How to Use Nano Banana via API :Developer's Guide

Google has once again pushed the boundaries with its powerful image generation model, colloquially known as “Nano Banana.”

Officially named Gemini 2.5 Flash Image, this state-of-the-art model offers an unprecedented level of control and creativity in generating and manipulating images programmatically.

This comprehensive guide will walk you through everything you need to know to harness the power of the Nano Banana via the Gemini API, enabling you to build the next generation of AI-powered visual applications.

This guide is designed for developers and technical content creators, providing a clean, practical, and fluff-free walkthrough of the Gemini image model’s capabilities.

We’ll cover everything from setting up your environment to advanced image editing techniques, all while incorporating key concepts to help you create high-ranking, valuable content.

Understanding Nano Banana: More Than Just Image Generation

Before diving into the technical details, it’s crucial to understand what makes Nano Banana so special.

It’s not just a text-to-image generator; it’s a conversational and contextual image powerhouse. This means you can engage in a dialogue with the model, iteratively refining your images until they are perfect.

Key Capabilities of Nano Banana:

• Text-to-Image Generation: Create high-quality images from simple or complex text descriptions.

• Image and Text-to-Image Editing: Provide an image and use text prompts to add, remove, or modify elements, change styles, or adjust colors.

• Multi-Image Composition and Style Transfer: Use multiple input images to create a new scene or transfer the artistic style from one image to another.

• Iterative Refinement: Engage in a back-and-forth conversation to make precise adjustments to your images.

• High-Fidelity Text Rendering: Accurately generate images that contain legible and well-placed text, perfect for logos and posters.

All images generated with Gemini include a SynthID watermark, a digital identifier that helps distinguish them as AI-generated.

Getting Started: Your First Steps with the Gemini API

To begin your journey with Nano Banana, you’ll need to set up your development environment and obtain an API key.

1. Obtaining Your API Key

Your Gemini API key is your passport to accessing the model’s capabilities. You can get your free API key from Google AI Studio.

Simply sign in with your Google account and follow the prompts to create a new API key. Remember to keep this key secure and avoid exposing it in your client-side code.

2. Setting Up Your Development Environment

The Gemini API is accessible through various programming languages, with robust support for Python and JavaScript (Node.js).

For Python:

You’ll need to install the google-generativeai library. You can do this using pip:

bash

pip install google-generativeai

For JavaScript (Node.js):

You’ll need to install the @google/genai package using npm:

bash

npm install @google/genai

Once you have the necessary libraries installed, you can configure your application with your API key.

It’s a best practice to store your API key as an environment variable (e.g., GEMINI_API_KEY) to keep it secure.

Basic Image Generation: From Text to Pixels

Now that your environment is set up, let’s dive into the core functionality: generating images from text prompts.

The key to success with Nano Banana is to be descriptive. Instead of a list of keywords, a narrative and detailed paragraph will yield better and more coherent results.

Python Implementation

The following code sends a text prompt to the model. It then processes the response to find the image data and saves it to a file using the Pillow library.

python

import os

from google import genai

from PIL import Image

from io import BytesIO

# Configure the client with your API key from an environment variable

genai.configure(api_key="YOUR_API_KEY")

# Define the model for image generation

model = genai.GenerativeModel(model_name="gemini-1.5-flash")

# The descriptive text prompt for the image

prompt = "A photorealistic image of a futuristic city at sunset, with flying cars and lush vertical gardens on the skyscrapers."

# Generate the content

response = model.generate_content(prompt)

# Extract and save the image data from the response

try:

    image_part = next(p for p in response.candidates[0].[content.parts](http://content.parts) if p.inline_data)

    image_data = image_part.inline_[data.data](http://data.data)

    # Open the image from bytes and save it

    image = [Image.open](http://Image.open)(BytesIO(image_data))

    [image.save](http://image.save)("futuristic_city.png")

    print("Image saved as futuristic_city.png")

except StopIteration:

    print("No image data found in the response.")

JavaScript (Node.js) Implementation

This example performs the same text-to-image generation in a Node.js environment.

It finds the base64-encoded image data in the response, converts it to a buffer, and writes it to a file.

javascript

import { GoogleGenerativeAI } from "@google/genai";

import * as fs from "fs";

async function main() {

  // Configure the client with your API key

  const genAI = new GoogleGenerativeAI("YOUR_API_KEY");

  // Define the model for image generation

  const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

  const prompt =

    "A whimsical watercolor painting of a red panda enjoying a cup of tea in a cherry blossom forest.";

  const result = await model.generateContent(prompt);

  const response = result.response;

  // Find the image part in the response

  const imagePart = response.candidates[0].[content.parts](http://content.parts).find(part => part.inlineData);

  if (imagePart) {

    const imageData = [imagePart.inlineData.data](http://imagePart.inlineData.data);

    const buffer = Buffer.from(imageData, "base64");

    fs.writeFileSync("red_panda_watercolor.png", buffer);

    console.log("Image saved as red_panda_watercolor.png");

  } else {

    console.log("No image data found in the response.");

  }

}

main();

Advanced Image Manipulation: Unleash Your Creativity

Nano Banana truly shines when it comes to its advanced image editing and composition capabilities.

This is where the conversational and contextual nature of the model comes into play.

Image Editing with Text Prompts

You can provide an image along with a text prompt to make specific changes.

The model will intelligently apply the edits while maintaining the original image’s style and lighting.

Python Implementation for Image Editing:

This code loads a local image and passes it with a text prompt to instruct the model on how to edit it.

python

import os

from google import genai

from PIL import Image

from io import BytesIO

genai.configure(api_key="YOUR_API_KEY")

model = genai.GenerativeModel(model_name="gemini-1.5-flash")

# The instructional text prompt

prompt = "Using the provided image of my cat, please add a small, knitted wizard hat on its head. Ensure the lighting on the hat matches the rest of the image."

# Open the local image file

try:

    source_image = [Image.open](http://Image.open)("path/to/your_cat_image.png")

except FileNotFoundError:

    print("Error: Source image not found. Please update the file path.")

    exit()

# Send both the prompt and the image as a list of contents

response = model.generate_content([prompt, source_image])

# Extract and save the newly generated image

try:

    image_part = next(p for p in response.candidates[0].[content.parts](http://content.parts) if p.inline_data)

    image_data = image_part.inline_[data.data](http://data.data)

    image = [Image.open](http://Image.open)(BytesIO(image_data))

    [image.save](http://image.save)("cat_with_hat.png")

    print("Image saved as cat_with_hat.png")

except StopIteration:

    print("No edited image data found in the response.")

Inpainting: Targeted Edits with Precision

Inpainting allows you to define a specific area of an image to edit while leaving the rest untouched. You can achieve this conversationally by describing the element you want to change.

For example, with an image of a living room, you could use a prompt like: “Using the provided image, change only the blue sofa to be a vintage, brown leather chesterfield sofa. Keep everything else in the image exactly the same, preserving the original style, lighting, and composition.”

Multi-Image Composition and Style Transfer

One of the most powerful features is the ability to combine elements from multiple images or transfer the style of one image to another.

This is perfect for creating product mockups or artistic compositions. For instance, by providing an image of a dress and an image of a model, you can prompt: “Create a new image by placing the dress from the first image onto the model from the second image. The final image should be a professional e-commerce fashion photo.”

Bringing Your Creations to Life: Practical Applications

The capabilities of the Nano Banana API open up a world of possibilities for developers. Here are just a few ideas to get you started:

• AI-Powered Design Tools: Build applications that allow users to generate logos, social media graphics, and other marketing materials with simple text prompts.

• Virtual Try-On Experiences: For e-commerce, allow customers to see how clothing or accessories would look on a model or even themselves.

• Creative Storytelling Platforms: Develop tools that can generate consistent characters and scenes for AI-driven animations and storybooks, a concept further explored in our guide on how to use Nano Banana in your AI animation workflow).

• Automated Product Photography: Generate high-quality product images in various settings without the need for a physical photoshoot. For more detail, see our tutorial on how to create product photography with Nano Banana).

Gemini 2.5 Flash Image Pricing

The Gemini 2.5 Flash Image model uses a pay-as-you-go structure after the free tier.

Since this is a Preview model, pricing may change in the future.

💰 Pricing Breakdown

• Input Price: $0.30 per 1 million tokens (applies to both text and image inputs).

• Output Price: $0.039 per generated image.

🔢 Token Calculation

• Image outputs (up to 1024 × 1024 px) consume a fixed 1290 tokens.

• At the rate of $30 per 1 million tokens, this equals ~$0.039 per image.

Troubleshooting Common Issues

As with any API, you may occasionally encounter errors. Here are a few common issues and how to address them:

• 400: Bad Request*:** This often indicates a malformed request. Double-check your prompt and that any image data you are sending is correctly formatted.

• 403: Forbidden*:** This typically points to an issue with your API key. Ensure it is correct, valid, and has the necessary permissions enabled.

• 429: Resource Exhausted*:** You have exceeded your request rate limit. Consider implementing exponential backoff for retries or checking your quota limits in the Google Cloud console.

For a more comprehensive list of potential errors and their solutions, refer to the official Gemini API troubleshooting guide.

The Future is Visual: Embracing the Power of Nano Banana

Google’s Gemini 2.5 Flash Image, or Nano Banana, represents a significant leap forward in AI image generation and editing.

Its intuitive, conversational nature, combined with its powerful feature set, empowers developers to create visually stunning and highly customized content.

By following this guide and continuing to explore the extensive documentation and community resources,

you can unlock the full potential of this groundbreaking technology and build the next wave of innovative visual applications.