docs/DEPLOYMENT_GUIDE.md

DeOldify Deployment Guide

This guide covers the various ways you can deploy and run DeOldify.

🏠 Local Deployment

Running DeOldify locally gives you the best performance and privacy, provided you have the necessary hardware.

Option 1: Conda (Recommended)

We recommend using Anaconda or Miniconda to manage dependencies.

1. Clone the repository:

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

2. Create the environment:

   • For NVIDIA GPU:

     conda env create -f environment.yml
     conda activate deoldify
     

   • For Intel GPU:

     conda env create -f environment_intel.yml
     conda activate deoldify-intel
     

3. Run Jupyter Lab:

   jupyter lab
   

Option 2: Pip

If you prefer standard Python venv:

1. Install Python 3.10+.
2. Install dependencies:

   pip install -r requirements.txt
   

   Note: You may need to install PyTorch manually first to ensure you get the correct CUDA version for your hardware.

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☁️ Cloud Deployment

Google Colab

The easiest way to try DeOldify without installing anything.

• Image Colorizer: Open in Colab
• Video Colorizer: Open in Colab

Notes:

• Requires a Google account.
• Free tier GPUs are sufficient for images and short videos.
• Pro tier recommended for longer videos or faster rendering.

Google Cloud Platform (Vertex AI)

Coming Soon - We are working on official scripts to deploy DeOldify as a scalable API endpoint on Vertex AI.

Docker

Coming Soon - Official Docker images will be available to simplify deployment on any container orchestration platform.

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📦 Model Weights

DeOldify relies on pre-trained model weights. These are downloaded automatically by the notebooks/scripts when you first run them.

• Artistic: ColorizeArtistic_gen.pth
• Stable: ColorizeStable_gen.pth
• Video: ColorizeVideo_gen.pth

If you are deploying in an air-gapped environment, you will need to download these weights manually and place them in the models/ directory.