Picture by Writer | Gemini (nano-banana self portrait)
# Introduction
Picture era with generative AI has grow to be a broadly used instrument 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 expertise and competitors, many trendy, superior picture era merchandise have been launched, equivalent to Steady Diffusion, Midjourney, DALL-E, Imagen, and plenty of extra. Every presents distinctive benefits to its customers. Nonetheless, Google not too long ago made a major 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 enhancing 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 presents far higher management than earlier fashions from Google or its rivals.
This text will discover nano-banana’s means to generate and edit photos. 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 observe this tutorial, you will have to register for a Google account and register 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.
For those who 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 ready up, let’s discover the best way 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 likely 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 method, 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 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, fingers 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 mild rakes throughout the material, revealing fantastic 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 life like and faithfully adheres to the given immediate. For those who favor 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
# Exchange 'YOUR-API-KEY' together with your precise API key
api_key = 'YOUR-API-KEY'
shopper = 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 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 mild rakes throughout the material, revealing fantastic 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.components
if half.inline_data
]
if image_parts:
picture = Picture.open(BytesIO(image_parts[0]))
# picture.save('your_image.png')
show(picture)
For those who present your API key and the specified immediate, the Python code above will generate the picture.
We’ve 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 are going to 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 supplied picture, place a pair of skinny studying glasses gently on the artisan’s nostril whereas she attracts the wax traces. 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 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 'shopper' has been configured from the earlier step
base_image = Picture.open('/path/to/your/photograph.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 take a look at character consistency by producing a brand new scene the place the artisan is wanting 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 wanting up on the digicam with a relaxed smile, seated on the identical wood desk. Medium close-up, 85 mm look with gentle 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 id reference: the identical artisan presenting a completed indigo batik material, arms prolonged towards the digicam. Mushy, even window mild, 50 mm look, impartial background muddle.
The result’s proven beneath.
The ensuing picture reveals a totally totally different scene however maintains the identical character. This highlights the mannequin’s means to realistically produce different content material from a single reference picture.
Subsequent, let’s strive picture fashion 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 fabric, gentle 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 mild 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 lady 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 (digicam left), ribbon tails drifting softly with gravity. Use gentle sky mild 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 fingers 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 adjustments to the pose and general fashion. 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/photograph.png')
hat_image = Picture.open('/path/to/your/hat.png')
fusion_prompt = "Transfer the identical lady and pose outdoor 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 photos. Utilizing extra could cut back output high quality.
That covers the fundamentals of utilizing the nano-banana mannequin. For my part, this mannequin excels when you’ve gotten current photos that you simply wish to rework or edit. It is particularly helpful for sustaining consistency throughout a sequence of generated photos.
Strive it for your self and do not be afraid to iterate, as you typically 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 era and enhancing mannequin from Google. It boasts highly effective capabilities in comparison with earlier picture era fashions. On this article, we explored the best way to use nano-banana to generate and edit photos, 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 suggestions through social media and writing media. Cornellius writes on a wide range of AI and machine studying matters.
