Nano Banana

Nano Banana - Image Generation API

Generate images from text prompts using Google Nano Banana (Gemini's native image generation).

Base URL

https://api.eng0.ai/api/data

Models

Generate Image

Create an image from a text description.

curl -X POST https://api.eng0.ai/api/data/images/generate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A futuristic cityscape at sunset with flying cars",
    "model": "flash",
    "aspectRatio": "16:9"
  }'

Parameters:

Aspect Ratios:

Response:

{
  "image": {
    "base64": "iVBORw0KGgoAAAANSUhEUgAA...",
    "mimeType": "image/png",
    "dataUrl": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
  },
  "description": "A vibrant futuristic cityscape..."
}

Response Fields:

Common Workflows

Generate a Simple Image

curl -X POST https://api.eng0.ai/api/data/images/generate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A cute robot holding a cup of coffee"
  }'

Generate High-Quality 4K Image

curl -X POST https://api.eng0.ai/api/data/images/generate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Professional product photo of a sleek smartphone on marble surface",
    "model": "pro",
    "imageSize": "4K",
    "aspectRatio": "4:3"
  }'

Generate Social Media Banner

curl -X POST https://api.eng0.ai/api/data/images/generate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Abstract gradient background with geometric shapes in blue and purple",
    "aspectRatio": "16:9"
  }'

Generate Mobile Story Image

curl -X POST https://api.eng0.ai/api/data/images/generate \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Cozy coffee shop interior with warm lighting",
    "aspectRatio": "9:16"
  }'

Using with Python

import requests
import base64
from pathlib import Path

BASE_URL = "https://api.eng0.ai/api/data"

def generate_image(
    prompt: str,
    model: str = "flash",
    aspect_ratio: str = "1:1",
    image_size: str = "1K"
) -> dict:
    """Generate an image from a text prompt."""
    response = requests.post(
        f"{BASE_URL}/images/generate",
        json={
            "prompt": prompt,
            "model": model,
            "aspectRatio": aspect_ratio,
            "imageSize": image_size
        }
    )
    return response.json()

def save_image(result: dict, filepath: str) -> None:
    """Save generated image to a file."""
    if "image" in result:
        image_data = base64.b64decode(result["image"]["base64"])
        Path(filepath).write_bytes(image_data)
        print(f"Saved to {filepath}")
    else:
        print(f"Error: {result.get('error', 'Unknown error')}")

# Example: Generate and save an image
result = generate_image(
    prompt="A serene mountain landscape at dawn with mist in the valley",
    aspect_ratio="16:9"
)
save_image(result, "landscape.png")

# Example: Generate high-quality product image
result = generate_image(
    prompt="Minimalist tech gadget on white background, studio lighting",
    model="pro",
    image_size="4K"
)
save_image(result, "product.png")

Using with Node.js

const fs = require('fs');

const BASE_URL = 'https://api.eng0.ai/api/data';

async function generateImage(prompt, options = {}) {
  const response = await fetch(`${BASE_URL}/images/generate`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      prompt,
      model: options.model || 'flash',
      aspectRatio: options.aspectRatio || '1:1',
      imageSize: options.imageSize || '1K'
    })
  });
  return response.json();
}

function saveImage(result, filepath) {
  if (result.image) {
    const buffer = Buffer.from(result.image.base64, 'base64');
    fs.writeFileSync(filepath, buffer);
    console.log(`Saved to ${filepath}`);
  } else {
    console.error('Error:', result.error || 'Unknown error');
  }
}

// Example usage
(async () => {
  const result = await generateImage(
    'A neon-lit cyberpunk street scene at night',
    { aspectRatio: '21:9' }
  );
  saveImage(result, 'cyberpunk.png');
})();

Prompt Tips

Be Specific

Bad:  "A dog"
Good: "A golden retriever puppy playing in autumn leaves, warm sunlight"

Include Style

"Oil painting style portrait of a woman in Renaissance clothing"
"Minimalist vector illustration of a mountain range"
"Photorealistic close-up of a dewdrop on a leaf"

Specify Lighting

"... with dramatic side lighting"
"... golden hour sunlight"
"... soft diffused studio lighting"

Add Context

"... in the style of Studio Ghibli"
"... professional product photography"
"... vintage 1970s film aesthetic"

Model Comparison

When to use Flash:

• Rapid prototyping
• Social media content
• High-volume generation
• When speed matters

When to use Pro:

• Professional/commercial use
• Print materials
• Complex compositions
• When quality matters

Error Handling

No Image Generated:

{
  "error": "No image generated",
  "message": "SAFETY"
}

This occurs when the prompt triggers content safety filters.

Invalid Parameters:

{
  "error": "Invalid parameters",
  "message": "Missing required parameter: prompt"
}

Important Notes

• All generated images include invisible SynthID watermarking
• Images are generated server-side; base64 responses can be large
• The pro model takes longer but produces higher quality
• 4K resolution is only available with the pro model
• Content safety filters may block certain prompts

Combining with Other Skills

This skill provides image generation. Combine with:

• deep-research - Generate illustrations for research reports
• market-data - Create visualizations of financial data

Example workflow:

1. Research a topic (deep-research)
2. Generate relevant illustrations (this skill)
3. Compile into a report