Picture by Creator | Gemini (nano-banana self portrait)
# Introduction
Picture technology with generative AI has change into a extensively used software for each people and companies, permitting them to immediately create their supposed visuals without having any design experience. Basically, 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 know-how and competitors, many fashionable, superior picture technology merchandise have been launched, resembling Steady Diffusion, Midjourney, DALL-E, Imagen, and lots of extra. Every provides distinctive benefits to its customers. Nonetheless, Google just lately made a major 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 enhancing mannequin, that includes capabilities like lifelike picture creation, a number of picture mixing, character consistency, focused prompt-based transformations, and public accessibility. The mannequin provides far larger management than earlier fashions from Google or its rivals.
This text will discover nano-banana’s skill to generate and edit pictures. We’ll exhibit 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 observe this tutorial, you will have to register for a Google account and sign up 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 any free tier accessible.
In the event you desire to make use of the API with Python, be sure to put in the Google Generative AI library with the next command:
As soon as your account is about up, let’s discover 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 might 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 listing key phrases. This narrative strategy, describing the picture you envision, sometimes produces superior outcomes.
Within the AI Studio chat interface, you may see a platform just like the one under the place you possibly can enter your immediate.
We’ll 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 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 high-quality 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 under:
As you possibly can see, the picture generated is lifelike and faithfully adheres to the given immediate. In the event 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' 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 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 high-quality 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 = 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.elements
if half.inline_data
]
if image_parts:
picture = Picture.open(BytesIO(image_parts[0]))
# picture.save('your_image.png')
show(picture)
In the event you present your API key and the specified immediate, the Python code above will generate the picture.
We have now 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’ll 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 traces. Guarantee reflections look lifelike and the glasses sit naturally on her face with out obscuring her eyes.
The ensuing picture is proven under:
The picture above is equivalent 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 total consistency.
To do that with Python, you possibly can 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 smooth veranda gentle, background jars subtly blurred.
The picture result’s proven under.
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 material, arms prolonged towards the digicam. Smooth, even window gentle, 50 mm look, impartial background litter.
The result’s proven under.
The ensuing picture reveals a very completely different scene however maintains the identical character. This highlights the mannequin’s skill to realistically produce different content material from a single reference picture.
Subsequent, let’s strive picture model switch. We’ll use the next immediate to vary 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, smooth bleeding edges on the floral motif, pale umbers for the desk and background. Maintain 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 under.
The picture demonstrates that the model has been reworked 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 lady 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 smooth sky gentle as key with a mild rim from the intense 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. Maintain the batik material and her palms unchanged. Maintain the watercolor model unchanged.
This course of merges the hat picture with the bottom picture to generate a brand new picture, with minimal adjustments to the pose and total model. 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 lady 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 pictures. 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 gotten current pictures that you simply need to remodel or edit. It is particularly helpful for sustaining consistency throughout a sequence of generated pictures.
Attempt 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 most recent picture technology and enhancing mannequin from Google. It boasts highly effective capabilities in comparison with earlier picture technology fashions. On this article, we explored use nano-banana to generate and edit pictures, highlighting its options for sustaining consistency and making use of stylistic adjustments.
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 by way of social media and writing media. Cornellius writes on a wide range of AI and machine studying subjects.
