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| Map | Channels | Description |
|---|---|---|
| Basecolor | 3 | Albedo / diffuse color |
| Normal | 3 | Surface normals (tangent space) |
| Roughness | 1 | R channel of RMD - surface roughness |
| Metallic | 1 | G channel of RMD - metalness |
| Depth | 1 | B channel of RMD - relative depth |
| RGB | 3 | Reconstructed RGB from inverse maps |
shadenet/
├── app.py # Gradio Space app
├── inference.py # Standalone inference script (CLI)
├── inference_utils.py # Inference utilities (tiling, compositing)
├── model.py # Model architecture
├── layers.py # Layer components
├── config.py # Configuration
├── requirements.txt # Python dependencies
├── README.md # This file
├── checkpoints/
│ └── last.ckpt # PyTorch Lightning checkpoint (model weights)
└── onnx/
├── model_mode0.onnx # Mode 0 ONNX (RGB → inverse maps)
├── model_mode0_quantized.onnx # Quantized mode 0
├── model_mode1.onnx # Mode 1 ONNX (inverse maps → RGB)
└── model_mode1_quantized.onnx # Quantized mode 1app.py in this repo is the Space entrypoint. To create one:1pip install -r requirements.txt
2python inference.py input.jpg --output_dir ./outputonnx/ folder contains exported ONNX models for deployment without PyTorch:model_mode0.onnx / model_mode0_quantized.onnx: RGB → basecolor, normal, RMDmodel_mode1.onnx / model_mode1_quantized.onnx: Inverse maps → RGB[1, 3, 512, 512], values in [-1, 1]@software{shadenet,
author = {Sachin},
title = {ShadeNet},
year = {2026},
}