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Getting Started with WanGP

This guide will help you get started with WanGP video generation quickly and easily.

Prerequisites

Before starting, ensure you have:

  • A compatible GPU (RTX 20XX or newer recommended)
  • Python 3.11.14 for manual installation; guided installers can prepare Python for you
  • At least 6GB of VRAM for basic models
  • Internet connection for model downloads

Quick Setup

Method 1: Bat/SH Scripts

Use the included scripts/install.bat on Windows or scripts/install.sh on Linux/macOS. See the script instructions.

Method 2: Pinokio

Use Pinokio App with the community wan2gp or wan2gp-amd scripts by Morpheus.

Method 3: Wan2GP Desktop

Wan2GP Desktop by GKArtist installs, updates and launches WanGP from a desktop window.

Method 4: WanGP Manager (Experimental, Windows)

WanGP Manager is the official experimental Windows x64 app for guided installation, launching, updates and rollback. Download: WanGPManager.exe. This permanent link downloads the latest Manager release.

Run the executable and follow Manager's Install tab. It prepares Python and dependencies, recommends a profile for your detected GPU and lets you select a WanGP version. After installation, click Start WanGP, then Open in App or Open in Browser. See the Manager instructions.

Manual Installation

git clone https://github.com/deepbeepmeep/WanGP.git
cd WanGP
conda create -n wan2gp python=3.11.14
conda activate wan2gp
pip install torch==2.10.0 torchvision==0.25.0 torchaudio==2.10.0 --index-url https://download.pytorch.org/whl/cu130
pip install -r requirements.txt

For detailed installation instructions, see INSTALLATION.md.

First Launch

With WanGP Manager, use Start WanGP and choose Open in App or Open in Browser. With another guided installer, use its launch action. The command below is for a manual installation.

Basic Launch

python wgp.py

This launches the WanGP generator with default settings. You will be able to pick from a Drop Down menu which model you want to use.

Alternative Modes

python wgp.py --i2v        # Wan Image-to-video mode
python wgp.py --t2v-1-3B   # Wan Smaller, faster model

Understanding the Interface

When you launch WanGP, you'll see a web interface with several sections:

Main Generation Panel

  • Model Selection: Dropdown to choose between different models
  • Prompt: Text description of what you want to generate
  • Generate Button: Start the video generation process

Advanced Settings (click checkbox to enable)

  • Generation Settings: Steps, guidance, seeds
  • Loras: Additional style customizations
  • Sliding Window: For longer videos

Your First Video

Let's generate a simple text-to-video:

  1. Launch WanGP: python wgp.py
  2. Open Browser: Navigate to http://localhost:7860
  3. Enter Prompt: "A cat walking in a garden"
  4. Click Generate: Wait for the video to be created
  5. View Result: The video will appear in the output section

Recommended First Settings

  • Model: Wan 2.1 text2video 1.3B (faster, lower VRAM)
  • Frames: 49 (about 2 seconds)
  • Steps: 20 (good balance of speed/quality)

Model Selection

Text-to-Video Models

  • Wan 2.1 T2V 1.3B: Fastest, lowest VRAM (6GB), good quality
  • Wan 2.1 T2V 14B: Best quality, requires more VRAM (12GB+)
  • Hunyuan Video: Excellent quality, slower generation
  • LTX Video: Good for longer videos

Image-to-Video Models

  • Wan Fun InP 1.3B: Fast image animation
  • Wan Fun InP 14B: Higher quality image animation
  • VACE: Advanced control over video generation

Choosing the Right Model

  • Low VRAM (6-8GB): Use 1.3B models
  • Medium VRAM (10-12GB): Use 14B models or Hunyuan
  • High VRAM (16GB+): Any model, longer videos

Basic Settings Explained

Generation Settings

  • Frames: Number of frames (more = longer video)

    • 25 frames ≈ 1 second
    • 49 frames ≈ 2 seconds
    • 73 frames ≈ 3 seconds

  • Steps: Quality vs Speed tradeoff

    • 15 steps: Fast, lower quality
    • 20 steps: Good balance
    • 30+ steps: High quality, slower

  • Guidance Scale: How closely to follow the prompt

    • 3-5: More creative interpretation
    • 7-10: Closer to prompt description
    • 12+: Very literal interpretation

Seeds

  • Random Seed: Different result each time
  • Fixed Seed: Reproducible results
  • Use same seed + prompt: Generate variations

Common Beginner Issues

"Out of Memory" Errors

  1. Use smaller models (1.3B instead of 14B)
  2. Reduce frame count
  3. Lower resolution in advanced settings
  4. Enable quantization (usually on by default)

Slow Generation

  1. Use 1.3B models for speed
  2. Reduce number of steps
  3. Install Sage attention (see INSTALLATION.md)
  4. Enable TeaCache: python wgp.py --teacache 2.0

Poor Quality Results

  1. Increase number of steps (25-30)
  2. Improve prompt description
  3. Use 14B models if you have enough VRAM
  4. Enable Skip Layer Guidance in advanced settings

Writing Good Prompts

Basic Structure

[Subject] [Action] [Setting] [Style/Quality modifiers]

Examples

A red sports car driving through a mountain road at sunset, cinematic, high quality

A woman with long hair walking on a beach, waves in the background, realistic, detailed

A cat sitting on a windowsill watching rain, cozy atmosphere, soft lighting

Tips

  • Be specific about what you want
  • Include style descriptions (cinematic, realistic, etc.)
  • Mention lighting and atmosphere
  • Describe the setting in detail
  • Use quality modifiers (high quality, detailed, etc.)

Next Steps

Once you're comfortable with basic generation:

  1. Explore Advanced Features:

  2. Improve Performance:

    • Install better attention mechanisms
    • Optimize memory settings
    • Use compilation for speed

  3. Join the Community:

    • Discord Server - Get help and share videos
    • Share your best results
    • Learn from other users

Troubleshooting First Steps

Installation Issues

  • Ensure Python 3.10.9 is used
  • Check CUDA version compatibility
  • See INSTALLATION.md for detailed steps

Generation Issues

  • Check GPU compatibility
  • Verify sufficient VRAM
  • Try basic settings first
  • See TROUBLESHOOTING.md for specific issues

Performance Issues

  • Use appropriate model for your hardware
  • Enable performance optimizations
  • Check CLI.md for optimization flags

Remember: Start simple and gradually explore more advanced features as you become comfortable with the basics!

Applies to: First installation, launch and basic interface use. Example models and settings illustrate an initial workflow; project-specific models, settings and saved templates may differ.