Picture by Creator | Gemini (nano-banana self portrait)
# Introduction
Picture era with generative AI has turn into a extensively used software for each people and companies, permitting them to immediately create their supposed visuals while not having any design experience. Basically, these instruments can speed up duties that might in any other case take a big period of time, finishing them in mere seconds.
With the development of expertise and competitors, many fashionable, superior picture era merchandise have been launched, similar to Steady Diffusion, Midjourney, DALL-E, Imagen, and lots 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 Picture (or nano-banana).
Nano-banana is Google’s superior picture era and modifying mannequin, that includes capabilities like reasonable 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 opponents.
This text will discover nano-banana’s capability to generate and edit pictures. We are going to display 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 is no such thing as a free tier accessible.
When you choose 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 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 one of the best 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 will see a platform just like the one beneath the place you possibly can 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, fingers stained with wax, tracing a flowing motif on indigo fabric with a canting pen. She works at a picket desk in a breezy veranda; folded textiles and dye vats blur behind her. Late-morning window mild rakes throughout the material, revealing high quality 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 concentrated, tactile, and proud.
The generated picture is proven beneath:
As you possibly can see, the picture generated is reasonable and faithfully adheres to the given immediate. When you choose the Python implementation, you should use 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.Consumer(api_key=api_key)
immediate = "A photorealistic close-up portrait of an Indonesian batik artisan, fingers stained with wax, tracing a flowing motif on indigo fabric with a canting pen. She works at a picket desk in a breezy veranda; folded textiles and dye vats blur behind her. Late-morning window mild rakes throughout the material, revealing high quality 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 concentrated, 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.components
if half.inline_data
]
if image_parts:
picture = Picture.open(BytesIO(image_parts[0]))
# picture.save('your_image.png')
show(picture)
When you present your API key and the specified immediate, the Python code above will generate the picture.
We’ve got 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 attempt 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 reasonable 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 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 'consumer' 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 = 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 trying instantly on the digital camera and smiling:
Generate a brand new and photorealistic picture utilizing the supplied picture as a reference for identification: the identical batik artisan now trying up on the digital camera with a relaxed smile, seated on the similar picket desk. Medium close-up, 85 mm look with comfortable veranda mild, 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 identification reference: the identical artisan presenting a completed indigo batik fabric, arms prolonged towards the digital camera. Tender, even window mild, 50 mm look, impartial background litter.
The result’s proven beneath.
The ensuing picture reveals a very completely different scene however maintains the identical character. This highlights the mannequin’s capability to realistically produce diversified content material from a single reference picture.
Subsequent, let’s attempt picture type switch. We are going to use the next immediate to vary the photorealistic picture right into a watercolor portray.
Utilizing the supplied picture as identification reference, recreate the scene as a fragile watercolor on cold-press paper: free indigo washes for the fabric, comfortable bleeding edges on the floral motif, pale umbers for the desk and background. Maintain her pose holding the material, mild smile, and spherical glasses; let the veranda recede into mild granulation and visual paper texture.
The result’s proven beneath.
The picture demonstrates that the type has been reworked into watercolor whereas preserving the topic and composition of the unique.
Lastly, we are going to attempt 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 pinnacle realistically; bow over her proper ear (digital camera left), ribbon tails drifting softly with gravity. Use comfortable sky mild as key with a delicate rim from the intense background. Preserve 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. Maintain the batik fabric and her fingers unchanged. Maintain 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 total 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 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 pictures. Utilizing extra could scale back output high quality.
That covers the fundamentals of utilizing the nano-banana mannequin. For my part, this mannequin excels when you’ve gotten present pictures that you simply need to rework or edit. It is particularly helpful for sustaining consistency throughout a collection of generated pictures.
Attempt it for your self and do not be afraid to iterate, as you usually will not get the right picture on the primary attempt.
# 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 use nano-banana to generate and edit pictures, 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 suggestions through social media and writing media. Cornellius writes on quite a lot of AI and machine studying subjects.
