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

# Introduction

 
Picture technology with generative AI has change into a broadly used instrument for each people and companies, permitting them to immediately create their meant visuals without having 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 know-how and competitors, many fashionable, superior picture technology merchandise have been launched, corresponding to Steady Diffusion, Midjourney, DALL-E, Imagen, and lots of extra. Every gives distinctive benefits to its customers. Nonetheless, Google just lately made a big influence on the picture technology panorama with the discharge of Gemini 2.5 Flash Picture (or nano-banana).

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

This text will discover nano-banana’s skill to generate and edit photographs. We are going to show these options utilizing the Google AI Studio platform and the Gemini API inside a Python atmosphere.

Let’s get into it.

# Testing the Nano-Banana Mannequin

 
To comply with this tutorial, you have to 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, make sure that to put in the Google Generative AI library with the next command:

As soon as your account is ready up, let’s discover easy methods 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 will probably be utilizing.

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

Within the AI Studio chat interface, you will see a platform just like the one beneath the place you may 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 tremendous wax traces and the grain of the teak. Captured on an 85 mm at f/2 for light separation and creamy bokeh. The general temper is targeted, tactile, and proud.

The generated picture is proven beneath:

As you may see, the picture generated is real looking and faithfully adheres to the given immediate. In case you desire the Python implementation, you should utilize the next code to create the picture:

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

# Exchange 'YOUR-API-KEY' along 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 tremendous wax traces and the grain of the teak. Captured on an 85 mm at f/2 for light 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 lengthen additional. As talked about beforehand, nano-banana is especially highly effective for picture modifying, which we are going to discover subsequent.

Let’s strive prompt-based picture modifying 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 traces. Guarantee reflections look real looking 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 primarily based on a descriptive immediate whereas sustaining general consistency.

To do that with Python, you may 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/photograph.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 take a look at character consistency by producing a brand new scene the place the artisan is wanting immediately on the digital camera and smiling:

Generate a brand new and photorealistic picture utilizing the offered picture as a reference for identification: 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 delicate 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 identification 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 litter.

The result’s proven beneath.

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

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

Utilizing the offered picture as identification reference, recreate the scene as a fragile watercolor on cold-press paper: unfastened indigo washes for the fabric, delicate bleeding edges on the floral motif, pale umbers for the desk and background. Preserve her pose holding the material, light 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 fashion has been reworked into watercolor whereas preserving the topic and composition of the unique.

Lastly, we are going to 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 lady’s hat utilizing nano-banana:

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

Transfer the identical girl and pose outdoor in open shade and place the straw hat from the product picture on her head. Align the crown and brim to the top realistically; bow over her proper ear (digital camera left), ribbon tails drifting softly with gravity. Use delicate 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 high of the glasses. Preserve the batik material and her arms unchanged. Preserve the watercolor fashion unchanged.

This course of merges the hat photograph with the bottom picture to generate a brand new picture, with minimal modifications to the pose and general fashion. 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/photograph.png')
hat_image = Picture.open('/path/to/your/hat.png')
fusion_prompt = "Transfer the identical girl and pose outdoor 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 greatest outcomes, use a most of three enter photographs. Utilizing extra could cut 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 photographs that you just wish to remodel or edit. It is particularly helpful for sustaining consistency throughout a collection of generated photographs.

Strive it for your self and do not be afraid to iterate, as you usually will not get the proper picture on the primary strive.

# Wrapping Up

 
Gemini 2.5 Flash Picture, or nano-banana, is the newest picture technology and modifying mannequin from Google. It boasts highly effective capabilities in comparison with earlier picture technology fashions. On this article, we explored easy methods to use nano-banana to generate and edit photographs, 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 information author. Whereas working full-time at Allianz Indonesia, he likes to share Python and information ideas by way of social media and writing media. Cornellius writes on a wide range of AI and machine studying matters.