File size: 2,989 Bytes
e9f9fd3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
# 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.*
|