NanoBanana CLI

A command-line tool for AI image generation and editing using NanoBanana (Gemini-powered) through the AceDataCloud API.

Generate and edit AI images directly from your terminal — no MCP client required.

Features

• Image Generation — Generate images from text prompts with multiple models
• Image Editing — Edit, combine, and transform images with AI
• Multiple Models — nano-banana (fast), nano-banana-2 (improved), nano-banana-2.1 (efficient, 1K/2K/4K), nano-banana-pro (best, 4K)
• Flexible Output — Aspect ratios (1:1, 16:9, 9:16, etc.) and resolutions (1K/2K/4K)
• 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

1. Open Ace Data Cloud → Applications. Sign in, or register an account first.
2. Find General application. If it has not been created, choose Initialize General Application. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and current pricing.
3. Copy the API Key from the application card, or open Keys / Manage Keys → Create to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.
4. When creating a key, review Expiration Time, Set Usage Limitation, and Allowed APIs. Enable only the restrictions you need, allow the operations you will call, then click Create and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.
5. Copy only the token value, without quotation marks, spaces, or Bearer . An API Key / API Token field expects this raw value; only a manually configured HTTP Authorization header needs the Bearer prefix. A platform-... account-management token cannot replace a generation API key.

Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.

2. Install

# Install with pip
pip install nano-banana-pro-cli

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

# Or from source
git clone https://github.com/AceDataCloud/NanoBananaCli.git
cd NanoBananaCli
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 an image from a prompt
nano-banana-pro generate "A cat sitting on a windowsill at sunset, warm lighting"

# Generate with specific model and aspect ratio
nano-banana-pro generate "Product photo of a watch" -m nano-banana-pro -a 16:9 -r 4K

# Generate and edit with Nano Banana 2.1
nano-banana-pro generate "A blue ceramic vase on a cream background" -m nano-banana-2.1 -r 2K
nano-banana-pro edit "Make the vase green" -i https://example.com/photo.jpg -m nano-banana-2.1 -r 4K

# Edit an image
nano-banana-pro edit "Make it look like an oil painting" -i https://example.com/photo.jpg

# Virtual try-on (combine person + clothing)
nano-banana-pro edit "Let this person wear this T-shirt" -i person.jpg -i shirt.jpg

# Check task status
nano-banana-pro task <task-id>

# Wait for completion with polling
nano-banana-pro wait <task-id> --interval 5

# List available models
nano-banana-pro models

Commands

Image Generation & Editing

Task Management

Utilities

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    NanoBanana model version (default: nano-banana)

Scripting & Piping

The --json flag outputs machine-readable JSON suitable for piping:

# Generate and extract task ID
TASK_ID=$(nano-banana-pro generate "a red circle" --json | jq -r '.task_id')

# Wait for completion and get image URL
nano-banana-pro wait $TASK_ID --json | jq -r '.data[0].image_url'

# Batch generate from a file of prompts
while IFS= read -r prompt; do
  nano-banana-pro generate "$prompt" --json >> results.jsonl
done < prompts.txt

Available Models

Specify -m nano-banana-2.1 explicitly; the default remains nano-banana. Both generation and editing support -r 1K, -r 2K, or -r 4K. There is no nano-banana-2.1:official variant.

Aspect Ratios

Configuration

Environment Variables

Development

Setup Development Environment

# Clone repository
git clone https://github.com/AceDataCloud/NanoBananaCli.git
cd NanoBananaCli

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# Install with dev dependencies
pip install -e ".[dev,test]"

Run Tests

# Run unit tests
pytest

# Run with coverage
pytest --cov=nanobanana_cli

# Run integration tests (requires API token)
pytest tests/test_integration.py -m integration

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy nanobanana_cli

Build & Publish

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Docker

# Pull the image
docker pull ghcr.io/acedatacloud/nano-banana-pro-cli:latest

# Run a command
docker run --rm -e ACEDATACLOUD_API_TOKEN=your_token \
  ghcr.io/acedatacloud/nano-banana-pro-cli generate "A beautiful sunset"

# Or use docker-compose
docker compose run --rm nano-banana-pro-cli generate "A beautiful sunset"

Project Structure

NanoBananaCli/
├── nanobanana_cli/            # Main package
│   ├── __init__.py
│   ├── __main__.py            # python -m nanobanana_cli entry point
│   ├── main.py                # CLI entry point
│   ├── core/                  # Core modules
│   │   ├── client.py          # HTTP client for NanoBanana API
│   │   ├── config.py          # Configuration management
│   │   ├── exceptions.py      # Custom exceptions
│   │   └── output.py          # Rich terminal formatting
│   └── commands/              # CLI command groups
│       ├── image.py           # Image generation & editing 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

NanoBanana CLI vs MCP NanoBanana

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.