luma-pro-cli 2026.3.27.1

CLI tool for Luma Dream Machine AI Video Generation via AceDataCloud API

Luma CLI

A command-line tool for AI video generation using Luma 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- Video Extension — Extend existing videos
• Multiple Models — luma
• 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 Luma API page
3. Click "Acquire" to get your token

2. Install

# Install with pip
pip install luma-pro-cli

# Or with uv (recommended)
uv pip install luma-pro-cli

# Or from source
git clone https://github.com/AceDataCloud/LumaCli.git
cd LumaCli
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
luma generate "A test video"

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

# Extend a video
luma extend <video-id>

# Check task status
luma task <task-id>

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

# List available models
luma models

Commands

Command	Description
luma generate <prompt>	Generate a video from a text prompt
luma image-to-video <prompt> -i <url>	Generate a video from reference image(s)
luma extend <video_id>	Extend an existing video
luma task <task_id>	Query a single task status
luma tasks <id1> <id2>...	Query multiple tasks at once
luma wait <task_id>	Wait for task completion with polling
luma models	List available Luma models
luma config	Show current configuration
luma aspect-ratios	List available aspect ratios

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    Luma model version (default: luma)

Available Models

Model	Version	Notes
luma	Standard	Standard quality video generation (default)

Configuration

Environment Variables

Variable	Description	Default
ACEDATACLOUD_API_TOKEN	API token from AceDataCloud	Required
ACEDATACLOUD_API_BASE_URL	API base URL	https://api.acedata.cloud
LUMA_DEFAULT_MODEL	Default model	luma
LUMA_REQUEST_TIMEOUT	Timeout in seconds	1800

Development

Setup Development Environment

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

Run Tests

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

Code Quality

ruff format .
ruff check .
mypy luma_cli

Docker

docker pull ghcr.io/acedatacloud/luma-pro-cli:latest
docker run --rm -e ACEDATACLOUD_API_TOKEN=your_token \
  ghcr.io/acedatacloud/luma-pro-cli generate "A test video"

Project Structure

LumaCli/
├── luma_cli/                # Main package
│   ├── __init__.py
│   ├── __main__.py            # python -m luma_cli entry point
│   ├── main.py                # CLI entry point
│   ├── core/                  # Core modules
│   │   ├── client.py          # HTTP client for Luma 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
├── .github/workflows/         # CI/CD (lint, test, publish to PyPI)
├── Dockerfile                 # Container image
├── deploy/                    # Kubernetes deployment configs
├── .env.example               # Environment template
├── pyproject.toml             # Project configuration
└── README.md

Luma CLI vs MCP Luma

Feature	Luma CLI	MCP Luma
Interface	Terminal commands	MCP protocol
Usage	Direct shell, scripts, CI/CD	Claude, VS Code, MCP clients
Output	Rich tables / JSON	Structured MCP responses
Automation	Shell scripts, piping	AI agent workflows
Install	pip install luma-pro-cli	pip install mcp-luma

Both tools use the same AceDataCloud API and share the same API token.

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing)
5. Open a Pull Request

Development Requirements

• Python 3.10+
• Dependencies: pip install -e ".[all]"
• Lint: ruff check . && ruff format --check .
• Test: pytest

License

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