Gemini Image Generation (Nano Banana Pro)
Generate and edit images using Google's Gemini API. The environment variable GEMINI_API_KEY must be set.
Default Model
| Model | Resolution | Best For |
|---|---|---|
gemini-3-pro-image-preview | 1K-4K | All image generation (default) |
Note: Always use this Pro model. Only use a different model if explicitly requested.
Quick Reference
Default Settings
- Model:
gemini-3-pro-image-preview - Resolution: 1K (default, options: 1K, 2K, 4K)
- Aspect Ratio: 1:1 (default)
Available Aspect Ratios
1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
Available Resolutions
1K (default), 2K, 4K
Core API Pattern
import os
from google import genai
from google.genai import types
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
# Basic generation (1K, 1:1 - defaults)
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=["Your prompt here"],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
),
)
for part in response.parts:
if part.text:
print(part.text)
elif part.inline_data:
image = part.as_image()
image.save("output.png")Custom Resolution & Aspect Ratio
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", # Wide format
image_size="2K" # Higher resolution
),
)
)Resolution Examples
# 1K (default) - Fast, good for previews
image_config=types.ImageConfig(image_size="1K")
# 2K - Balanced quality/speed
image_config=types.ImageConfig(image_size="2K")
# 4K - Maximum quality, slower
image_config=types.ImageConfig(image_size="4K")Aspect Ratio Examples
# Square (default)
image_config=types.ImageConfig(aspect_ratio="1:1")
# Landscape wide
image_config=types.ImageConfig(aspect_ratio="16:9")
# Ultra-wide panoramic
image_config=types.ImageConfig(aspect_ratio="21:9")
# Portrait
image_config=types.ImageConfig(aspect_ratio="9:16")
# Photo standard
image_config=types.ImageConfig(aspect_ratio="4:3")Editing Images
Pass existing images with text prompts:
from PIL import Image
img = Image.open("input.png")
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=["Add a sunset to this scene", img],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
),
)Multi-Turn Refinement
Use chat for iterative editing:
from google.genai import types
chat = client.chats.create(
model="gemini-3-pro-image-preview",
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:
"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
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"),
],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
),
)Important: File Format & Media Type
CRITICAL: The Gemini API returns images in JPEG format by default. When saving, always use .jpg extension to avoid media type mismatches.
# CORRECT - Use .jpg extension (Gemini returns JPEG)
image.save("output.jpg")
# WRONG - Will cause "Image does not match media type" errors
image.save("output.png") # Creates JPEG with PNG extension!Converting to PNG (if needed)
If you specifically need PNG format:
from PIL import Image
# Generate with Gemini
for part in response.parts:
if part.inline_data:
img = part.as_image()
# Convert to PNG by saving with explicit format
img.save("output.png", format="PNG")Verifying Image Format
Check actual format vs extension with the file command:
file image.png
# If output shows "JPEG image data" - rename to .jpg!Notes
- All generated images include SynthID watermarks
- Gemini returns JPEG format by default - always use
.jpgextension - Image-only mode (
responseModalities: ["IMAGE"]) won't work with Google Search grounding - For editing, describe changes conversationally—the model understands semantic masking
- Default to 1K resolution for speed; use 2K/4K when quality is critical