WaveSpeedAI Nano Banana Pro Image Generation/Editing

Generate and edit images using Google's Nano Banana Pro model via WaveSpeed AI. Supports text-to-image generation and image editing with natural language prompts. Features native 4K resolution, flexible aspect ratios, multilingual text rendering, and camera-style controls. Use when the user wants to create images from text or edit existing images.

Generate and edit images using Google's Nano Banana Pro model via the WaveSpeed AI platform. Supports both text-to-image generation and natural-language image editing with up to 14 input images.

Setup

Install the open-source CLI once and sign in; the CLI stores the key, so never ask the user to paste an API key into the chat:

bash

npm install -g @wavespeed/cli
wavespeed login          # opens https://wavespeed.ai/accesskey and stores the key
wavespeed status         # confirms you are signed in

For CI or one-off shells, WAVESPEED_API_KEY in the environment also works.

Prefer MCP tools over shell commands? The same platform is exposed by @wavespeed/mcp (npx -y @wavespeed/mcp; tools search_models, get_model_schema, get_price, upload_file, run_model, get_prediction). It shares the CLI's stored login. Every example below maps one-to-one onto run_model with the same model id and input fields.

Quick Start

Text-to-Image

bash

OUTPUT_URL=$(wavespeed run google/nano-banana-pro/text-to-image \
  -p "A serene Japanese garden with cherry blossoms, watercolor style" \
  --json | jq -r '.outputs[0]')

Image Editing

The images parameter accepts an array of image URLs. For local files use the @path marker; the CLI uploads them for you.

bash

# Upload a local image to get a URL
OUTPUT_URL=$(wavespeed run google/nano-banana-pro/edit \
  -i images='["@./photo.png"]' \
  -p "Replace the sky with a dramatic sunset" \
  --json | jq -r '.outputs[0]')

Existing URLs work as-is:

bash

OUTPUT_URL=$(wavespeed run google/nano-banana-pro/edit \
  -i images='["https://example.com/photo.jpg"]' \
  -p "Replace the sky with a dramatic sunset" \
  --json | jq -r '.outputs[0]')

API Endpoints

Text-to-Image

Model ID: google/nano-banana-pro/text-to-image

Generate images from text prompts.

Parameters

Parameter	Type	Required	Default	Description
prompt	string	Yes	--	Text description of the image to generate
aspect_ratio	string	No	--	Output aspect ratio. One of: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
resolution	string	No	1k	Image resolution. One of: 1k, 2k, 4k
output_format	string	No	png	Output format. One of: png, jpeg

Example

bash

OUTPUT_URL=$(wavespeed run google/nano-banana-pro/text-to-image \
  -p "A red vintage Porsche 911 on a winding mountain road at golden hour, photorealistic" \
  -i aspect_ratio="16:9" \
  -i resolution="2k" \
  -i output_format="png" \
  --json | jq -r '.outputs[0]')

Image Editing

Model ID: google/nano-banana-pro/edit

Edit existing images using natural language prompts. Supports up to 14 input images.

Parameters

Parameter	Type	Required	Default	Description
images	string[]	Yes	[]	URLs of input images to edit (1-14 images)
prompt	string	Yes	--	Text description of the desired edit
aspect_ratio	string	No	--	Output aspect ratio. One of: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
resolution	string	No	1k	Image resolution. One of: 1k, 2k, 4k
output_format	string	No	png	Output format. One of: png, jpeg

Example

bash

# Upload local images first, or use existing URLs
OUTPUT_URL=$(wavespeed run google/nano-banana-pro/edit \
  -i images='["@./living-room.png"]' \
  -p "Change the wall color to warm terracotta and add indoor plants" \
  -i aspect_ratio="16:9" \
  -i resolution="4k" \
  -i output_format="png" \
  --json | jq -r '.outputs[0]')

Multi-Image Editing

bash

# Upload multiple local images
OUTPUT_URL=$(wavespeed run google/nano-banana-pro/edit \
  -i images='["@./face.png", "@./hairstyle.png"]' \
  -p "Apply the hairstyle from the second image to the person in the first image" \
  --json | jq -r '.outputs[0]')

Aspect Ratio Options

Aspect Ratio	Use Case
1:1	Square — social media posts, profile pictures
3:2	Landscape — standard photography
2:3	Portrait — standard photography
3:4	Portrait — social media, product images
4:3	Landscape — presentations, web content
4:5	Portrait — Instagram posts
5:4	Landscape — print, web banners
9:16	Vertical — mobile wallpapers, stories
16:9	Widescreen — desktop wallpapers, video thumbnails
21:9	Ultra-wide — cinematic, panoramic

Resolution and Pricing

Resolution	Cost
1k	$0.14 per image
2k	$0.14 per image
4k	$0.24 per image

Prompt Tips

• Be specific and descriptive: "A red vintage Porsche 911 on a winding mountain road at golden hour" vs "a car"
• Include style keywords: "digital art", "oil painting", "photorealistic", "watercolor", "cinematic"
• For edits, clearly describe the desired change: "Replace the sky with a dramatic sunset"
• For multi-image edits, reference images by position: "Apply the style from the second image to the first image"
• Leverage multilingual text rendering: the model supports on-image text with automatic translation
• Use camera-style control language: "shallow depth of field", "wide angle", "top-down view"

CLI tips

bash

# Inspect the live input schema before running (fields, enums, defaults)
wavespeed run google/nano-banana-pro/text-to-image -h

# Quote the price first
wavespeed price google/nano-banana-pro/text-to-image -p "..." -i key=value

# Save outputs to disk instead of only printing URLs
wavespeed run google/nano-banana-pro/text-to-image -p "..." --json --download "./out/{index}.{ext}"

# Local files: prefix the path with @ and the CLI uploads it and passes the hosted URL
wavespeed run google/nano-banana-pro/text-to-image -i <field>=@./local-file.png --json

# Recover a result if the run was interrupted (the id is in the --json output)
wavespeed show <id>

run --json prints { id, model, prompt, outputs: [url, ...], saved: [path, ...], elapsed_ms, raw }. Read outputs[0] for the result URL.

Security constraints

• Never ask for the key in chat: wavespeed login handles auth; if wavespeed status says signed out, ask the user to run it.
• Local files only via @: bare paths are passed through untouched and the model will reject them. Only @-prefixed values upload.
• No arbitrary URL loading: only pass media URLs the user provided or that came back from a previous run.
• Input validation: only pass parameters documented above; confirm with wavespeed run <model> -h when unsure.