How to Generate 4K Images with Nano Banana Pro: Complete Guide 2025

Nano Banana Pro, Google DeepMind's latest image generation model (officially known as Gemini 3 Pro Image), has revolutionized AI-powered image creation with its native 4K (4096×4096) resolution capability. Whether you're a developer building AI-powered applications or a content creator seeking the highest quality outputs, this comprehensive guide will walk you through everything you need to know about generating stunning 4K images with Nano Banana Pro.

By the end of this tutorial, you'll have working Python code, understand how to save 79% on costs through laozhang.ai, and be equipped to troubleshoot any issues you encounter. Let's dive in.

Understanding Nano Banana Pro and 4K Generation

Before we get into the technical implementation, it's essential to understand what makes Nano Banana Pro special and why 4K resolution matters for your projects.

What is Nano Banana Pro?

Nano Banana Pro is the community nickname for Google DeepMind's Gemini 3 Pro Image model, released in late 2025. The playful name emerged from early API testing communities and has stuck as the model's unofficial moniker. Here's what sets it apart:

Unlike previous image generation models that required upscaling for high-resolution outputs, Nano Banana Pro generates true 4K images natively. This means no quality loss from interpolation, sharper details, and better text rendering within images.

For those familiar with other Gemini models, you might want to explore our complete Gemini Flash Image API guide which covers the free tier options and compares them to the Pro version.

4K Resolution Explained

4K resolution (4096×4096 pixels) represents 16.78 million pixels per image—four times the pixel count of 2K (2048×2048) and sixteen times that of 1K (1024×1024). Here's why this matters:

4K is essential for:

• Print materials: Brochures, posters, and marketing collateral
• Product mockups: E-commerce images that need zoom capability
• Professional portfolios: Design and photography showcases
• Digital assets: Stock images and commercial licensing

Access Options Overview

There are several ways to access Nano Banana Pro's 4K capabilities:

As you can see, using laozhang.ai saves you 79% compared to official pricing while providing global access without regional restrictions. This is why we recommend it as the primary access method throughout this guide.

If you're interested in comparing image generation models, check out our Flux image generation API guide for alternative options.

Setting Up Your API Access via laozhang.ai

Now let's get you set up with API access. We'll use laozhang.ai because it offers the best combination of price ($0.05 per 4K image), ease of setup, and reliability.

Creating Your laozhang.ai Account

The registration process takes less than 2 minutes:

Step 1: Navigate to laozhang.ai and click "Register"

Step 2: Enter your email address and create a password

Step 3: Verify your email (check spam folder if needed)

Step 4: Log in to access your dashboard

Upon registration, new accounts receive free credits to test the service. This is enough to generate approximately 10-20 4K images at no cost, giving you ample room to experiment before committing.

Getting Your API Key

Once logged in, follow these steps to obtain your API key:

Step 1: Click on "API Keys" in the left sidebar

Step 2: Click "Create New Key"

Step 3: Give your key a descriptive name (e.g., "nano-banana-4k-project")

Step 4: Copy and securely store your API key

“

Security Note: Never share your API key publicly, commit it to version control, or include it in client-side code. Treat it like a password.

Configuration and Security

laozhang.ai uses OpenAI-compatible endpoints, making integration seamless if you've worked with OpenAI's API before. Here's the configuration you'll need:

python

BASE_URL = "https://api.laozhang.ai/v1"
MODEL_NAME = "gemini-3-pro-image"
API_KEY = "your_api_key_here"  # Store securely, never hardcode

Security features include:

• TLS 1.3 encryption: All API traffic is encrypted
• No data retention: Your prompts and images are not stored
• Rate limiting: Protection against accidental overspending
• 99.5% uptime: Enterprise-grade reliability

For a deeper understanding of API gateway architecture and security, see our developer's guide to LLM API gateways.

Step-by-Step 4K Image Generation

Now let's generate your first 4K image. Follow this complete workflow to go from setup to stunning output.

Prerequisites and Setup

Before running any code, ensure you have:

System Requirements:

• Python 3.8 or higher
• 100MB+ free disk space (for image outputs)
• Stable internet connection

Required Packages:

bash
pip install openai requests pillow

Environment Setup:

Create a .env file in your project root:

bash
LAOZHANG_API_KEY=your_api_key_here

Then load it in Python:

python
import os
from dotenv import load_dotenv

load_dotenv()
api_key = os.getenv("LAOZHANG_API_KEY")

Your First 4K Image

Here's the simplest working code to generate a 4K image:

python
from openai import OpenAI
import base64
from pathlib import Path

# Initialize client with laozhang.ai endpoint
client = OpenAI(
    api_key="your_api_key_here",
    base_url="https://api.laozhang.ai/v1"
)

# Generate 4K image
response = client.images.generate(
    model="gemini-3-pro-image",
    prompt="A majestic mountain landscape at golden hour, ultra detailed, professional photography, 4K quality",
    size="4096x4096",
    quality="hd",
    n=1
)

# Extract and decode image
image_data = response.data[0].b64_json
image_bytes = base64.b64decode(image_data)

# Save to file
output_path = Path("my_first_4k_image.png")
output_path.write_bytes(image_bytes)

print(f"4K image saved to: {output_path}")
print(f"File size: {output_path.stat().st_size / 1024 / 1024:.2f} MB")

Run this code, and within 10 seconds you'll have your first 4K image saved locally.

Verifying Output Quality

After generation, verify your image meets 4K standards:

python
from PIL import Image

# Load and verify
img = Image.open("my_first_4k_image.png")
width, height = img.size

print(f"Resolution: {width} × {height}")
print(f"Total pixels: {width * height:,}")
print(f"Is 4K: {width >= 4096 and height >= 4096}")
print(f"Color mode: {img.mode}")

Expected output:

Resolution: 4096 × 4096
Total pixels: 16,777,216
Is 4K: True
Color mode: RGB

If your image isn't 4096×4096, check that you specified size="4096x4096" in your API call.

Python Code for 4K Generation

Let's build a production-ready Python client that handles real-world scenarios.

Basic Generation Code

Here's a clean, well-documented basic implementation:

python
"""
Nano Banana Pro 4K Image Generator
Basic implementation for single image generation
"""

from openai import OpenAI
import base64
from pathlib import Path
from typing import Optional
import time

class NanoBananaProClient:
    """Client for generating 4K images with Nano Banana Pro via laozhang.ai"""

    def __init__(self, api_key: str):
        self.client = OpenAI(
            api_key=api_key,
            base_url="https://api.laozhang.ai/v1"
        )
        self.model = "gemini-3-pro-image"
        self.default_size = "4096x4096"

    def generate_4k(
        self,
        prompt: str,
        output_path: Optional[str] = None,
        quality: str = "hd"
    ) -> bytes:
        """
        Generate a single 4K image.

        Args:
            prompt: Image description
            output_path: Optional path to save image
            quality: Image quality ("standard" or "hd")

        Returns:
            Raw image bytes
        """
        start_time = time.time()

        response = self.client.images.generate(
            model=self.model,
            prompt=prompt,
            size=self.default_size,
            quality=quality,
            n=1
        )

        image_bytes = base64.b64decode(response.data[0].b64_json)
        generation_time = time.time() - start_time

        print(f"Generated in {generation_time:.2f} seconds")

        if output_path:
            Path(output_path).write_bytes(image_bytes)
            print(f"Saved to: {output_path}")

        return image_bytes


# Usage example
if __name__ == "__main__":
    import os

    client = NanoBananaProClient(api_key=os.getenv("LAOZHANG_API_KEY"))

    image = client.generate_4k(
        prompt="A serene Japanese garden with cherry blossoms, koi pond, traditional architecture, 4K ultra detailed",
        output_path="japanese_garden_4k.png"
    )

Production-Ready Client Class

For production use, you need error handling, retries, and cost tracking:

python
"""
Production-Ready Nano Banana Pro Client
Includes error handling, retry logic, and cost tracking
"""

from openai import OpenAI, APIError, RateLimitError, APIConnectionError
import base64
from pathlib import Path
from typing import Optional, List, Dict
import time
import logging
from dataclasses import dataclass

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class GenerationResult:
    """Result of image generation"""
    image_bytes: bytes
    generation_time: float
    cost: float
    prompt: str
    resolution: str

class NanoBananaProClient:
    """Production-ready client for 4K image generation"""

    COST_PER_4K_IMAGE = 0.05  # laozhang.ai pricing
    MAX_RETRIES = 3
    RETRY_DELAYS = [1, 2, 4]  # Exponential backoff

    def __init__(self, api_key: str):
        self.client = OpenAI(
            api_key=api_key,
            base_url="https://api.laozhang.ai/v1"
        )
        self.model = "gemini-3-pro-image"
        self.total_cost = 0.0
        self.total_images = 0

    def generate_4k(
        self,
        prompt: str,
        output_path: Optional[str] = None,
        quality: str = "hd"
    ) -> GenerationResult:
        """Generate 4K image with retry logic"""

        last_error = None

        for attempt in range(self.MAX_RETRIES):
            try:
                start_time = time.time()

                response = self.client.images.generate(
                    model=self.model,
                    prompt=prompt,
                    size="4096x4096",
                    quality=quality,
                    n=1
                )

                image_bytes = base64.b64decode(response.data[0].b64_json)
                generation_time = time.time() - start_time

                # Track costs
                self.total_cost += self.COST_PER_4K_IMAGE
                self.total_images += 1

                # Save if path provided
                if output_path:
                    Path(output_path).write_bytes(image_bytes)
                    logger.info(f"Saved to: {output_path}")

                logger.info(f"Generated in {generation_time:.2f}s | Cost: ${self.COST_PER_4K_IMAGE}")

                return GenerationResult(
                    image_bytes=image_bytes,
                    generation_time=generation_time,
                    cost=self.COST_PER_4K_IMAGE,
                    prompt=prompt,
                    resolution="4096x4096"
                )

            except RateLimitError as e:
                last_error = e
                if attempt < self.MAX_RETRIES - 1:
                    delay = self.RETRY_DELAYS[attempt]
                    logger.warning(f"Rate limited. Retrying in {delay}s...")
                    time.sleep(delay)

            except APIConnectionError as e:
                last_error = e
                if attempt < self.MAX_RETRIES - 1:
                    delay = self.RETRY_DELAYS[attempt]
                    logger.warning(f"Connection error. Retrying in {delay}s...")
                    time.sleep(delay)

            except APIError as e:
                logger.error(f"API error: {e}")
                raise

        raise last_error

    def get_stats(self) -> Dict:
        """Get usage statistics"""
        return {
            "total_images": self.total_images,
            "total_cost": f"${self.total_cost:.2f}",
            "average_cost_per_image": f"${self.COST_PER_4K_IMAGE}"
        }

Batch Processing Implementation

For generating multiple images efficiently:

python
import asyncio
from concurrent.futures import ThreadPoolExecutor
from typing import List

class BatchGenerator:
    """Batch processing for multiple 4K images"""

    def __init__(self, client: NanoBananaProClient, max_workers: int = 3):
        self.client = client
        self.max_workers = max_workers

    def generate_batch(
        self,
        prompts: List[str],
        output_dir: str = "output"
    ) -> List[GenerationResult]:
        """Generate multiple images in parallel"""

        Path(output_dir).mkdir(exist_ok=True)
        results = []

        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            futures = []

            for i, prompt in enumerate(prompts):
                output_path = f"{output_dir}/image_{i:03d}.png"
                future = executor.submit(
                    self.client.generate_4k,
                    prompt=prompt,
                    output_path=output_path
                )
                futures.append(future)

            for future in futures:
                try:
                    result = future.result()
                    results.append(result)
                except Exception as e:
                    logger.error(f"Batch generation error: {e}")

        return results


# Usage example
if __name__ == "__main__":
    import os

    client = NanoBananaProClient(api_key=os.getenv("LAOZHANG_API_KEY"))
    batch = BatchGenerator(client, max_workers=3)

    prompts = [
        "A futuristic city skyline at night, neon lights, 4K ultra detailed",
        "A peaceful forest with morning mist, sunbeams through trees, 4K",
        "An underwater coral reef with tropical fish, crystal clear water, 4K"
    ]

    results = batch.generate_batch(prompts, output_dir="batch_output")

    print(f"\nBatch complete!")
    print(f"Generated: {len(results)} images")
    print(f"Stats: {client.get_stats()}")

Optimizing Prompts for 4K Quality

Writing effective prompts is crucial for getting the best results from Nano Banana Pro's 4K capabilities. Let's explore techniques that maximize image quality.

For more comprehensive prompt engineering strategies, see our ChatGPT image prompts master guide which covers advanced techniques applicable to all image generation models.

4K Prompt Structure

A well-structured 4K prompt follows this pattern:

[Subject] + [Setting/Context] + [Style] + [Quality Modifiers] + [Technical Specs]

Example breakdown:

Complete prompt:

A majestic lion on African savanna at sunset, wildlife photography,
National Geographic style, ultra detailed, sharp focus, dramatic lighting,
4K resolution, professional quality

Detail and Quality Keywords

These keywords consistently improve 4K output quality:

Detail Enhancement:

• "ultra detailed", "intricate details", "fine textures"
• "photorealistic", "hyperrealistic"
• "sharp focus", "crystal clear"

Lighting Keywords:

• "golden hour lighting", "dramatic shadows"
• "soft diffused light", "rim lighting"
• "studio lighting", "natural lighting"

Quality Indicators:

• "4K quality", "high resolution"
• "professional photography", "award-winning"
• "magazine quality", "editorial style"

Style Specificity:

• "cinematic", "film grain"
• "DSLR photo", "mirrorless camera"
• "85mm lens", "shallow depth of field"

Example Prompts That Work

Here are proven prompts optimized for 4K generation:

Portrait Photography:

Professional headshot of a confident business executive,
studio lighting, neutral gray background, sharp eye focus,
Canon EOS R5, 85mm f/1.4 lens, 4K ultra detailed

Landscape Photography:

Breathtaking mountain vista at golden hour,
snow-capped peaks reflected in alpine lake,
dramatic clouds, professional landscape photography,
wide angle lens, ultra detailed 4K resolution

Product Photography:

Luxury watch floating on reflective black surface,
studio product photography, perfect lighting,
every detail visible, commercial quality,
4K resolution, professional product shot

Architectural Visualization:

Modern minimalist interior design, living room with
floor-to-ceiling windows, natural light flooding in,
high-end furniture, architectural photography,
4K ultra detailed, magazine cover quality

Abstract Art:

Abstract fluid art with vibrant colors,
flowing organic shapes, iridescent highlights,
contemporary art style, museum quality,
4K resolution, ultra detailed textures

Troubleshooting Common Issues

Even with the best setup, you'll occasionally encounter issues. This section covers the most common problems and their solutions.

Authentication Errors (401)

Symptom: API returns 401 Unauthorized error

Common Causes and Solutions:

Debugging Code:

python
import os

# Verify API key is loaded
api_key = os.getenv("LAOZHANG_API_KEY")
print(f"API Key loaded: {api_key is not None}")
print(f"Key length: {len(api_key) if api_key else 0}")
print(f"Key prefix: {api_key[:10]}..." if api_key else "No key")

# Test with simple request
try:
    from openai import OpenAI
    client = OpenAI(api_key=api_key, base_url="https://api.laozhang.ai/v1")
    # Make minimal test request
    response = client.models.list()
    print("Authentication successful!")
except Exception as e:
    print(f"Authentication failed: {e}")

Rate Limits and Timeouts (429, 504)

429 Rate Limited:

laozhang.ai has generous rate limits, but you may hit them with batch processing:

python
import time
from functools import wraps

def rate_limit_handler(max_retries=3, base_delay=2):
    """Decorator for handling rate limits"""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except RateLimitError:
                    if attempt == max_retries - 1:
                        raise
                    delay = base_delay * (2 ** attempt)  # Exponential backoff
                    print(f"Rate limited. Waiting {delay}s...")
                    time.sleep(delay)
        return wrapper
    return decorator

@rate_limit_handler(max_retries=3)
def generate_with_retry(client, prompt):
    return client.generate_4k(prompt)

504 Timeout:

4K generation can sometimes take longer than expected:

python
from openai import OpenAI

# Increase timeout for 4K generation
client = OpenAI(
    api_key=api_key,
    base_url="https://api.laozhang.ai/v1",
    timeout=60.0  # 60 second timeout
)

Content and Generation Errors

Content Policy Violations:

Nano Banana Pro has content filters. If you receive a content policy error:

• Avoid explicit or violent content
• Don't request copyrighted characters by name
• Avoid generating realistic public figures
• Remove any potentially harmful requests

Generation Failures:

If generation consistently fails for specific prompts:

python
def safe_generate(client, prompt, fallback_prompt=None):
    """Generate with fallback option"""
    try:
        return client.generate_4k(prompt)
    except APIError as e:
        if "content_policy" in str(e).lower():
            print("Content policy issue. Trying modified prompt...")
            if fallback_prompt:
                return client.generate_4k(fallback_prompt)
        raise

# Usage
result = safe_generate(
    client,
    prompt="A detailed fantasy warrior",
    fallback_prompt="A detailed medieval knight in armor, epic style"
)

Cost Optimization Strategies

Managing costs effectively is crucial for production use. Here's how to maximize value while generating 4K images.

Pricing Comparison Table

For detailed pricing across all Gemini models, see our comprehensive Gemini API pricing guide.

When to Use 4K vs 2K

Not every image needs 4K. Use this decision matrix:

Cost Impact:

python
# Resolution-based pricing via laozhang.ai
PRICING = {
    "1024x1024": 0.02,  # 1K
    "2048x2048": 0.03,  # 2K
    "4096x4096": 0.05,  # 4K
}

def calculate_monthly_cost(images_per_day: int, resolution: str) -> float:
    daily_cost = images_per_day * PRICING[resolution]
    monthly_cost = daily_cost * 30
    return monthly_cost

# Example: 50 images per day
print(f"1K monthly: ${calculate_monthly_cost(50, '1024x1024'):.2f}")
print(f"2K monthly: ${calculate_monthly_cost(50, '2048x2048'):.2f}")
print(f"4K monthly: ${calculate_monthly_cost(50, '4096x4096'):.2f}")

# Output:
# 1K monthly: \$30.00
# 2K monthly: \$45.00
# 4K monthly: \$75.00

Volume Optimization Tips

1. Batch Processing with Off-Peak Hours

Generate in batches during off-peak hours for best performance:

python
import schedule
from datetime import datetime

def batch_generation_job():
    """Run batch generation during off-peak hours"""
    client = NanoBananaProClient(api_key=os.getenv("LAOZHANG_API_KEY"))
    batch = BatchGenerator(client)

    # Load pending prompts from queue
    prompts = load_pending_prompts()

    if prompts:
        results = batch.generate_batch(prompts)
        print(f"Generated {len(results)} images")

# Schedule for 2 AM (typically off-peak)
schedule.every().day.at("02:00").do(batch_generation_job)

2. Prompt Caching

Avoid regenerating similar images:

python
import hashlib
import json
from pathlib import Path

class PromptCache:
    """Cache generated images by prompt hash"""

    def __init__(self, cache_dir: str = ".image_cache"):
        self.cache_dir = Path(cache_dir)
        self.cache_dir.mkdir(exist_ok=True)
        self.index_file = self.cache_dir / "index.json"
        self.index = self._load_index()

    def _load_index(self) -> dict:
        if self.index_file.exists():
            return json.loads(self.index_file.read_text())
        return {}

    def _save_index(self):
        self.index_file.write_text(json.dumps(self.index, indent=2))

    def _hash_prompt(self, prompt: str) -> str:
        return hashlib.sha256(prompt.encode()).hexdigest()[:16]

    def get(self, prompt: str) -> bytes | None:
        """Get cached image if exists"""
        prompt_hash = self._hash_prompt(prompt)
        if prompt_hash in self.index:
            cache_path = self.cache_dir / self.index[prompt_hash]
            if cache_path.exists():
                return cache_path.read_bytes()
        return None

    def set(self, prompt: str, image_bytes: bytes):
        """Cache generated image"""
        prompt_hash = self._hash_prompt(prompt)
        filename = f"{prompt_hash}.png"
        cache_path = self.cache_dir / filename
        cache_path.write_bytes(image_bytes)
        self.index[prompt_hash] = filename
        self._save_index()

3. Resolution Downgrade for Drafts

Generate drafts at lower resolution, then upgrade for finals:

python
def generate_with_draft(client, prompt, final_4k: bool = False):
    """Generate draft first, then optionally upgrade to 4K"""

    # Generate 2K draft first (cheaper)
    draft = client.client.images.generate(
        model=client.model,
        prompt=prompt,
        size="2048x2048",  # 2K draft
        quality="standard",
        n=1
    )

    print("Draft generated. Preview and confirm before 4K generation.")

    if final_4k:
        # Generate final 4K version
        return client.generate_4k(prompt)

    return base64.b64decode(draft.data[0].b64_json)

Summary and FAQ

You've now learned everything needed to generate professional 4K images with Nano Banana Pro. Let's recap the key points and answer common questions.

Key Takeaways

1. Nano Banana Pro (Gemini 3 Pro Image) offers native 4K generation at 4096×4096 resolution

2. laozhang.ai provides 79% cost savings ($0.05 vs $0.24 per image) with easy setup

3. Production-ready code should include:

   • Error handling with retry logic
   • Exponential backoff for rate limits
   • Cost tracking for budget management

4. Prompt optimization matters:

   • Use structured prompts (subject + context + style + quality)
   • Include quality keywords ("ultra detailed", "4K", "professional")
   • Be specific about lighting and style

5. Cost optimization strategies:

   • Use 2K for drafts and non-critical images
   • Implement prompt caching
   • Batch process during off-peak hours

Frequently Asked Questions

Q: Is laozhang.ai safe to use? A: Yes. laozhang.ai uses TLS 1.3 encryption, doesn't store your prompts or images, and maintains 99.5% uptime. Many developers and businesses use it as their primary Nano Banana Pro access point.

Q: Can I use the same code with Google's official API? A: Yes, simply change the base_url to Google's endpoint and use your Google API key. The OpenAI-compatible interface works with both.

Q: What's the maximum number of images I can generate per minute? A: laozhang.ai allows up to 60 requests per minute for most accounts. For higher limits, contact their support team.

Q: Why does 4K generation cost more than 2K? A: 4K images contain 4x the pixels of 2K images, requiring more compute resources. The $0.05 price via laozhang.ai is already significantly discounted.

Q: Can I generate images with text in them? A: Yes, Nano Banana Pro has improved text rendering compared to earlier models. Specify the text clearly in your prompt, e.g., "A coffee mug with the text 'Morning Coffee' written on it."

Q: How long does 4K generation typically take? A: Usually 5-10 seconds via laozhang.ai. Complex prompts or high server load may occasionally increase this to 15-20 seconds.

Q: Is there a free tier for testing? A: The official free tier limits you to 1024×1024 resolution. However, new laozhang.ai accounts receive free credits sufficient for approximately 10-20 4K images.

Q: What file formats are supported? A: Nano Banana Pro returns images as base64-encoded PNG. You can convert to JPEG or other formats using Python's Pillow library if needed.

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Ready to start generating 4K images? Register at laozhang.ai to get your API key and begin creating stunning visuals with Nano Banana Pro today. With 79% cost savings and the production-ready code provided in this guide, you're equipped to integrate professional-quality 4K image generation into any project.