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# 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
```
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