mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
119 lines
2.9 KiB
Markdown
119 lines
2.9 KiB
Markdown
# 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 50XX (Stable)
|
|
|
|
This installation uses PyTorch 2.7.0 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 2.7.0 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
|
|
# Install core dependencies
|
|
pip install -r requirements.txt
|
|
```
|
|
|
|
### Step 4: Optional Performance Optimizations
|
|
|
|
#### Sage Attention (30% faster), don't install with RTX 50xx as it is not compatible
|
|
|
|
```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)
|
|
python -m pip install "setuptools<=75.8.2" --force-reinstall
|
|
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
|
|
```
|
|
|
|
|
|
## 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
|
|
|
|
### Attention GPU Compatibility
|
|
|
|
- RTX 10XX, 20XX: SDPA
|
|
- RTX 30XX, 40XX: SDPA, Flash Attention, Xformers, Sage, Sage2
|
|
- RTX 50XX: SDPA, SDPA, Flash Attention, Xformers, Sage2
|
|
|
|
## 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
|
|
|
|
|
|
For more troubleshooting, see [TROUBLESHOOTING.md](TROUBLESHOOTING.md)
|