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

# 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 2.6.0 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

# Install core dependencies
pip install -r requirements.txt

Step 4: Optional Performance Optimizations

Sage Attention (30% faster)

# 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)
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

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

# 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

# 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

pip install -r requirements.txt

Step 4: Optional Optimizations for RTX 50XX

Sage Attention

# Windows
pip install triton-windows 
pip install sageattention==1.0.6 

# Linux
pip install sageattention==1.0.6

Sage 2 Attention

# 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:

Check if Triton is properly installed
Clear Triton cache
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

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