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# Installation Guide
This guide covers installation for different GPU generations and operating systems.
## Requirements
- Python 3.10.9
- Conda or Python venv
- Compatible GPU (RTX 10XX or newer recommended)
## Installation for RTX 10XX to RTX 40XX (Stable)
This installation uses PyTorch 2.6.0 which is well-tested and stable.
### Step 1: Download and Setup Environment
```shell
# Clone the repository
git clone https://github.com/deepbeepmeep/Wan2GP.git
cd Wan2GP
# Create Python 3.10.9 environment using conda
conda create -n wan2gp python=3.10.9
conda activate wan2gp
```
### Step 2: Install PyTorch
```shell
# Install PyTorch 2.6.0 with CUDA 12.4
pip install torch==2.6.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu124
```
### Step 3: Install Dependencies
```shell
# Install core dependencies
pip install -r requirements.txt
```
### Step 4: Optional Performance Optimizations
#### Sage Attention (30% faster)
```shell
# Windows only: Install Triton
pip install triton-windows
# For both Windows and Linux
pip install sageattention==1.0.6
```
#### Sage 2 Attention (40% faster)
```shell
# Windows
pip install triton-windows
pip install https://github.com/woct0rdho/SageAttention/releases/download/v2.1.1-windows/sageattention-2.1.1+cu126torch2.6.0-cp310-cp310-win_amd64.whl
# Linux (manual compilation required)
git clone https://github.com/thu-ml/SageAttention
cd SageAttention
pip install -e .
```
#### Flash Attention
```shell
# May require CUDA kernel compilation on Windows
pip install flash-attn==2.7.2.post1
```
## Installation for RTX 50XX (Beta)
RTX 50XX GPUs require PyTorch 2.7.0 (beta). This version may be less stable.
⚠️ **Important:** Use Python 3.10 for compatibility with pip wheels.
### Step 1: Setup Environment
```shell
# Clone and setup (same as above)
git clone https://github.com/deepbeepmeep/Wan2GP.git
cd Wan2GP
conda create -n wan2gp python=3.10.9
conda activate wan2gp
```
### Step 2: Install PyTorch Beta
```shell
# Install PyTorch 2.7.0 with CUDA 12.8
pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128
```
### Step 3: Install Dependencies
```shell
pip install -r requirements.txt
```
### Step 4: Optional Optimizations for RTX 50XX
#### Sage Attention
```shell
# Windows
pip install triton-windows
pip install sageattention==1.0.6
# Linux
pip install sageattention==1.0.6
```
#### Sage 2 Attention
```shell
# Windows
pip install triton-windows
pip install https://github.com/woct0rdho/SageAttention/releases/download/v2.1.1-windows/sageattention-2.1.1+cu128torch2.7.0-cp310-cp310-win_amd64.whl
# Linux (manual compilation)
git clone https://github.com/thu-ml/SageAttention
cd SageAttention
pip install -e .
```
## Attention Modes
WanGP supports several attention implementations:
- **SDPA** (default): Available by default with PyTorch
- **Sage**: 30% speed boost with small quality cost
- **Sage2**: 40% speed boost
- **Flash**: Good performance, may be complex to install on Windows
## Performance Profiles
Choose a profile based on your hardware:
- **Profile 3 (LowRAM_HighVRAM)**: Loads entire model in VRAM, requires 24GB VRAM for 8-bit quantized 14B model
- **Profile 4 (LowRAM_LowVRAM)**: Default, loads model parts as needed, slower but lower VRAM requirement
## Troubleshooting
### Sage Attention Issues
If Sage attention doesn't work:
1. Check if Triton is properly installed
2. Clear Triton cache
3. Fallback to SDPA attention:
```bash
python wgp.py --attention sdpa
```
### Memory Issues
- Use lower resolution or shorter videos
- Enable quantization (default)
- Use Profile 4 for lower VRAM usage
- Consider using 1.3B models instead of 14B models
### GPU Compatibility
- RTX 10XX, 20XX: Supported with SDPA attention
- RTX 30XX, 40XX: Full feature support
- RTX 50XX: Beta support with PyTorch 2.7.0
For more troubleshooting, see [TROUBLESHOOTING.md](TROUBLESHOOTING.md) |