StableDelight addresses the challenging problem of removing specular reflections from textured surfaces, enabling the revelation of underlying textures. This solution is targeted at researchers and practitioners in computer vision and graphics who require high-quality reflection removal for applications like material analysis, augmented reality, and image editing. The primary benefit is the ability to recover detailed surface information obscured by glare.

How It Works

StableDelight leverages a diffusion model, building upon the StableNormal framework. It incorporates a multi-scale SSIM loss and a random conditional scales technique during diffusion training. This approach aims to enhance sharpness and stability in the reflection removal process, offering a more robust and detailed output compared to traditional methods.

Quick Start & Requirements

• Install via pip: pip install git+https://github.com/Stable-X/StableDelight.git
• Alternatively, clone the repo and run pip install -r requirements.txt followed by pip install -e ..
• Requires PyTorch.
• A Gradio interface is available via python app.py.
• Official Torch Hub loader example provided in README.

Highlighted Details

• Trained on Hypersim, Lumos, and TSHRNet datasets.
• Integrates multi-scale SSIM loss and random conditional scales for improved sharpness.
• Builds upon the StableNormal framework for enhanced diffusion model stability.

Maintenance & Community

• Project maintained by Stable-X.
• No explicit community links (Discord, Slack) or roadmap mentioned in the README.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

• The README does not mention specific hardware requirements (e.g., GPU) or performance benchmarks.
• The Gradio interface notes potential compatibility issues on macOS.
• No information on model size or inference speed is provided.