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151 lines
5.3 KiB
Markdown
151 lines
5.3 KiB
Markdown
# EternalKernel PyTorch Nodes
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[](https://www.gnu.org/licenses/agpl-3.0)
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[](https://pytorch.org/)
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[](https://www.python.org/)
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A comprehensive collection of PyTorch nodes for ComfyUI, enabling advanced machine learning workflows with neural network training, inference, and data manipulation capabilities.
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## 🌟 Features
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### 🧠 Neural Network Components
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- **Layer Nodes**: Linear, Convolutional, BatchNorm, Dropout, Transformer layers
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- **Activation Functions**: ReLU, Sigmoid, Tanh, Softmax, and more
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- **Model Building**: Sequential model construction and layer extraction
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- **Architecture Tools**: Reshape, flatten, and tensor manipulation utilities
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### 🚀 Training & Inference
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- **Model Training**: Full training loops with loss computation and optimization
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- **Grid Search**: Automated hyperparameter optimization
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- **Inference**: Efficient model inference with GPU acceleration
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- **Model Management**: Save/load PyTorch models with metadata
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### 📊 Data Handling
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- **Dataset Tools**: Download popular datasets (MNIST, CIFAR, etc.)
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- **Data Processing**: Split, shuffle, and batch your datasets
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- **Tensor Operations**: Slice, reshape, type conversion, and device management
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- **ComfyUI Integration**: Convert between ComfyUI images and PyTorch tensors
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### 🔧 Advanced Features
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- **GPU Support**: Automatic CUDA acceleration when available
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- **Model Modification**: Extract layers, freeze/unfreeze parameters
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- **Visualization**: Plot training metrics and data distributions
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- **Flexible I/O**: Support for various data formats and tensor types
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## 📦 Installation
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### Quick Start
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1. Navigate to your ComfyUI custom nodes directory:
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```bash
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cd ComfyUI/custom_nodes
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```
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2. Clone this repository:
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```bash
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git clone https://github.com/TashaSkyUp/EternalKernelPyTorchNodes.git
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```
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3. Install dependencies:
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```bash
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cd EternalKernelPyTorchNodes
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pip install -r requirements.txt
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```
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4. Restart ComfyUI and the nodes will appear under the **ETK/pytorch** category.
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### Requirements
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- **Python**: 3.8 or higher
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- **PyTorch**: 2.0+ (with CUDA support recommended)
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- **ComfyUI**: Latest version
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- **Dependencies**: See `requirements.txt` for full list
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## 🎯 Node Categories
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### Dataset & Data Processing
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- `PyTorchDatasetDownloader` - Download popular ML datasets
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- `DatasetSplitter` - Split datasets into train/test/validation
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- `TensorsToDataset` - Create datasets from tensor collections
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- `DatasetToDataloader` - Generate DataLoaders with batching
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### Neural Network Layers
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- `AddLinearLayerNode` - Fully connected layers
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- `AddConvLayer` - Convolutional layers with customizable parameters
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- `AddBatchNormLayer` - Batch normalization for stable training
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- `AddDropoutLayer` - Regularization through dropout
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- `AddTransformerLayer` - Modern attention-based layers
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- `AddReshapeLayer` - Dynamic tensor reshaping
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### Model Operations
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- `SequentialModelProvider` - Build sequential neural networks
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- `PyTorchInferenceNode` - Run inference on trained models
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- `TrainModel` - Complete training loops with optimization
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- `GridSearchTraining` - Automated hyperparameter tuning
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- `SaveModel` / `LoadModel` - Model persistence with metadata
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### Tensor Utilities
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- `FlattenTensor` - Flatten multi-dimensional tensors
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- `ReshapeTensor` - Reshape tensors to desired dimensions
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- `SliceTensor` - Extract tensor slices and subsets
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- `ChangeTensorType` - Convert between tensor data types
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- `PyTorchToDevice` - Move tensors between CPU/GPU
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- `RandomTensor` - Generate random tensors for testing
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### Advanced Tools
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- `ExtractLayersAsModel` - Extract sublayers as standalone models
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- `AddModelAsLayer` - Embed existing models as layers
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- `SetModelTrainable` - Freeze/unfreeze model parameters
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- `FuncModifyModel` - Apply custom functions to models
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- `PlotSeriesString` - Visualize training metrics
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## 🚀 Usage Examples
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### Basic Neural Network Training
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Create and train a neural network with just a few nodes:
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1. **Download Dataset** → **Split Data** → **Build Model** → **Train** → **Save**
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### Grid Search Optimization
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Automatically find the best hyperparameters for your model with the GridSearchTraining node.
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### ComfyUI Integration
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Seamlessly convert between ComfyUI images and PyTorch tensors for ML processing in your workflows.
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## 🧪 Testing
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Run the comprehensive test suite:
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```bash
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cd EternalKernelPyTorchNodes
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python -m pytest tests/ -v
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```
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Tests cover all node functionality, model training/inference, tensor operations, and GPU/CPU compatibility.
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## 🤝 Contributing
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Contributions welcome! Please:
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- Report bugs or issues
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- Suggest new features
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- Submit pull requests
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- Improve documentation
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## 📋 Compatibility
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- **ComfyUI**: All recent versions
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- **OS**: Windows, macOS, Linux
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- **Hardware**: CPU and CUDA GPUs
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- **PyTorch**: 2.0+ (optimized for latest)
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## 📄 License
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GNU Affero General Public License v3.0 - see [LICENSE](LICENSE) file for details.
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## 🙏 Acknowledgments
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Built for the [ComfyUI](https://github.com/comfyanonymous/ComfyUI) community, powered by [PyTorch](https://pytorch.org/).
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---
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**Made with ❤️ for the ComfyUI and PyTorch communities**
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For support: [GitHub Issues](https://github.com/TashaSkyUp/EternalKernelPyTorchNodes/issues)
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