Wan CLI

A command-line tool for AI video generation using Tongyi Wansiang through the AceDataCloud API.

Generate AI videos directly from your terminal — no MCP client required.

Features

• Video Generation — Generate videos from text prompts with multiple models
• Image-to-Video — Create videos from reference images
• Multiple Models — wan2.6-t2v, wan2.6-i2v, wan2.6-i2v-flash, wan2.6-r2v, wan3.0-video
• Task Management — Query tasks, batch query, wait with polling
• Rich Output — Beautiful terminal tables and panels via Rich
• JSON Mode — Machine-readable output with --json for piping

Quick Start

1. Get API Token

Get your API token from AceDataCloud Platform:

1. Sign up or log in
2. Navigate to the Wan API page
3. Click "Acquire" to get your token

2. Install

# Install with pip
pip install wan-cli

# Or with uv (recommended)
uv pip install wan-cli

# Or from source
git clone https://github.com/AceDataCloud/WanCli.git
cd WanCli
pip install -e .

3. Configure

# Set your API token
export ACEDATACLOUD_API_TOKEN=your_token_here

# Or use .env file
cp .env.example .env
# Edit .env with your token

4. Use

# Generate a video from text
wan generate "Astronauts shuttle from space to volcano"

# Generate from reference image
wan image-to-video "Animate this scene" -i https://example.com/photo.jpg

# Check task status
wan task <task-id>

# Wait for completion
wan wait <task-id> --interval 5

# List available models
wan models

Commands

Global Options

--token TEXT    API token (or set ACEDATACLOUD_API_TOKEN env var)
--version       Show version
--help          Show help message

Most commands support:

--json                        Output raw JSON (for piping/scripting)
--model TEXT                  Wan model version (default: wan2.6-t2v)
--resolution TEXT             Output resolution: 480P, 720P, 1080P
--duration TEXT               Duration in seconds: 2-30, or -1 for auto
--shot-type TEXT              Shot type: single, multi
--audio/--no-audio            Whether the video has sound
--prompt-extend/--no-prompt-extend  Enable prompt intelligent rewriting
--media TYPE=URL              Media item (repeatable)
--ratio TEXT                  Aspect ratio: adaptive, 16:9, 4:3, 1:1, 3:4, 9:16
--seed INTEGER                Random seed (0-2147483647)
--watermark/--no-watermark    Whether to add a watermark

Available Models

Configuration

Environment Variables

Development

Setup Development Environment

git clone https://github.com/AceDataCloud/WanCli.git
cd WanCli
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,test]"

Run Tests

pytest
pytest --cov=wan_cli
pytest tests/test_integration.py -m integration

Code Quality

ruff format .
ruff check .
mypy wan_cli

Docker

docker pull ghcr.io/acedatacloud/wan-cli:latest
docker run --rm -e ACEDATACLOUD_API_TOKEN=your_token \
  ghcr.io/acedatacloud/wan-cli generate "Astronauts shuttle from space to volcano"

Project Structure

WanCli/
├── wan_cli/                # Main package
│   ├── __init__.py
│   ├── __main__.py            # python -m wan_cli entry point
│   ├── main.py                # CLI entry point
│   ├── core/                  # Core modules
│   │   ├── client.py          # HTTP client for Wan API
│   │   ├── config.py          # Configuration management
│   │   ├── exceptions.py      # Custom exceptions
│   │   └── output.py          # Rich terminal formatting
│   └── commands/              # CLI command groups
│       ├── video.py           # Video generation commands
│       ├── task.py            # Task management commands
│       └── info.py            # Info & utility commands
├── tests/                     # Test suite
├── Dockerfile                 # Container image
├── deploy/                    # Kubernetes deployment configs
├── .env.example               # Environment template
├── pyproject.toml             # Project configuration
└── README.md

License

This project is licensed under the MIT License — see the LICENSE file for details.