ComfyUI TensorRT Upscaler

A high-performance image upscaling node for ComfyUI using TensorRT acceleration.

Features

• TensorRT-accelerated inference for fast upscaling
• Batch processing support
• Tile-based processing for large images
• Support for both FP16 and FP32 precision
• Automatic model detection from file names
• Feathered tile blending to reduce seams

Requirements

System Requirements

• NVIDIA GPU with Compute Capability 6.0+ (Pascal architecture or newer)
• CUDA 11.8 or newer
• Windows 10/11 or Linux

Software Dependencies

• TensorRT 8.6.0 or newer
• Python 3.8+
• PyTorch 2.0+
• CUDA-compatible GPU drivers

Installation

1. Install TensorRT

Download and install TensorRT from NVIDIA Developer: https://developer.nvidia.com/tensorrt

Follow NVIDIA's installation guide for your platform.

2. Install Python Dependencies

pip install -r requirements.txt

3. Set up Models

Run the setup script to download and convert models:

Windows:

setup_models.bat

Linux/Manual:

python convert_model.py <model_name> --models-dir ../../models/upscale_models

Supported Models

The node automatically detects upscaling models in the models/upscale_models directory. Currently supported:

• 4x_foolhardy_Remacri
• 4x-UltraSharp
• Any compatible .pth model (requires conversion)

Usage

1. Add the "TRT Upscaler" node to your ComfyUI workflow
2. Connect an image input
3. Select your model from the dropdown
4. Adjust tile size and overlap as needed
5. Choose precision (FP16 recommended for speed)

Parameters

• image: Input image tensor
• model_name: TensorRT model to use for upscaling
• tile_size: Size of processing tiles (64-2048, default: 512)
• tile_overlap: Overlap between tiles (0-256, default: 32)
• use_fp16: Use FP16 precision (faster, slightly lower quality)

Tile Settings Guidelines

• Small GPU memory: Use smaller tile sizes (256-512)
• Large GPU memory: Use larger tile sizes (1024-2048)
• High overlap: Better blending but slower processing
• Low overlap: Faster but may show tile seams

Model Conversion

To convert your own .pth models:

1. Place the .pth file in models/upscale_models/
2. Run: python convert_model.py <model_name>
3. The script will create both FP16 and FP32 TensorRT engines

Performance Tips

1. Use FP16: ~2x faster with minimal quality loss
2. Optimize tile size: Balance between speed and memory usage
3. Batch processing: Process multiple images together when possible
4. GPU memory: Monitor VRAM usage and adjust tile size accordingly

Troubleshooting

"TensorRT not available"

• Ensure TensorRT is properly installed
• Check that CUDA is available: nvidia-smi
• Verify Python can import tensorrt: python -c "import tensorrt"

"No TRT models found"

• Run setup_models.bat to download and convert models
• Check that .trt files exist in models/upscale_models/
• Ensure model names include scale factor (e.g., "4x", "2x")

Out of memory errors

• Reduce tile size
• Use FP16 precision
• Close other GPU-intensive applications
• Consider upgrading GPU memory

Poor upscaling quality

• Try FP32 precision
• Increase tile overlap
• Use a different model
• Check input image quality

Development

Running Tests

python -m pytest test_trt_upscaler.py -v

Adding New Models

1. Add model URL to setup_models.bat
2. Ensure model follows naming convention (include scale factor)
3. Test conversion with convert_model.py

File Structure

comfyui-trtupscaler/
├── __init__.py              # Node registration
├── nodes.py                 # Main TRTUpscaler node
├── convert_model.py         # Model conversion utilities
├── setup_models.bat         # Windows setup script
├── requirements.txt         # Python dependencies
├── test_trt_upscaler.py     # Unit tests
└── README.md               # This file

License

This project follows the same license as ComfyUI.

Contributing

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

Support

For issues and questions:

1. Check the troubleshooting section
2. Review ComfyUI logs for detailed error messages
3. Open an issue with system info and error details