| # DeOldify Hardware Compatibility Guide | |
| This guide outlines the supported hardware configurations for running DeOldify. We support a wide range of devices, from consumer GPUs to data center accelerators and CPUs. | |
| ## 🚀 Quick Reference Matrix | |
| | Hardware Type | Supported Devices | Recommended VRAM | Setup Guide | | |
| | :--- | :--- | :--- | :--- | | |
| | **NVIDIA GPU** | GeForce GTX 10-series+, RTX 20/30/40-series, Tesla, A100/H100 | 4GB+ (8GB+ for Video) | [NVIDIA Setup](nvidia_setup.md) | | |
| | **Intel GPU** | Arc A-Series (A770, A750), Data Center GPU Flex/Max | 8GB+ | [Intel Setup](intel_gpu_setup.md) | | |
| | **CPU** | Any modern x86_64 CPU (Intel/AMD) | N/A (System RAM > 16GB) | Standard Installation | | |
| --- | |
| ## 🟢 NVIDIA GPUs (Recommended) | |
| NVIDIA GPUs provide the most mature ecosystem for Deep Learning. DeOldify is optimized to take advantage of CUDA cores and Tensor cores on modern NVIDIA hardware. | |
| ### Requirements | |
| * **Driver**: CUDA-compatible driver (ensure support for CUDA 11.8 or 12.x). | |
| * **VRAM**: | |
| * **Artistic Images**: Minimum 4GB. | |
| * **Stable Video**: Minimum 8GB recommended to handle frame buffering and larger batch sizes. | |
| * **Performance**: `torch.backends.cudnn.benchmark` is enabled by default to optimize performance on your specific card. | |
| ### Legacy Support | |
| We strive to support NVIDIA drivers released in the last 5 years. If you are on an older GPU (e.g., Maxwell/Pascal architecture) that does not support CUDA 12.x: | |
| 1. Ensure your driver supports at least CUDA 11.8. | |
| 2. Use the legacy environment file: | |
| ```bash | |
| conda env create -f environment-legacy.yml | |
| conda activate deoldify-legacy | |
| ``` | |
| --- | |
| ## 🔵 Intel GPUs (New!) | |
| We now support Intel's discrete GPUs, including the Arc A-Series and Data Center GPU Flex/Max series, via the **Intel® Extension for PyTorch (IPEX)**. | |
| ### Requirements | |
| * **Hardware**: Intel Arc A750, A770, or Data Center GPU Flex/Max Series. | |
| * **Software**: Intel® Graphics Driver (latest stable release). | |
| * **Environment**: Requires a specific Conda environment (see setup guide). | |
| ### Performance Notes | |
| * **XPU Acceleration**: DeOldify automatically detects Intel GPUs as `xpu` devices. | |
| * **Memory**: Intel Arc cards with 16GB VRAM (like the A770 16GB) are excellent for high-resolution video colorization. | |
| --- | |
| ## ⚪ CPU (Fallback) | |
| If no GPU is detected, DeOldify will fallback to CPU mode. | |
| ### Pros & Cons | |
| * **Pros**: Works on almost any computer. No complex driver setup. | |
| * **Cons**: Significantly slower than GPU. Video colorization may be impractical for long clips. | |
| * **Recommendation**: Use for testing or single image colorization if no GPU is available. | |
| --- | |
| ## 📊 Benchmarks (Estimated) | |
| | Task | RTX 4090 | Arc A770 | CPU (Core i9) | | |
| | :--- | :--- | :--- | :--- | | |
| | **Image (Artistic)** | < 1 sec | ~2 sec | ~10-20 sec | | |
| | **Video (1 min, 1080p)** | ~2 mins | ~4 mins | ~30+ mins | | |
| *Note: Benchmarks are approximate and depend on render factor and resolution.* | |