Picture by Creator | Gemini (nano-banana self portrait)
# Introduction
Picture era with generative AI has change into a extensively used device for each people and companies, permitting them to immediately create their meant visuals without having any design experience. Primarily, these instruments can speed up duties that might in any other case take a major period of time, finishing them in mere seconds.
With the development of expertise and competitors, many trendy, superior picture era merchandise have been launched, akin to Steady Diffusion, Midjourney, DALL-E, Imagen, and plenty of extra. Every gives distinctive benefits to its customers. Nevertheless, Google not too long ago made a major influence on the picture era panorama with the discharge of Gemini 2.5 Flash Picture (or nano-banana).
Nano-banana is Google’s superior picture era and modifying mannequin, that includes capabilities like life like picture creation, a number of picture mixing, character consistency, focused prompt-based transformations, and public accessibility. The mannequin gives far higher management than earlier fashions from Google or its rivals.
This text will discover nano-banana’s potential 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 setting.
Let’s get into it.
# Testing the Nano-Banana Mannequin
To observe this tutorial, you have to to register for a Google account and check in to Google AI Studio. Additionally, you will want to accumulate an API key to make use of the Gemini API, which requires a paid plan as there isn’t a free tier out there.
If you happen to favor to make use of the API with Python, make certain to put in the Google Generative AI library with the next command:
As soon as your account is about 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 can 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 perfect outcomes is to describe the scene, not simply checklist key phrases. This narrative strategy, describing the picture you envision, usually produces superior outcomes.
Within the AI Studio chat interface, you may 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, palms stained with wax, tracing a flowing motif on indigo fabric 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 light 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 life like and faithfully adheres to the given immediate. If you happen to favor the Python implementation, you need to 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
# Change 'YOUR-API-KEY' together with your precise API key
api_key = 'YOUR-API-KEY'
shopper = genai.Shopper(api_key=api_key)
immediate = "A photorealistic close-up portrait of an Indonesian batik artisan, palms stained with wax, tracing a flowing motif on indigo fabric 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 light separation and creamy bokeh. The general temper is targeted, tactile, and proud."
response = shopper.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.components
if half.inline_data
]
if image_parts:
picture = Picture.open(BytesIO(image_parts[0]))
# picture.save('your_image.png')
show(picture)
If you happen to 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 modifying, which we’ll 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 supplied picture, place a pair of skinny studying glasses gently on the artisan’s nostril whereas she attracts the wax strains. Guarantee reflections look life like 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 'shopper' has been configured from the earlier step
base_image = Picture.open('/path/to/your/picture.png')
edit_prompt = "Utilizing the supplied picture, place a pair of skinny studying glasses gently on the artisan's nostril..."
response = shopper.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 trying straight on the digicam and smiling:
Generate a brand new and photorealistic picture utilizing the supplied picture as a reference for id: the identical batik artisan now trying up on the digicam with a relaxed smile, seated on the identical wood desk. Medium close-up, 85 mm look with tender 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 supplied picture as id reference: the identical artisan presenting a completed indigo batik fabric, arms prolonged towards the digicam. Delicate, even window gentle, 50 mm look, impartial background muddle.
The result’s proven beneath.
The ensuing picture reveals a very totally different scene however maintains the identical character. This highlights the mannequin’s potential to realistically produce diverse content material from a single reference picture.
Subsequent, let’s strive picture type switch. We are going to use the next immediate to alter the photorealistic picture right into a watercolor portray.
Utilizing the supplied picture as id reference, recreate the scene as a fragile watercolor on cold-press paper: unfastened indigo washes for the material, tender 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 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 lady’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 outside 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 (digicam left), ribbon tails drifting softly with gravity. Use tender sky gentle as key with a mild 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 fabric and her palms 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 'shopper' 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 outside in open shade and place the straw hat..."
response = shopper.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 might cut back output high quality.
That covers the fundamentals of utilizing the nano-banana mannequin. In my view, this mannequin excels when you’ve current photographs that you just need 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 typically will not get the right picture on the primary strive.
# Wrapping Up
Gemini 2.5 Flash Picture, or nano-banana, is the newest picture era and modifying mannequin from Google. It boasts highly effective capabilities in comparison with earlier picture era 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 through social media and writing media. Cornellius writes on quite a lot of AI and machine studying subjects.
