README.md

Image Colorization Application

This application provides a web-based interface for colorizing black and white images using DeOldify technology. It consists of a FastAPI backend for processing images and a Streamlit frontend for a user-friendly interface.

Features

• Colorize black and white images using DeOldify
• Choose between Artistic (more vibrant) and Stable (more realistic) models
• Adjust render factor to control quality and processing time
• Generate multiple renders with different factors to find the optimal result
• Download colorized images

Installation

Prerequisites

• Python 3.7 or higher
• CUDA-capable GPU (recommended for faster processing)
• Git

Setup Instructions

1. Clone the DeOldify repository inside your project directory:

git clone https://github.com/jantic/DeOldify.git

2. Install the requirements:

pip install -r requirements.txt

This installs both the dependencies for our application and most of the dependencies needed for DeOldify.

3. Download the pre-trained model weights:

• Artistic model: ColorizeArtistic_gen.pth
• Stable model: ColorizeStable_gen.pth

4. Create a models directory and place the downloaded weights:

mkdir -p DeOldify/models
# Move the downloaded .pth files to the DeOldify/models directory

IMPORTANT: The application expects the DeOldify directory to be in the same directory as the app.py and streamlit_app.py files.

Running the Application

1. Start the FastAPI server:

python app.py

This will start the API server on http://localhost:8000

2. In a new terminal window, start the Streamlit web application:

streamlit run streamlit_app.py

This will open the web interface in your browser, typically at http://localhost:8501

Troubleshooting

Common Issues

ModuleNotFoundError: No module named 'deoldify'

This error indicates that the application cannot find the DeOldify module. Make sure:

• The DeOldify repository is cloned in the same directory as app.py
• You're running the commands from the root directory of the project, not from inside the DeOldify directory

CUDA/GPU-related errors

If you encounter CUDA errors:

• Make sure your GPU drivers are up to date
• Try running with a lower render factor
• Edit app.py to change DeviceId.GPU0 to DeviceId.CPU if you don't have a GPU

API Documentation

Once the FastAPI server is running, you can access the API documentation at:

• http://localhost:8000/docs (Swagger UI)
• http://localhost:8000/redoc (ReDoc)

API Endpoints

• POST /colorize: Colorize a single image with specified parameters
• POST /colorize_multiple: Generate multiple colorizations with different render factors
• GET /image/{image_path}: Retrieve a colorized image by its path

Streamlit Web App

The web application provides an intuitive interface for:

• Uploading images
• Selecting colorization model (Artistic or Stable)
• Adjusting render factor
• Generating and comparing multiple renders
• Downloading colorized images

Technical Details

• The application uses DeOldify's image colorization models
• Default render factor is 10, but can be adjusted between 5-50
• The artistic model is used by default, but can be switched to stable
• For multiple renders, you can specify the minimum and maximum render factors and step size

Performance Tips

• If you encounter CUDA out-of-memory errors, try lowering the render factor
• For best results with the artistic model, a render factor of 35-40 is recommended
• For the stable model, a render factor of 20-25 typically works well

License

This project uses DeOldify, which is licensed under MIT License.