# 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** ```bash 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. ```bash conda env create -f environment.yml ``` 3. **Activate Environment** ```bash conda activate deoldify ``` 4. **Download Weights** Download the pretrained weights and place them in the `models/` directory: - [ColorizeArtistic_gen.pth](https://github.com/thookham/DeOldify/releases/download/v2.0-models/ColorizeArtistic_gen.pth) - [ColorizeStable_gen.pth](https://github.com/thookham/DeOldify/releases/download/v2.0-models/ColorizeStable_gen.pth) - [ColorizeVideo_gen.pth](https://github.com/thookham/DeOldify/releases/download/v2.0-models/ColorizeVideo_gen.pth) ```bash 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: ```bash 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: ```python import torch torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True ```