Introduction

Generative AI is evolving beyond single-image editing. Today’s creators and developers demand workflows that can take multiple input photos—such as product shots, portraits, or design assets—and merge them into coherent, creative composites with a single instruction. Enter Nano Banana, Google’s lightweight but powerful image model available through Google AI Studio templates.

This article explores how multi-image fusion works in Nano Banana, why it matters for developers and product designers, and how you can start building your own prompt-driven image editor with this capability. Whether you’re experimenting with creative composites or deploying editable AI apps at scale, Nano Banana provides the right balance of speed, control, and semantic accuracy.

What Is Multi-Image Fusion?

Multi-image fusion is the process of combining two or more input images into one unified output based on a text prompt. Instead of simply overlaying or collaging, the AI model interprets semantic cues from each image and merges them in a way that respects composition, lighting, and style.

For example:

• E-commerce: Merge product shots from different angles into a single marketing visual.
• Design prototypes: Blend UI mockups with branding elements to generate pitch-ready composites.
• Creative work: Place a subject from one photo into a new scene background.

Traditional editing would require layers, masks, and hours of manual retouching. With Nano Banana, developers can achieve the same effect with one well-crafted prompt. If you’re unfamiliar with the model itself, check out What Is Nano Banana? A Complete Guide to Google’s Gemini 2.5 Flash Image Model for a foundation.

Why Nano Banana Excels at Fusion

Nano Banana isn’t just another generative model. It has been optimized for prompt-based editing workflows that make it:

• Lightweight: Runs fast in browser-based apps and developer prototypes.
• Flexible: Handles object removal, background changes, and multi-image fusion equally well.
• Knowledge-enhanced: Powered by Gemini’s broader world knowledge for context-aware results (see our blog on how Gemini boosts Nano Banana’s intelligence).
• Developer-friendly: Provided as Nano Banana Templates in Google AI Studio, ready to remix.

This makes it one of the most developer-friendly AI tools for applied image generation today.

Using Nano Banana Templates for Multi-Image Fusion

Nano Banana is packaged into Google AI Studio templates that let you quickly spin up editing apps. For multi-image fusion, you typically start with:

1. Input slots for two or more images.
2. A text field where the user describes the composite (e.g., “Place the product in a beach setting at sunset”).
3. Model call that sends both the images and the prompt into Nano Banana for processing.

Want a hands-on walkthrough? Read our tutorial: How to Use Nano Banana via Google Gemini: A Step-by-Step Tutorial.

Example: Fusion Prompt

1{
2  "images": [
3    "product_front.png",
4    "product_side.png"
5  ],
6  "prompt": "Merge both views of the shoe into a single hero shot on a white background."
7}

The model outputs a clean, unified image ready for e-commerce use.

Step-by-Step Guide to Multi-Image Fusion with Nano Banana

Here’s a simplified step-by-step guide to AI image editing apps using Nano Banana:

1. Set Up Google AI Studio Template

• Open Google AI Studio.
• Select a Nano Banana template for AI image editing tools.
• Clone it into your project workspace.

(If you’re looking to dive deeper into template customization, check out Building a Prompt-Driven Image Editor with Nano Banana Templates.)

2. Configure Multi-Image Inputs

• Add multiple file input components in your app.
• Ensure the backend is configured to accept multiple image URLs or file paths.

3. Write the Prompt Logic

• Define a schema where the prompt + image inputs are bundled into one request.
• Example prompts:
  “Combine these two faces into a family portrait.”
  “Blend the product with this lifestyle background.”

4. Run the Fusion Model

• Call the Nano Banana API with inputs.
• Handle latency gracefully; Nano Banana is optimized for fast response, but batching helps.

(Developers interested in API setup can also read Getting Started with the Nano Banana API in AI Studio and Vertex AI.)

5. Deploy & Share

• Wrap the app for editable AI apps deployment.
• Publish for internal teams or as a client-facing tool.

This workflow shows how to build a prompt-driven image editor that scales beyond single-image tasks.

Practical Applications

For Product Designers

• Test brand placement in new environments.
• Quickly produce variants for pitch decks and websites.

For E-Commerce Teams

• Automate catalog generation by merging product + background shots.
• Use an AI-powered object removal editor to clean up source images before fusion.

For Creators

• Experiment with background change with AI to instantly place characters or objects into imaginative scenes.
• Use remixing AI templates to iterate on visual ideas faster.

For Developers

• Prototype multi-turn editing workflows without building models from scratch. (See Behind the Scenes: How Gemini 2.5 Flash Image Processes Multi-Prompt Edits for deeper context.)
• Extend Nano Banana into pipelines for applied AI in product design.

Best Practices for Multi-Image Fusion Prompts

A successful output depends on prompt clarity. Consider these tips:

• Be explicit about layout: e.g., “Place image A in the center, image B in the background.”
• Define lighting and style: e.g., “Merge into a cinematic, soft-lit scene.”
• Request cleanup: e.g., “Remove extra shadows and unify perspective.”

If you’re curious about maintaining subject likeness in repeated edits, read How Nano Banana Maintains Character Consistency Across Edits.

Google AI Studio Image Editing Examples

Here are a few real-world Google AI Studio image editing examples of multi-image fusion:

• Logo + mockup: Blend a brand logo into product packaging visuals.
• Lifestyle composites: Merge people into settings (e.g., athletes into stadiums).
• Marketing campaigns: Combine seasonal assets (e.g., holiday backgrounds with existing product shots).

For transparency in AI outputs, Nano Banana applies SynthID watermarks—learn more in Understanding SynthID Watermarks: Visible vs Invisible AI Authorship Labels.

Technical Considerations

While powerful, multi-image fusion isn’t magic. Developers should plan for:

• Resolution limits: Nano Banana templates are optimized for speed; very high-res outputs may require upscaling.
• Edge alignment: Fusion works best with clean, well-cropped inputs.
• Watermarking: Outputs include SynthID watermarks, making authorship traceable.
• Ethics & usage: Always verify rights for input images before generating composites.

Building Beyond Fusion

Multi-image fusion is one part of a broader ecosystem. By building with AI templates, teams can create:

• End-to-end AI-powered image editors.
• Custom workflows that combine object removal, background change, and fusion.
• Integrated pipelines for design, marketing, or commerce.

To see how Nano Banana fits into the bigger picture, check out Nano Banana in OpenRouter: Bringing Google’s Image Model to 3M+ Developers.

Conclusion

Nano Banana is more than a novelty—it’s a practical, lightweight, and flexible model that enables multi-image fusion with nothing more than a text prompt. By leveraging Google AI Studio templates, developers and creators can rapidly prototype and deploy custom AI image editing tools.

For product designers, marketers, and creators, this means faster iteration, richer visuals, and fewer manual bottlenecks. For developers, it means a ready-made framework for deploying custom AI image editors that integrate directly into real-world pipelines.

If you’re looking to experiment with the next generation of AI tools for product designers and generative AI for creators, start exploring Nano Banana Templates today—and build the prompt-driven image editor your workflow has been waiting for.