Generating Consistent Imagery with Gemini Nano Banana

1. Overview

In this lab, you will learn to build a prompt-based generation pipeline for your image library.

You will complete the following steps:

• 1️⃣ Start with an archive image
• 2️⃣ Extract a character to create a brand-new reference image
• 3️⃣ Generate a series of illustrations using only prompts and the new assets

Here is a summary of what you'll achieve:

What you'll learn

• How to generate new consistent images from images and prompts
• How to create a character sheet
• How to use descriptive or imperative prompts
• How to benefit from Gemini's spatial understanding
• How to build an asset graph

What you'll need

• Familiarity running Python in a notebook (in Colab or any other Jupyter environment)
• A Google Cloud project (Vertex AI) or a Gemini API key (Google AI Studio) with billing enabled

ℹ️ The total cost to run this lab on Google Cloud is less than 1 USD.

Let's get started...

2. Before you begin

To use the Gemini API, you have two main options:

1. Via Vertex AI with a Google Cloud project
2. Via Google AI Studio with a Gemini API key

🛠️ Option 1 - Gemini API via Vertex AI

Requirements:

• A Google Cloud project
• The Vertex AI API must be enabled for this project

Enable the Vertex AI API

🛠️ Option 2 - Gemini API via Google AI Studio

Requirement:

• A Gemini API key

Learn more about getting a Gemini API key from Google AI Studio.

3. Run the notebook

Choose your preferred tool to open the notebook:

🧰 Tool A - Open the notebook in Colab

Open in Colab

💡 This is the most straightforward tool to run the notebook directly from your browser (zero configuration required), easily save and manage it in your Google Drive.

🧰 Tool B - Open the notebook in Colab Enterprise or Vertex AI Workbench

Open in Colab Enterprise Open in Vertex AI Workbench

💡 This might be preferred if you already have a Google Cloud project configured with a Colab Enterprise or Vertex AI Workbench instance.

🧰 Tool C - Get the notebook from GitHub and run it in your own environment

Get the notebook

⚠️ You will need to get the notebook from GitHub (or clone the repository) and run it in your own Jupyter environment.

🗺️ Notebook table of contents

For easier navigation, make sure to expand and use the table of contents. Example:

🏁 Run the notebook

You are ready. You can now follow and run the notebook. Have fun!...

4. Congratulations!

Congratulations for completing the codelab!

Learn more

• Check out the Nano Banana recipes notebook for more practical examples.
• Explore additional use cases in the Vertex AI Prompt Gallery.
• Stay updated by following the Vertex AI Release Notes.