6 Tips and a Two-Step Practical Method for Improving Nano Banana Image Text Rendering Accuracy

When generating images with Nano Banana, many developers encounter a frustrating problem: the images look great, but the text on them is either misspelled, blurry, or completely garbled.

The good news is that Google's official documentation actually provides a key tip: first have the model generate the text content, and then request an image containing that text. This is known as the "Two-Step Approach," and it can significantly improve text rendering accuracy.

This article will dive deep into the technical reasons behind this phenomenon and provide 6 proven text rendering techniques to help you get clear and accurate text in your Nano Banana generated images.

Key Takeaway: After reading this article, you'll understand how Nano Banana renders text, master the Two-Step Approach and 6 other practical techniques, and take your image text accuracy from "luck of the draw" to a controllable level.

Nano Banana Text Rendering Status: Powerful, but Requires Skill

Let's get straight to the point: The Nano Banana series models' text rendering capabilities are top-tier in the AI image generation space, but it's not a "just write any prompt and get perfect text" situation.

Nano Banana Text Rendering Accuracy Data

As you can see, Nano Banana Pro's 94% accuracy is already the highest in the industry. However, the remaining 6% of failure scenarios—spelling errors, blurry text, missing characters—are simply unacceptable for commercial use.

Why AI Image Generation Struggles with Text Rendering

To understand why the "two-step method" is necessary, let's first grasp the challenges of rendering text in AI-generated images:

1. Pixel-level Precision Required: Text in images must be pixel-perfect; even a single stroke out of place can turn it into a typo. Other AI-generated content (landscapes, people) allows for a certain degree of blurriness.
2. Explosive Character Combinations: With 26 English letters, thousands of Chinese characters, plus variations in case, fonts, and arrangements, the possibilities are almost infinite.
3. Contextual Interference: When generating the overall composition of an image, the model can easily get "distracted"—it needs to render the background well and arrange the text correctly, with both tasks competing for its attention.
4. Training Data Bias: The proportion of images with perfect text in training datasets is limited, meaning the model might not have sufficient learning for certain fonts and layout combinations.

🎯 Pro Tip: Understanding the difficulties of text rendering is key to optimizing your prompt effectively. By using the APIYI apiyi.com platform to invoke Nano Banana Pro and Nano Banana 2, you can quickly compare the text rendering effects of both models and choose the best solution for your scenario.

Core Technique One: The Two-Step Method – Official Best Practice for Text Rendering

This is the method explicitly recommended in Google's official documentation, and it's the most important technique we'll cover in this article.

How the Two-Step Method Works

Traditional One-Step Method (Poor Results):

"Generate a poster with 'SUMMER SALE 50% OFF' written on it."
→ Model processes composition and text simultaneously → Text is prone to errors

Two-Step Method (Good Results):

Step One: "Please generate poster copy for me: Summer Sale 50% Off"
→ Model outputs text: "SUMMER SALE 50% OFF"

Step Two: "Generate a poster image that accurately displays the text 'SUMMER SALE 50% OFF'"
→ Model focuses on rendering the confirmed text into the image → Accuracy significantly improves

Why the Two-Step Method Works – A Technical Explanation

Nano Banana is built upon the Gemini multimodal Large Language Model. When you use the one-step method and directly ask it to "generate an image containing certain text," the model needs to simultaneously complete two tasks:

1. Understand and plan the image composition — scene, colors, layout
2. Precisely render text characters — spelling, font, position

These two tasks compete with each other within the model's attention mechanism. The model's "thinking resources" are limited, and when it tries to handle two high-precision tasks at once, the text rendering often becomes the casualty.

The core idea behind the two-step method is task separation:

• The first step allows the model to focus on generating and confirming the text content — at this point, the model is in a pure text mode, ensuring extremely high spelling accuracy.
• The second step allows the model to focus on rendering the confirmed text into the image — the text content is already fixed, so the model only needs to solve the "how to draw it" problem.

It's like asking a painter to first decide what text should be on a poster (the copywriting phase) before actually painting the poster (the design phase). Doing these two stages separately leads to higher efficiency and accuracy.

Two-Step Method API Code Implementation

import openai

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"  # APIYI unified interface
)

# ========== Step 1: Have the model generate/confirm text content ==========
text_response = client.chat.completions.create(
    model="gemini-3.1-flash-image-preview",
    messages=[{
        "role": "user",
        "content": "I need a promotional poster for a coffee shop. Please generate the English copy to be displayed on the poster, keeping it concise and powerful, no more than 20 characters. Only output the text, no other content."
    }]
)

poster_text = text_response.choices[0].message.content.strip()
print(f"Step 1 - Generated copy: {poster_text}")
# Output example: "BREW YOUR PERFECT DAY"

# ========== Step 2: Use the confirmed text to generate the image ==========
image_response = client.chat.completions.create(
    model="gemini-3.1-flash-image-preview",
    messages=[{
        "role": "user",
        "content": f'Generate an image: A warm-toned coffee shop promotional poster. Display the exact text "{poster_text}" in bold serif font, centered at the top. Background shows a cozy cafe interior with warm lighting.'
    }]
)

print("Step 2 - Image generation complete")

Key Details of the Two-Step Method

Core Skill Two: The 25-Character Golden Rule

This is the most crucial hard constraint for Nano Banana text rendering.

Nano Banana Text Rendering Accuracy vs. Character Count

Strategies for Exceeding 25 Characters

When your text genuinely exceeds 25 characters, you've got 3 ways to handle it:

Strategy One: Split into Multiple Short Lines

# ❌ Rendering long text all at once
prompt = 'Generate a poster with text "ANNUAL SUMMER CLEARANCE SALE - UP TO 70% OFF ALL ITEMS"'

# ✅ Splitting into multiple short lines
prompt = '''Generate a poster with two lines of text:
Line 1 (large, bold): "SUMMER SALE 70% OFF"
Line 2 (smaller, below): "ALL ITEMS INCLUDED"'''

Strategy Two: Gradual Addition Through Multi-Turn Conversations

# Round 1: Generate image with only the main title
# Round 2: Add subtitle based on the previous result
# Round 3: Add bottom explanatory text

Strategy Three: Use Images for Key Text, Post-Synthesis for Long Text

For scenarios that truly require a lot of text (like infographics), we recommend using Nano Banana only for generating key, short titles. Long paragraphs of text can be overlaid later using design tools.

Core Skill Three: Double Quotes + Explicit Font Specification

Combining these two small tricks can boost text rendering accuracy even further.

The Role of Double Quotes

Double quotes tell the model: the content within the quotes needs to be rendered precisely, character by character, rather than being a general description.

# ❌ No quotes, the model might take liberties
prompt = "Generate a sign that says Welcome to Tokyo"
# Might output: "WELCOME TO TOKIO" (misspelled) or completely different text

# ✅ Enclosed in double quotes, forcing character-by-character rendering
prompt = 'Generate a sign that displays the exact text "Welcome to Tokyo"'
# Output: "Welcome to Tokyo" (highly likely to be accurate)

Explicit Font Specification

Explicitly specifying the font type can help the model choose font styles that are easier to render:

💡 Practical Tip: Bold serif fonts are the most accurate font type for text rendering. Their thick strokes and clear structure make them easier for the model to generate accurately. Handwritten and script fonts have the lowest accuracy, so try to avoid them for critical text.

Core Skill Four: Special Handling for Multilingual Text Rendering

Nano Banana performs excellently in multilingual text rendering, but different languages require different handling strategies.

Multilingual Text Rendering Performance

Multilingual Text Rendering Prompt Templates

# English — Most reliable
prompt = 'Generate a poster with bold text "HELLO WORLD" in white serif font'

# Chinese — Specify language + concise
prompt = 'Generate a poster with Chinese text "欢迎光临" in bold Chinese calligraphy style font, centered'

# Japanese — Explicitly state language
prompt = 'Generate a Japanese store sign with text "いらっしゃいませ" in clean sans-serif Japanese font'

# Mixed languages — Handle line by line
prompt = '''Generate a bilingual poster:
Top line in English: "GRAND OPENING"
Bottom line in Chinese: "盛大开业"
Both in bold, high contrast against dark background'''

🎯 Technical Tip: For multilingual text rendering, we recommend extensive testing and comparison using the APIYI apiyi.com platform. The results can vary significantly between languages, so practical testing is more reliable than theoretical parameters. The platform supports quick switching between Nano Banana Pro and Nano Banana 2 models.

Core Skill Five: Structured Prompt Templates (Essential for Practical Use)

Let's combine all the previous tips into a standardized prompt template for various scenarios.

Nano Banana Universal Text Rendering Prompt Template

Generate an image:
[Scene description, max 100 characters].
Display the exact text "[Your text, ≤25 characters]" in [font style] font,
positioned at [position], [size description].
The text should be [color] with high contrast against the background.
Ensure the text is perfectly legible and correctly spelled.

Practical Examples for Different Scenarios

Scenario One: Commercial Poster

prompt = '''Generate an image:
A vibrant summer sale promotional poster with tropical beach background.
Display the exact text "SUMMER SALE" in bold white serif font,
positioned at the center top, large and prominent.
Below it, display "50% OFF" in bold yellow sans-serif font.
The text should have high contrast against the background.
Ensure all text is perfectly legible and correctly spelled.'''

Scenario Two: Logo Design

prompt = '''Generate an image:
A minimalist tech company logo on a clean white background.
Display the exact text "NEXUS" in modern bold sans-serif font,
positioned at the center, medium size.
The text should be dark navy blue (#1a1a2e).
Ensure the text is perfectly legible and correctly spelled.'''

Scenario Three: Social Media Graphic

prompt = '''Generate an image:
An inspirational quote card with soft gradient background (blue to purple).
Display the exact text "START NOW" in elegant white serif font,
positioned at the center, large and prominent.
The text should be pure white with subtle drop shadow.
Ensure the text is perfectly legible and correctly spelled.'''

Core Skill Six: Iterative Refinement with Multi-turn Conversations

Even after using the first 5 techniques, text rendering might still not be perfect. A major advantage of Nano Banana is its support for multi-turn conversational editing—if you're not satisfied, you can directly refine the previous result.

Text Correction Conversation Flow

import openai

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

messages = []

# Round 1: Generate initial image
messages.append({
    "role": "user",
    "content": 'Generate an image: A coffee shop menu board with text "TODAY\'S SPECIAL" in chalk-style white font on dark background'
})

response_1 = client.chat.completions.create(
    model="gemini-3.1-flash-image-preview",
    messages=messages
)
messages.append({"role": "assistant", "content": response_1.choices[0].message.content})

# Round 2: Check and correct text
messages.append({
    "role": "user",
    "content": 'The text is slightly blurry. Please regenerate with the text "TODAY\'S SPECIAL" rendered more sharply and clearly. Make the font bolder and increase the contrast.'
})

response_2 = client.chat.completions.create(
    model="gemini-3.1-flash-image-preview",
    messages=messages
)

Common Correction Prompts

🚀 Quick Start: Multi-turn conversational editing is perfect for scenarios where you have high demands for text effects. By invoking Nano Banana via the APIYI apiyi.com platform, each editing round costs about $0.02, and you can achieve satisfactory results in 3-4 iterations.

Nano Banana Text Rendering Full Workflow

Let's integrate all 6 techniques into a standardized workflow:

Step One: Plan Text Content

• Determine the text to be rendered (≤25 characters)
• If over 25 characters, split into multiple lines
• Confirm accurate spelling

Step Two: Two-Step Generation

• First, let the model confirm/optimize the text content
• Then, use the confirmed text to generate the image

Step Three: Prompt Optimization

• Enclose text in double quotes
• Explicitly specify font style
• Use a structured template
• Add the constraint "Ensure text is perfectly legible"

Step Four: Check and Iterate

• Check if the text in the generated result is accurate
• If not satisfied, correct with multi-turn conversations
• Typically, 1-3 rounds are enough to achieve satisfactory results

#!/usr/bin/env python3
"""
Nano Banana Text Rendering Optimization Workflow
Full implementation of the two-step method + 6 key techniques
"""

import openai
import base64
import re
from datetime import datetime

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.apiyi.com/v1"

client = openai.OpenAI(api_key=API_KEY, base_url=BASE_URL)

def render_text_in_image(
    scene_description: str,
    desired_text: str,
    font_style: str = "bold serif",
    text_color: str = "white",
    text_position: str = "centered",
    model: str = "gemini-3.1-flash-image-preview",
    max_fix_rounds: int = 2
):
    """
    Generates an image with accurate text using the two-step method.

    Args:
        scene_description: Scene description (without text requirements)
        desired_text: The text to be rendered (recommended ≤25 characters)
        font_style: Font style
        text_color: Text color
        text_position: Text position
        model: The model to use
        max_fix_rounds: Maximum number of correction rounds
    """
    # Check text length
    if len(desired_text) > 25:
        print(f"⚠️ Text length {len(desired_text)} exceeds 25 characters, accuracy might decrease")

    # ===== Step One: Confirming text content =====
    print(f"📝 Step One: Confirming text content → '{desired_text}'")

    text_check = client.chat.completions.create(
        model=model,
        messages=[{
            "role": "user",
            "content": f"Please verify this text is correctly spelled and formatted: '{desired_text}'. Only reply with the verified text, nothing else."
        }]
    )
    verified_text = text_check.choices[0].message.content.strip().strip("'\"")
    print(f"✅ Verified text: '{verified_text}'")

    # ===== Step Two: Generating image with text =====
    print(f"🎨 Step Two: Generating image...")

    image_prompt = f'''Generate an image:
{scene_description}.
Display the exact text "{verified_text}" in {font_style} font,
positioned at {text_position}, with {text_color} color.
The text should have high contrast against the background.
Ensure the text is perfectly legible and correctly spelled.'''

    messages = [{"role": "user", "content": image_prompt}]

    response = client.chat.completions.create(
        model=model,
        messages=messages
    )

    content = response.choices[0].message.content
    print(f"✅ Image generation complete")

    # Save image
    save_image(content, f"text_render_{datetime.now().strftime('%H%M%S')}.png")
    return content


def save_image(content, filename):
    """Extracts and saves the image from the response"""
    patterns = [
        r'data:image/[^;]+;base64,([A-Za-z0-9+/=]+)',
        r'([A-Za-z0-9+/=]{1000,})'
    ]
    for pattern in patterns:
        match = re.search(pattern, content)
        if match:
            data = base64.b64decode(match.group(1))
            with open(filename, 'wb') as f:
                f.write(data)
            print(f"💾 Saved to: {filename} ({len(data):,} bytes)")
            return True
    print("⚠️ No image data found")
    return False


# ===== Usage Example =====
if __name__ == "__main__":
    # Example 1: Commercial Poster
    render_text_in_image(
        scene_description="A vibrant promotional poster with tropical beach background, summer vibes",
        desired_text="SUMMER SALE",
        font_style="bold white serif",
        text_position="top center, large and prominent"
    )

    # Example 2: Logo
    render_text_in_image(
        scene_description="A minimalist tech company logo on clean white background",
        desired_text="NEXUS",
        font_style="modern bold sans-serif",
        text_color="dark navy blue",
        text_position="centered"
    )

    # Example 3: Chinese Text
    render_text_in_image(
        scene_description="A traditional Chinese restaurant sign with red and gold decorations",
        desired_text="福满楼",
        font_style="bold Chinese calligraphy",
        text_color="gold",
        text_position="centered, large"
    )

Nano Banana Pro vs. Nano Banana 2: Text Rendering Comparison

Both models have their strengths when it comes to text rendering:

Model Selection Recommendations for Text Rendering

• Commercial Posters/Branding Materials: Choose Nano Banana Pro — 94% accuracy + more font styles
• Social Media Graphics/Rapid Prototypes: Choose Nano Banana 2 — faster speed + better cost-effectiveness
• Scenarios Requiring Frequent Iteration: Choose Nano Banana 2 — faster speed means lower iteration costs
• Multilingual Text: Not much difference between the two; choose based on speed/cost requirements

Frequently Asked Questions

Summary

Nano Banana's text rendering capability is already top-tier in AI image generation (Pro 94%, Banana 2 87%). However, to consistently leverage this ability, you'll need to master the right techniques.

Here are 6 core techniques, ranked by importance:

1. Two-Step Method — Generate text first, then generate the image. This is officially recommended and yields the most significant results.
2. 25-Character Rule — Control text length. For longer texts, break them down and process them separately.
3. Double Quotes + Font Specification — Force character-by-character rendering + choose fonts with high accuracy.
4. Multilingual Special Handling — Use different strategies for different languages.
5. Structured Prompt Templates — Standardize your prompts to improve stability.
6. Multi-Turn Conversation Refinement — If you're not satisfied, iterate and optimize.

Once you've mastered these techniques, Nano Banana's text rendering transforms from a "hit-or-miss" endeavor into a controllable and predictable capability. We recommend using APIYI apiyi.com to quickly start testing and find the parameter combinations that best suit your specific scenarios.

References

1. Google Official – Nano Banana Image Generation Documentation

   • Link: ai.google.dev/gemini-api/docs/image-generation
   • Description: Includes the official recommendation to "generate text first, then generate the image".

2. Google Developers Blog – Prompting Tips for Nano Banana Pro

   • Link: blog.google/products/gemini/prompting-tips-nano-banana-pro/
   • Description: Official prompt optimization tips.

3. Google Developers Blog – How to Prompt Gemini 2.5 Flash Image Generation

   • Link: developers.googleblog.com/how-to-prompt-gemini-2-5-flash-image-generation-for-the-best-results/
   • Description: Image generation optimization strategies for Flash series models.

📝 Author: APIYI Team | For technical discussions and API integration, please visit apiyi.com