear, Convolutional, BatchNorm, Dropout, Transformer layers

Activation Functions: ReLU, Sigmoid, Tanh, Softmax, and more

Model Building: Sequential model construction and layer extraction

Architecture Tools: Reshape, flatten, and tensor manipulation utilities

🚀 Training & Inference

Model Training: Full training loops with loss computation and optimization

Grid Search: Automated hyperparameter optimization

Inference: Efficient model inference with GPU acceleration

Model Management: Save/load PyTorch models with metadata

📊 Data Handling

Dataset Tools: Download popular datasets (MNIST, CIFAR, etc.)

Data Processing: Split, shuffle, and batch your datasets

Tensor Operations: Slice, reshape, type conversion, and device management

ComfyUI Integration: Convert between ComfyUI images and PyTorch tensors

🔧 Advanced Features

GPU Support: Automatic CUDA acceleration when available

Model Modification: Extract layers, freeze/unfreeze parameters

Visualization: Plot training metrics and data distributions

Flexible I/O: Support for various data formats and tensor types

📦 Installation

Quick Start

Navigate to your ComfyUI custom nodes directory:

cd ComfyUI/custom_nodes

Clone this repository:

git clone https://github.com/TashaSkyUp/EternalKernelPyTorchNodes.git

Install dependencies:

cd EternalKernelPyTorchNodes
pip install -r requirements.txt

Restart ComfyUI and the nodes will appear under the ETK/pytorch category.

Requirements

Python: 3.8 or higher

PyTorch: 2.0+ (with CUDA support recommended)

ComfyUI: Latest version

Dependencies: See requirements.txt for full list

🎯 Node Categories

Dataset & Data Processing

PyTorchDatasetDownloader – Download popular ML datasets

DatasetSplitter – Split datasets into train/test/validation

TensorsToDataset – Create datasets from tensor collections

DatasetToDataloader – Generate DataLoaders with batching

Neural Network Layers

AddLinearLayerNode – Fully connected layers

AddConvLayer – Convolutional layers with customizable parameters

AddBatchNormLayer – Batch normalization for stable training

AddDropoutLayer – Regularization through dropout

AddTransformerLayer – Modern attention-based layers

AddReshapeLayer – Dynamic tensor reshaping

Model Operations

SequentialModelProvider – Build sequential neural networks

PyTorchInferenceNode – Run inference on trained models

TrainModel – Complete training loops with optimization

GridSearchTraining – Automated hyperparameter tuning

SaveModel / LoadModel – Model persistence with metadata

Tensor Utilities

FlattenTensor – Flatten multi-dimensional tensors

ReshapeTensor – Reshape tensors to desired dimensions

SliceTensor – Extract tensor slices and subsets

ChangeTensorType – Convert between tensor data types

PyTorchToDevice – Move tensors between CPU/GPU

RandomTensor – Generate random tensors for testing

Advanced Tools

ExtractLayersAsModel – Extract sublayers as standalone models

AddModelAsLayer – Embed existing models as layers

SetModelTrainable – Freeze/unfreeze model parameters

FuncModifyModel – Apply custom functions to models

PlotSeriesString – Visualize training metrics

🚀 Usage Examples

Basic Neural Network Training

Create and train a neural network with just a few nodes:

Download Dataset → Split Data → Build Model → Train → Save

Grid Search Optimization

Automatically find the best hyperparameters for your model with the GridSearchTraining node.

ComfyUI Integration

Seamlessly convert between ComfyUI images and PyTorch tensors for ML processing in your workflows.

🧪 Testing

Run the comprehensive test suite:

cd EternalKernelPyTorchNodes
python -m pytest tests/ -v

Tests cover all node functionality, model training/inference, tensor operations, and GPU/CPU compatibility.

🤝 Contributing

Contributions welcome! Please:

Report bugs or issues

Suggest new features

Submit pull requests

Improve documentation

📋 Compatibility

ComfyUI: All recent versions

OS: Windows, macOS, Linux

Hardware: CPU and CUDA GPUs

PyTorch: 2.0+ (optimized for latest)

📄 License

GNU Affero General Public License v3.0 – see LICENSE file for details.

🙏 Acknowledgments

Built for the ComfyUI community, powered by PyTorch.

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Made with ❤️ for the ComfyUI and PyTorch communities

For support: GitHub Issues