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| title: ColorGradingAE | |
| emoji: 🎨 | |
| colorFrom: purple | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.20.0 | |
| python_version: '3.13' | |
| app_file: gradio_demo2.py | |
| pinned: false | |
| license: mit | |
| # ColorGradingAE - Contrastive Color Grade AI | |
| An AI-powered color grading tool that uses contrastive learning with an encoder-decoder architecture to automatically analyze and apply professional color grading to images. | |
| ## Features | |
| - 🎨 **Automatic Color Analysis**: Extracts color features from images using HSV color space analysis | |
| - 🧠 **Contrastive Learning Model**: Uses encoder-decoder architecture trained on color grading patterns | |
| - 🎯 **Zone-based Processing**: Analyzes shadows, midtones, and highlights separately | |
| - 🌐 **Interactive Web Interface**: Easy-to-use Gradio interface for real-time color grading | |
| - 🚀 **GPU Acceleration**: CUDA support for fast inference | |
| ## Installation | |
| Clone the repository and install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### Requirements | |
| - Python 3.13+ | |
| - PyTorch with CUDA support (or CPU) | |
| - OpenCV | |
| - Gradio | |
| - Pillow | |
| - NumPy | |
| ## Usage | |
| ### Web Interface | |
| Run the interactive Gradio demo: | |
| ```bash | |
| python gradio_demo.py | |
| ``` | |
| This launches a web interface where you can upload images and apply color grading in real-time. | |
| ### Python API | |
| Use the color grading functions programmatically: | |
| ```python | |
| from inference import generate_grade, apply_grade | |
| # Generate color grade parameters for an image | |
| grade = generate_grade("input.jpg") | |
| # Apply the generated grade to an image | |
| output = apply_grade("input.jpg", grade) | |
| ``` | |
| ### Training | |
| To train or fine-tune the model on your own images: | |
| ```bash | |
| python trainer.py | |
| ``` | |
| Place your training images in the `data/images/` directory. | |
| ## Project Structure | |
| ``` | |
| ├── gradio_demo.py # Main Gradio web interface | |
| ├── gradio_demo2.py # Alternative demo interface | |
| ├── inference.py # Color grading inference functions | |
| ├── trainer.py # Model training and feature extraction | |
| ├── downloader.py # Utility for downloading images/models | |
| ├── encoder.pt # Pre-trained encoder model | |
| ├── decoder.pt # Pre-trained decoder model | |
| ├── data/ | |
| │ └── images/ # Training images directory | |
| └── flagged/ # Gradio flagged/saved results | |
| ``` | |
| ## How It Works | |
| 1. **Feature Extraction**: Analyzes image color distributions across HSV channels in 16 hue bins | |
| 2. **Zone Analysis**: Processes shadows, midtones, and highlights separately for more nuanced grading | |
| 3. **Encoding**: Compresses color features using the trained encoder | |
| 4. **Decoding**: Generates color grading parameters using the trained decoder | |
| 5. **Application**: Applies the generated grade to produce the final color-graded output | |
| ## Model Architecture | |
| - **Encoder**: Compresses color feature vectors into latent space | |
| - **Decoder**: Reconstructs color grading parameters from latent vectors | |
| - **Training**: Uses contrastive learning to learn meaningful color relationships | |
| ## Performance | |
| - Supports GPU acceleration via CUDA for fast inference | |
| - Falls back to CPU if GPU unavailable | |
| - Real-time processing suitable for interactive applications | |
| ## License | |
| MIT License - See LICENSE file for details | |
| --- | |
| For more information, check out the [Hugging Face documentation](https://huggingface.co/docs/hub/spaces-config-reference) | |