Gemini Image Generation (Nano Banana)

Generate and edit images using Google's Gemini API. The environment variable GEMINI_API_KEY must be set.

Available Models

Model Alias Resolution Best For

gemini-2.5-flash-image

Nano Banana 1024px Speed, high-volume tasks

gemini-3-pro-image-preview

Nano Banana Pro Up to 4K Professional assets, complex instructions, text rendering

Quick Start Scripts

Text-to-Image

python scripts/generate_image.py "A cat wearing a wizard hat" output.png

Edit Existing Image

python scripts/edit_image.py input.png "Add a rainbow in the background" output.png

Multi-Turn Chat (Iterative Refinement)

python scripts/multi_turn_chat.py

Core API Pattern

All image generation uses the generateContent endpoint with responseModalities: ["TEXT", "IMAGE"] :

import os import base64 from google import genai

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

response = client.models.generate_content( model="gemini-2.5-flash-image", contents=["Your prompt here"], )

for part in response.parts: if part.text: print(part.text) elif part.inline_data: image = part.as_image() image.save("output.png")

Image Configuration Options

Control output with image_config :

from google.genai import types

response = client.models.generate_content( model="gemini-3-pro-image-preview", contents=[prompt], config=types.GenerateContentConfig( response_modalities=['TEXT', 'IMAGE'], image_config=types.ImageConfig( aspect_ratio="16:9", # 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 image_size="2K" # 1K, 2K, 4K (Pro only for 4K) ), ) )

Editing Images

Pass existing images with text prompts:

from PIL import Image

img = Image.open("input.png") response = client.models.generate_content( model="gemini-2.5-flash-image", contents=["Add a sunset to this scene", img], )

Multi-Turn Refinement

Use chat for iterative editing:

from google.genai import types

chat = client.chats.create( model="gemini-2.5-flash-image", config=types.GenerateContentConfig(response_modalities=['TEXT', 'IMAGE']) )

response = chat.send_message("Create a logo for 'Acme Corp'")

Save first image...

response = chat.send_message("Make the text bolder and add a blue gradient")

Save refined image...

Prompting Best Practices

Photorealistic Scenes

Include camera details: lens type, lighting, angle, mood.

"A photorealistic close-up portrait, 85mm lens, soft golden hour light, shallow depth of field"

Stylized Art

Specify style explicitly:

"A kawaii-style sticker of a happy red panda, bold outlines, cel-shading, white background"

Text in Images

Be explicit about font style and placement. Use gemini-3-pro-image-preview for best results:

"Create a logo with text 'Daily Grind' in clean sans-serif, black and white, coffee bean motif"

Product Mockups

Describe lighting setup and surface:

"Studio-lit product photo on polished concrete, three-point softbox setup, 45-degree angle"

Advanced Features (Pro Model Only)

Google Search Grounding

Generate images based on real-time data:

response = client.models.generate_content( model="gemini-3-pro-image-preview", contents=["Visualize today's weather in Tokyo as an infographic"], config=types.GenerateContentConfig( response_modalities=['TEXT', 'IMAGE'], tools=[{"google_search": {}}] ) )

Multiple Reference Images (Up to 14)

Combine elements from multiple sources:

response = client.models.generate_content( model="gemini-3-pro-image-preview", contents=[ "Create a group photo of these people in an office", Image.open("person1.png"), Image.open("person2.png"), Image.open("person3.png"), ], )

REST API (curl)

curl -s -X POST
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent"
-H "x-goog-api-key: $GEMINI_API_KEY"
-H "Content-Type: application/json"
-d '{ "contents": [{"parts": [{"text": "A serene mountain landscape"}]}] }' | jq -r '.candidates[0].content.parts[] | select(.inlineData) | .inlineData.data' | base64 --decode > output.png

Notes

• All generated images include SynthID watermarks

• Image-only mode (responseModalities: ["IMAGE"] ) won't work with Google Search grounding

• For editing, describe changes conversationally—the model understands semantic masking