Gemini Imagegen

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

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"

Landing Pages

Specify layout structure, color scheme, and target audience:

"Modern landing page hero section, gradient background from deep purple to blue, centered headline with CTA button, clean minimalist design, SaaS product"

"Landing page for fitness app, energetic layout with workout photos, bright orange and black color scheme, mobile-first design, prominent download buttons"

Website Design Ideas

Describe overall aesthetic, navigation style, and content hierarchy:

"E-commerce homepage wireframe, grid layout for products, sticky navigation bar, warm earth tones, plenty of whitespace, professional photography style"

"Portfolio website for photographer, full-screen image galleries, dark mode interface, elegant serif typography, minimal UI elements to highlight work"

"Tech startup homepage, glassmorphism design trend, floating cards, neon accent colors on dark background, modern illustrations, hero section with product demo"

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

Converted and distributed by TomeVault — claim your Tome and manage your conversions.