nano-banana-flash

name	nano-banana-flash
description	Generate images using Google Gemini 2.5 Flash - fast, 1024x1024, up to 3 reference images
user_invocable	true
allowed_tools	["Bash"]

Nano Banana Flash

Generate images using Google's Gemini 2.5 Flash Image model. Fast, optimized for high-volume generation.

⚡ Launch the result

The image is the deliverable — show it the instant it's done instead of leaving the user to hunt for a path. After saving ANY generated image, immediately open it (open <abs_path> on macOS → Preview; xdg-open <abs_path> on Linux) and surface the file to the user, before writing any explanation. Lead with the image; keep commentary short. Applies to every image path.

Trigger Conditions

Activate when:

• User says "generate image", "create image", "make me an image"
• User invokes /nano-banana-flash or /nbf
• User wants quick image generation
• High-volume batch image generation needed

Model Details

Property	Value
Model ID	gemini-2.5-flash-image
Speed	Fast (optimized for latency)
Resolution	1024x1024 default
Max Reference Images	3

Prerequisites

1. API Key: Set GEMINI_API_KEY in .env file
2. Package: pip install google-genai Pillow python-dotenv

Get API key: https://aistudio.google.com/app/apikey

Usage

Basic Generation

from google import genai
from google.genai import types
from dotenv import load_dotenv
import os

load_dotenv()
client = genai.Client(api_key=os.environ.get('GEMINI_API_KEY'))

response = client.models.generate_content(
    model='gemini-2.5-flash-image',
    contents='YOUR PROMPT HERE',
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE'],
    ),
)

# Save the image, then launch it so the user sees it immediately
import subprocess
for part in response.parts:
    if part.inline_data:
        image = part.as_image()
        image.save('output.png')
        subprocess.run(['open', 'output.png'])  # macOS (use 'xdg-open' on Linux)
        print('Image saved + opened: output.png')

With Aspect Ratio

response = client.models.generate_content(
    model='gemini-2.5-flash-image',
    contents='YOUR PROMPT HERE',
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio='16:9',  # Options: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
        ),
    ),
)

SDK note: older google-genai versions don't expose types.ImageConfig. Guard it with if hasattr(types, 'ImageConfig'): and only add image_config when present (otherwise omit it) so the call still runs across SDK versions.

With Reference Images

from PIL import Image

ref_image = Image.open('reference.png')

response = client.models.generate_content(
    model='gemini-2.5-flash-image',
    contents=['Combine these in a surreal art style', ref_image],
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE'],
    ),
)

Aspect Ratios

Ratio	Use Case
1:1	Square (social media, profile pics)
16:9	Landscape (presentations, thumbnails)
9:16	Portrait (stories, mobile)
4:3	Standard photo
3:4	Portrait photo
21:9	Ultrawide/cinematic

When to Use Flash vs Pro

Use Case	Model
Quick iterations	Flash
Batch generation	Flash
Cost-sensitive	Flash
4K resolution needed	Pro
Complex prompts	Pro
Character consistency	Pro

Limitations

• Max 3 reference images
• 1024x1024 max resolution (varies by aspect ratio)
• SynthID watermark on all images
• Some geographic restrictions (EU, MEA)

Documentation

Full docs: https://ai.google.dev/gemini-api/docs/image-generation