Nano Banana – Image Generation + Prompt Library

Generate images using Gemini API with access to 6000+ curated professional prompts.

Quick Start

Direct Generation

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

Search Prompts First

python scripts/search_prompts.py "avatar professional"

Edit Existing Image

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

Workflow

Step 0: Mode Detection

Classify user intent:

Step 1: Prompt Search (exploration mode)

Search by category:

• avatars – Headshots, portraits, profile pictures
• social_media – Instagram, Twitter, Facebook content (3800+)
• product_marketing – Ads, campaigns (1900+)
• infographic – Data visualization
• thumbnails – YouTube covers
• comics – Sequential art, storyboards
• ecommerce – Product photography
• game_assets – Sprites, characters
• posters – Events, announcements
• web_design – UI mockups

Token optimization: Use grep patterns, NEVER fully load reference files.

Present max 3 matching prompts with sample images when available.

Step 2: Generation

Step 3: Refinement (optional)

Use multi-turn chat for iterative editing:

python scripts/multi_turn_chat.py

Prompt Categories

Models

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"),
    ],
)

Environment

Requires GEMINI_API_KEY environment variable.

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