Nano Banana Pro Image Generator

Generate images using Google's advanced Nano Banana Pro model (gemini-3-pro-image-preview).

Prerequisites

The user must have GEMINI_API_KEY environment variable set with a valid Google AI API key.

Usage

The script is located in the same directory as this SKILL.md file. Run it with uv run:

uv run /path/to/skills/nano-banana-pro/generate_image.py "your prompt" -o output.png

When this skill is invoked, locate generate_image.py in the skill directory and run it.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the image to generate or transformation to apply
-o, --output	Yes	Output filename (you decide the path based on context)
-i, --image	No	Input image(s) for editing/transformation or as context/reference (can be used multiple times)
--aspect-ratio	No	One of: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 (default: 1:1)
--size	No	Image size: 1K, 2K, 4K (default: 1K)

Examples

Basic image generation:

uv run generate_image.py "A sunset over mountains" -o sunset.png

Infographic with specific aspect ratio:

uv run generate_image.py "Infographic showing the water cycle with labeled stages" -o water_cycle.png --aspect-ratio 9:16

High-resolution ultrawide:

uv run generate_image.py "Professional photo of a modern office space" -o office.png --aspect-ratio 21:9 --size 4K

Edit an existing image:

uv run generate_image.py "Add a sunset sky to this image" -i photo.png -o edited.png

Transform with style:

uv run generate_image.py "Make this look like a watercolor painting" -i input.jpg -o watercolor.png

Combine multiple images:

uv run generate_image.py "Create a collage blending these images together" -i img1.png -i img2.png -o collage.png

Use image as context/reference:

uv run generate_image.py "Generate a new landscape in the same style as this reference" -i reference.png -o new_landscape.png

Model Capabilities

Nano Banana Pro excels at:

• Accurate infographics with real data (uses Google Search grounding)
• Text rendering in images
• Image editing and transformation from input images
• Context-aware generation using reference images for style, composition, or subject
• Cartographic visualizations and maps
• Detailed instruction following
• Chain-of-thought reasoning for complex visual tasks

Output

The script prints:

• Progress message while generating
• Path to saved image on success
• Any text response from the model
• Error message if no image was generated