| name | nano-banana-pro |
| description | Generate images using Google Gemini 3 Pro - up to 4K resolution, 14 reference images, advanced reasoning |
| user_invocable | true |
| allowed_tools | ["Bash"] |
Nano Banana Pro
Generate images using Google's Gemini 3 Pro Image model. Professional quality, up to 4K resolution, advanced reasoning.
⚡ 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" AND wants high quality
- User invokes
/nano-banana-pro or /nbp
- User needs higher resolution
- User needs character consistency across multiple images
- Complex prompts requiring advanced reasoning
Model Details
| Property | Value |
|---|
| Model ID | gemini-3-pro-image-preview |
| Speed | Slower (more processing) |
| Resolution | Up to 4K |
| Max Reference Images | 14 (6 objects, 5 humans max) |
Prerequisites
- API Key: Set
GEMINI_API_KEY in .env file
- 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-3-pro-image-preview',
contents='YOUR PROMPT HERE',
config=types.GenerateContentConfig(
response_modalities=['IMAGE'],
),
)
import subprocess
for part in response.parts:
if part.inline_data:
image = part.as_image()
image.save('output.png')
subprocess.run(['open', 'output.png'])
print('Image saved + opened: output.png')
With Aspect Ratio and Image Size
response = client.models.generate_content(
model='gemini-3-pro-image-preview',
contents='Professional product photography of a luxury watch on marble',
config=types.GenerateContentConfig(
response_modalities=['IMAGE'],
image_config=types.ImageConfig(
aspect_ratio='4:3',
image_size='2K',
),
),
)
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-3-pro-image-preview',
contents=['Create a variation of this image in watercolor style', ref_image],
config=types.GenerateContentConfig(
response_modalities=['IMAGE'],
),
)
Image Size Options (Gemini 3 Pro Only)
| image_size | Use Case |
|---|
1K | Standard web use (default) |
2K | High-quality displays |
4K | Print, professional assets |
Note: Use uppercase K (e.g., '2K'). Lowercase will be rejected.
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 |
Advanced Capabilities
- Character consistency: Same characters across multiple generations
- World knowledge: Semantic understanding for realistic scenes
- Complex prompts: Better understanding of nuanced instructions
- Professional quality: Suitable for marketing, product shots
When to Use Pro vs Flash
| Use Case | Model |
|---|
| 4K resolution | Pro |
| Character consistency | Pro |
| Complex scenes | Pro |
| Professional assets | Pro |
| Many reference images | Pro |
| Quick iterations | Flash |
| Cost-sensitive | Flash |
| Batch generation | Flash |
Limitations
- Preview status (may change)
- Higher cost ($0.134 vs $0.039)
- Slower generation
- SynthID watermark on all images
Documentation
Full docs: External_Documentation/GOOGLE_GEMINI_IMAGE_GENERATION.md
