Google’s Nano-Banana Simply Unlocked a New Period of Picture Technology

Picture by Creator | Gemini (nano-banana self portrait)

# Introduction

 
Picture era with generative AI has turn out to be a broadly used software for each people and companies, permitting them to immediately create their meant visuals with no need any design experience. Basically, these instruments can speed up duties that will in any other case take a big period of time, finishing them in mere seconds.

With the development of expertise and competitors, many trendy, superior picture era merchandise have been launched, equivalent to Stable Diffusion, Midjourney, DALL-E, Imagen, and plenty of extra. Every affords distinctive benefits to its customers. Nonetheless, Google just lately made a big affect on the picture era panorama with the discharge of Gemini 2.5 Flash Image (or nano-banana).

Nano-banana is Google’s superior picture era and enhancing mannequin, that includes capabilities like sensible picture creation, a number of picture mixing, character consistency, focused prompt-based transformations, and public accessibility. The mannequin affords far better management than earlier fashions from Google or its rivals.

This text will discover nano-banana’s capacity to generate and edit photos. We are going to reveal these options utilizing the Google AI Studio platform and the Gemini API inside a Python surroundings.

Let’s get into it.

# Testing the Nano-Banana Mannequin

 
To comply with this tutorial, you will have to register for a Google account and register to Google AI Studio. Additionally, you will want to amass an API key to make use of the Gemini API, which requires a paid plan as there is no such thing as a free tier out there.

In case you desire to make use of the API with Python, ensure that to put in the Google Generative AI library with the next command:

As soon as your account is about up, let’s discover tips on how to use the nano-banana mannequin.

First, navigate to Google AI Studio and choose the Gemini-2.5-flash-image-preview mannequin, which is the nano-banana mannequin we shall be utilizing.

With the mannequin chosen, you can begin a brand new chat to generate a picture from a immediate. As Google suggests, a elementary precept for getting the most effective outcomes is to describe the scene, not simply listing key phrases. This narrative strategy, describing the picture you envision, usually produces superior outcomes.

Within the AI Studio chat interface, you will see a platform just like the one beneath the place you’ll be able to enter your immediate.

We are going to use the next immediate to generate a photorealistic picture for our instance.

A photorealistic close-up portrait of an Indonesian batik artisan, arms stained with wax, tracing a flowing motif on indigo material with a canting pen. She works at a wood desk in a breezy veranda; folded textiles and dye vats blur behind her. Late-morning window gentle rakes throughout the material, revealing effective wax strains and the grain of the teak. Captured on an 85 mm at f/2 for mild separation and creamy bokeh. The general temper is targeted, tactile, and proud.

The generated picture is proven beneath:

As you’ll be able to see, the picture generated is sensible and faithfully adheres to the given immediate. In case you desire the Python implementation, you should use the next code to create the picture:

from google import genai
from google.genai import sorts
from PIL import Picture
from io import BytesIO
from IPython.show import show 

# Substitute 'YOUR-API-KEY' together with your precise API key
api_key = 'YOUR-API-KEY'
consumer = genai.Shopper(api_key=api_key)

immediate = "A photorealistic close-up portrait of an Indonesian batik artisan, arms stained with wax, tracing a flowing motif on indigo material with a canting pen. She works at a wood desk in a breezy veranda; folded textiles and dye vats blur behind her. Late-morning window gentle rakes throughout the material, revealing effective wax strains and the grain of the teak. Captured on an 85 mm at f/2 for mild separation and creamy bokeh. The general temper is targeted, tactile, and proud."

response = consumer.fashions.generate_content(
    mannequin="gemini-2.5-flash-image-preview",
    contents=immediate,
)

image_parts = [
    part.inline_data.data
    for part in response.candidates[0].content material.elements
    if half.inline_data
]

if image_parts:
    picture = Picture.open(BytesIO(image_parts[0]))
    # picture.save('your_image.png')
    show(picture)

In case you present your API key and the specified immediate, the Python code above will generate the picture.

We now have seen that the nano-banana mannequin can generate a photorealistic picture, however its strengths prolong additional. As talked about beforehand, nano-banana is especially highly effective for picture enhancing, which we’ll discover subsequent.

Let’s strive prompt-based picture enhancing with the picture we simply generated. We are going to use the next immediate to barely alter the artisan’s look:

Utilizing the offered picture, place a pair of skinny studying glasses gently on the artisan’s nostril whereas she attracts the wax strains. Guarantee reflections look sensible and the glasses sit naturally on her face with out obscuring her eyes.

The ensuing picture is proven beneath:

The picture above is similar to the primary one, however with glasses added to the artisan’s face. This demonstrates how nano-banana can edit a picture based mostly on a descriptive immediate whereas sustaining general consistency.

To do that with Python, you’ll be able to present your base picture and a brand new immediate utilizing the next code:

from PIL import Picture

# This code assumes 'consumer' has been configured from the earlier step
base_image = Picture.open('/path/to/your/picture.png')
edit_prompt = "Utilizing the offered picture, place a pair of skinny studying glasses gently on the artisan's nostril..."


response = consumer.fashions.generate_content(
    mannequin="gemini-2.5-flash-image-preview",
    contents=[edit_prompt, base_image])

Subsequent, let’s check character consistency by producing a brand new scene the place the artisan is wanting straight on the digital camera and smiling:

Generate a brand new and photorealistic picture utilizing the offered picture as a reference for id: the identical batik artisan now wanting up on the digital camera with a relaxed smile, seated on the identical wood desk. Medium close-up, 85 mm look with mushy veranda gentle, background jars subtly blurred.

The picture result’s proven beneath.

We have efficiently modified the scene whereas sustaining character consistency. To check a extra drastic change, let’s use the next immediate to see how nano-banana performs.

Create a product-style picture utilizing the offered picture as id reference: the identical artisan presenting a completed indigo batik material, arms prolonged towards the digital camera. Comfortable, even window gentle, 50 mm look, impartial background muddle.

The result’s proven beneath.

The ensuing picture exhibits a totally completely different scene however maintains the identical character. This highlights the mannequin’s capacity to realistically produce different content material from a single reference picture.

Subsequent, let’s strive picture type switch. We are going to use the next immediate to vary the photorealistic picture right into a watercolor portray.

Utilizing the offered picture as id reference, recreate the scene as a fragile watercolor on cold-press paper: unfastened indigo washes for the material, mushy bleeding edges on the floral motif, pale umbers for the desk and background. Preserve her pose holding the material, mild smile, and spherical glasses; let the veranda recede into gentle granulation and visual paper texture.

The result’s proven beneath.

The picture demonstrates that the type has been remodeled into watercolor whereas preserving the topic and composition of the unique.

Lastly, we’ll strive picture fusion, the place we add an object from one picture into one other. For this instance, I’ve generated a picture of a girl’s hat utilizing nano-banana:

Utilizing the picture of the hat, we’ll now place it on the artisan’s head with the next immediate:

Transfer the identical girl and pose open air in open shade and place the straw hat from the product picture on her head. Align the crown and brim to the pinnacle realistically; bow over her proper ear (digital camera left), ribbon tails drifting softly with gravity. Use mushy sky gentle as key with a delicate rim from the brilliant background. Keep true straw and lace texture, pure pores and skin tone, and a plausible shadow from the brim over the brow and prime of the glasses. Preserve the batik material and her arms unchanged. Preserve the watercolor type unchanged.

This course of merges the hat picture with the bottom picture to generate a brand new picture, with minimal modifications to the pose and general type. In Python, use the next code:

from PIL import Picture

# This code assumes 'consumer' has been configured from step one
base_image = Picture.open('/path/to/your/picture.png')
hat_image = Picture.open('/path/to/your/hat.png')
fusion_prompt = "Transfer the identical girl and pose open air in open shade and place the straw hat..."

response = consumer.fashions.generate_content(
    mannequin="gemini-2.5-flash-image-preview",
    contents=[fusion_prompt, base_image, hat_image])

For finest outcomes, use a most of three enter photos. Utilizing extra might scale back output high quality.

That covers the fundamentals of utilizing the nano-banana mannequin. In my view, this mannequin excels when you will have present photos that you just need to remodel or edit. It is particularly helpful for sustaining consistency throughout a collection of generated photos.

Attempt it for your self and do not be afraid to iterate, as you typically will not get the right picture on the primary strive.

# Wrapping Up

 
Gemini 2.5 Flash Picture, or nano-banana, is the most recent picture era and enhancing mannequin from Google. It boasts highly effective capabilities in comparison with earlier picture era fashions. On this article, we explored tips on how to use nano-banana to generate and edit photos, highlighting its options for sustaining consistency and making use of stylistic modifications.

I hope this has been useful!
 
 

Cornellius Yudha Wijaya is a knowledge science assistant supervisor and knowledge author. Whereas working full-time at Allianz Indonesia, he likes to share Python and knowledge ideas through social media and writing media. Cornellius writes on a wide range of AI and machine studying subjects.