add GitHub Actions workflow for syncing to Hugging Face space

.github/workflows/huggingface.yml

@@ -0,0 +1,25 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v5
+        with:
+          fetch-depth: 0
+      - name: Add remote 
+        env:
+          HF: ${{secrets.HF_TOKEN }}
+          HFUSER: ${{secrets.HFUSER }}
+        run: git remote add space https://$HFUSER:$HF@huggingface.co/spaces/$HFUSER/Depth-Anything-Compare-demo
+      - name: Push to hub
+        env:
+          HF: ${{ secrets.HF_TOKEN}}
+          HFUSER: ${{secrets.HFUSER }}
+        run: git push --force https://$HFUSER:$HF@huggingface.co/spaces/$HFUSER/Depth-Anything-Compare-demo main

README.md

@@ -9,4 +9,185 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Depth Anything v1 vs v2 Comparison Demo
+
+A comprehensive comparison tool for **Depth Anything v1** and **Depth Anything v2** models, built with Gradio and optimized for HuggingFace Spaces with ZeroGPU support.
+
+## 🚀 Features
+
+### Three Comparison Modes
+
+1. **🎚️ Slider Comparison**: Interactive side-by-side comparison with a draggable slider
+2. **🔍 Method Comparison**: Traditional side-by-side view with model labels
+3. **🔬 Single Model**: Run individual models for detailed analysis
+
+### Supported Models
+
+#### Depth Anything v1
+- **ViT-S (Small)**: Fastest inference, good quality
+- **ViT-B (Base)**: Balanced speed and quality
+- **ViT-L (Large)**: Best quality, slower inference
+
+#### Depth Anything v2
+- **ViT-Small**: Enhanced small model with improved accuracy
+- **ViT-Base**: Balanced performance with v2 improvements
+- **ViT-Large**: State-of-the-art depth estimation quality
+
+## 🖼️ Example Images
+
+The demo includes 20+ carefully selected example images showcasing various scenarios:
+- Indoor and outdoor scenes
+- Different lighting conditions
+- Various object types and compositions
+- Challenging depth estimation scenarios
+
+## 🛠️ Technical Details
+
+### Architecture
+- **Framework**: Gradio 4.0+ with modern UI components
+- **Backend**: PyTorch with CUDA acceleration
+- **Deployment**: ZeroGPU-optimized for HuggingFace Spaces
+- **Memory Management**: Automatic model loading/unloading for efficient GPU usage
+
+### ZeroGPU Optimizations
+- `@spaces.GPU` decorators for GPU-intensive functions
+- Automatic memory cleanup between inferences
+- On-demand model loading to prevent OOM errors
+- Efficient resource allocation and deallocation
+
+### Depth Visualization
+- **Colormap**: Spectral_r colormap for intuitive depth representation
+- **Normalization**: Min-max scaling for consistent visualization
+- **Resolution**: Maintains original image aspect ratios
+
+## 📦 Installation & Setup
+
+### Local Development
+
+1. **Clone the repository**:
+```bash
+git clone <repository-url>
+cd Depth-Anything-Compare-demo
+```
+
+2. **Install dependencies**:
+```bash
+pip install -r requirements.txt
+```
+
+3. **Download model checkpoints** (for local usage):
+```bash
+# Depth Anything v1 models are downloaded automatically from HuggingFace Hub
+# For v2 models, download checkpoints to Depth-Anything-V2/checkpoints/
+```
+
+4. **Run locally**:
+```bash
+python app_local.py  # For local development
+python app.py        # For ZeroGPU deployment
+```
+
+### HuggingFace Spaces Deployment
+
+This app is optimized for HuggingFace Spaces with ZeroGPU. Simply:
+
+1. Upload the repository to your HuggingFace Space
+2. Set hardware to "ZeroGPU"
+3. The app will automatically handle GPU allocation and model loading
+
+## 📁 Project Structure
+
+```
+Depth-Anything-Compare-demo/
+├── app.py                 # ZeroGPU-optimized main application
+├── app_local.py          # Local development version
+├── requirements.txt      # Python dependencies
+├── README.md            # This file
+├── assets/
+│   └── examples/        # Example images for testing
+├── Depth-Anything/      # Depth Anything v1 implementation
+│   ├── depth_anything/
+│   │   ├── dpt.py      # v1 model architecture
+│   │   └── util/       # v1 utilities and transforms
+│   └── torchhub/       # Required dependencies
+└── Depth-Anything-V2/   # Depth Anything v2 implementation
+    ├── depth_anything_v2/
+    │   ├── dpt.py      # v2 model architecture
+    │   └── dinov2_layers/ # DINOv2 components
+    └── assets/
+        └── examples/    # v2-specific examples
+```
+
+## 🔧 Configuration
+
+### Model Configuration
+Models are configured in the respective config dictionaries:
+- `V1_MODEL_CONFIGS`: HuggingFace Hub model identifiers
+- `V2_MODEL_CONFIGS`: Local checkpoint paths and architecture parameters
+
+### Environment Variables
+- `DEVICE`: Automatically detects CUDA availability
+- GPU memory is managed automatically by ZeroGPU
+
+## 📊 Performance
+
+### Inference Times (Approximate)
+- **ViT-S models**: ~1-2 seconds
+- **ViT-B models**: ~2-4 seconds  
+- **ViT-L models**: ~4-8 seconds
+
+*Times vary based on image resolution and GPU availability*
+
+### Memory Usage
+- Optimized for ZeroGPU's memory constraints
+- Automatic model unloading prevents OOM errors
+- Efficient batch processing for multiple comparisons
+
+## 🎯 Usage Examples
+
+### Compare v1 vs v2 Models
+1. Upload an image or select from examples
+2. Choose models from both v1 and v2 families
+3. Click "Compare" or "Slider Compare"
+4. Analyze the depth estimation differences
+
+### Analyze Single Model Performance
+1. Select "Single Model" tab
+2. Choose any available model
+3. Upload image and click "Run"
+4. Examine detailed depth map output
+
+## 🤝 Contributing
+
+Contributions are welcome! Areas for improvement:
+- Additional model variants
+- New visualization options
+- Performance optimizations
+- UI/UX enhancements
+
+## 📚 References
+
+- **Depth Anything v1**: [LiheYoung/Depth-Anything](https://github.com/LiheYoung/Depth-Anything)
+- **Depth Anything v2**: [DepthAnything/Depth-Anything-V2](https://github.com/DepthAnything/Depth-Anything-V2)
+- **Original Papers**: 
+  - [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891)
+  - [Depth Anything V2: More Efficient, Better Supervised](https://arxiv.org/abs/2406.09414)
+
+## 📄 License
+
+This project combines implementations from:
+- Depth Anything v1: MIT License
+- Depth Anything v2: Apache 2.0 License
+- Demo code: MIT License
+
+Please check individual component licenses for specific terms.
+
+## 🙏 Acknowledgments
+
+- Original Depth Anything authors and contributors
+- HuggingFace team for Spaces and ZeroGPU infrastructure
+- Gradio team for the excellent UI framework
+
+---
+
+**Note**: This is a demonstration/comparison tool. For production use of the Depth Anything models, please refer to the original repositories and follow their recommended practices.