docs/nvidia_setup.md

NVIDIA GPU Setup Guide for DeOldify

This guide covers how to set up DeOldify with modern NVIDIA GPUs (RTX 30xx, 40xx, 50xx) using CUDA 12.x and PyTorch 2.5+.

Prerequisites

• NVIDIA Driver: Version 550.x or later (supports CUDA 12.4+)
• Anaconda or Miniconda installed
• Git installed

Installation Steps

1. Clone the Repository

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

2. Create Conda Environment We use a modern environment file that installs PyTorch 2.5+ and CUDA 12.4 support.

   conda env create -f environment.yml
   

3. Activate Environment

   conda activate deoldify
   

4. Download Weights Download the pretrained weights and place them in the models/ directory:

   • ColorizeArtistic_gen.pth
   • ColorizeStable_gen.pth
   • ColorizeVideo_gen.pth

   mkdir -p models
   # Example using wget
   wget https://github.com/thookham/DeOldify/releases/download/v2.0-models/ColorizeArtistic_gen.pth -O models/ColorizeArtistic_gen.pth
   

Verification

Run the verification script to ensure everything is set up correctly:

python verify_refactor.py

Troubleshooting

"CUDA not available"

• Ensure you have the correct NVIDIA drivers installed.
• Run nvidia-smi to check driver status.
• Ensure you installed the environment from environment.yml which pulls pytorch-cuda.

"Out of Memory"

• Reduce render_factor in your scripts.
• Ensure no other processes are using the GPU.

Performance Tuning

• For RTX 40xx/50xx series, you can enable TF32 for better performance:

  import torch
  torch.backends.cuda.matmul.allow_tf32 = True
  torch.backends.cudnn.allow_tf32 = True