Wan2.1/docs/INSTALLATION.md

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

# 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

# Install PyTorch 2.7.0 with CUDA 12.4
pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128

Step 3: Install Dependencies

# 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

# Windows only: Install Triton
pip install triton-windows 

# For both Windows and Linux
pip install sageattention==1.0.6 

Sage 2 Attention (40% faster)

# 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

# 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:

   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