Interpreting Nano Banana Pro January 2026 Policy Adjustments: 2 Major Changes in IMAGE_SAFETY Filtering and Well-known IP Restrictions

Google's AI image generation service, Nano Banana Pro, underwent a major policy adjustment in late January 2026 (around January 24th). Two significant changes—strengthened IMAGE_SAFETY content filtering and strict restrictions on famous IP character generation—have sparked widespread discussion within the developer community. This article provides a deep dive into these updates to help you understand the background and find effective workarounds.

Core Value: A 3-minute guide to the core content, underlying reasons, and practical impact of the Nano Banana Pro policy adjustment, including 5 actionable strategies.

Core Information on Nano Banana Pro Policy Adjustments

Quick Overview

Deep Dive: 2 Core Changes

Change 1: Famous IP Character Restrictions

The Situation:

When you try to generate images featuring well-known brand characters (like Disney, Marvel, Star Wars, etc.), the system now rejects the request. IP-related content that previously generated without issues now triggers a copyright protection mechanism.

Affected IP Scope:

Example Error Response:

{
  "finishReason": "SAFETY",
  "finishMessage": "The image was filtered because it may contain copyrighted characters or content."
}

Change 2: Strengthened IMAGE_SAFETY Content Filtering

The Situation:

Content safety audits have become significantly stricter. Previously compliant content (such as e-commerce product photos or fashion photography) may now trigger IMAGE_SAFETY filters.

Typical Error Response:

{
  "finishReason": "IMAGE_SAFETY",
  "finishMessage": "Unable to show the generated image. The image was filtered out because it violated Google's Generative AI Prohibited Use policy. You will not be charged for blocked images. Try rephrasing the prompt. If you think this was an error, send feedback.",
  "safetyInfo": {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "blocked": true
  }
}

Affected Scenarios:

Background Analysis of Nano Banana Pro Policy Adjustments

Driven by Disney Copyright Lawsuits

In December 2025, Disney sent a Cease and Desist Letter to Google, accusing Google's AI image generation services of "massive scale copyright infringement."

Key Points of Disney's Allegations:

In the letter, Disney provided evidence of users generating images of its characters using simple prompts, claiming Google is "intentionally amplifying the scope of its infringement."

🎯 Background Info: Interestingly, while Disney is accusing Google of infringement, they also announced a $1 billion partnership with OpenAI, licensing their characters for use on the Sora video platform. This suggests Disney isn't against AI generation itself, but rather demands formal authorization and a share of the commercial revenue.

Increasing Industry Compliance Pressure

Beyond Disney, groups of visual artists have also filed copyright lawsuits against Google, alleging that the Imagen model was trained on their works without authorization. These legal pressures have pushed Google to proactively strengthen its content filtering.

Google's Policy Level Adjustments

In December 2024, Google updated its Generative AI Prohibited Use Policy, explicitly forbidding:

• Generating content that infringes on third-party intellectual property rights
• Using personal data or biometric features without consent
• Creating Deepfake content
• Generating non-consensual intimate imagery

This recent policy adjustment for Nano Banana Pro is the concrete implementation of these policies at the product level.

Analysis of Nano Banana Pro IMAGE_SAFETY Filtering Mechanism

How the Filter Works

The Nano Banana Pro safety filtering system uses a multi-layered moderation mechanism:

Layer 1: Prompt Moderation

• Analyzes the text prompts entered by the user
• Detects sensitive keywords and semantic intent
• Identifies potential copyright-related descriptions

Layer 2: Generation Process Monitoring

• Real-time detection of image features during the generation process
• Comparison against a database of known IP features
• Assessment of content safety scores

Layer 3: Output Moderation

• Performs a comprehensive scan of the final image
• Applies HARM_CATEGORY classifiers
• Decides whether to release or intercept the output

Detailed Filter Categories

Why Is Legitimate Content Being Blocked?

There's been plenty of feedback on developer forums: why are legitimate lingerie product images for e-commerce being blocked by IMAGE_SAFETY?

Reasoning:

1. Overfitted Safety Models: To ensure no violations slip through, safety models tend to adopt a "better safe than sorry" approach.
2. Lack of Contextual Judgment: It's hard for the system to distinguish between "e-commerce product shots" and "inappropriate content."
3. Tightened Global Strategies: Under pressure from legal lawsuits, Google has chosen to raise the overall filtering threshold.
4. Black-box Decisions: Users often can't tell which specific rule was triggered.

💡 Tech Insight: By using the request log analysis features of the APIYI (apiyi.com) platform, developers can get a clearer understanding of why specific requests were blocked, which helps in optimizing prompt strategies.

Impact of Nano Banana Pro Policy Adjustments on Developers

Direct Impact

Business-Level Impact

For the E-commerce Industry:

• AI product image generation solutions for apparel e-commerce need adjustment.
• The lingerie and swimwear categories are the most significantly affected.
• Businesses might need to revert to traditional photography or switch to alternative services.

For Content Creators:

• Derivative works and fan art generation are restricted.
• There's a need for more original designs rather than relying on IP borrowing.
• Creative freedom has seen a noticeable decline.

For AI Application Developers:

• More error-handling logic needs to be added to products.
• User experience might suffer due to frequent blocking.
• It may be necessary to introduce backup image generation services.

5 Strategies to Handle Nano Banana Pro Policy Adjustments

Strategy 1: Optimize Prompt Phrasing

If you're facing IMAGE_SAFETY false positives, you can try adjusting how you phrase your prompts:

# Before optimization - easy to trigger a block
prompt_before = "model wearing lace bra, fashion photography"

# After optimization - reduces the probability of misidentification
prompt_after = "fashion catalog photo, model in elegant intimate apparel, professional studio lighting, clean background"

Optimization Tips:

Strategy 2: Adjust Safety Filter Settings

If you're using the Vertex AI version of Imagen, you can try adjusting the safety filter thresholds:

from google.cloud import aiplatform
from vertexai.preview.vision_models import ImageGenerationModel

# Adjust safety settings
safety_settings = {
    "HARM_CATEGORY_SEXUALLY_EXPLICIT": "BLOCK_ONLY_HIGH",
    "HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_ONLY_HIGH",
}

model = ImageGenerationModel.from_pretrained("imagen-3.0")
response = model.generate_images(
    prompt="your prompt here",
    safety_filter_level="block_only_high"  # Lower filtering sensitivity
)

⚠️ Note: You can't adjust safety settings in the free version of AI Studio; you'll need the paid Vertex AI version.

Strategy 3: Use Alternative Image Generation Services

When Nano Banana Pro isn't suitable, consider other image generation services:

🚀 Quick Switch: Through the APIYI (apiyi.com) platform, developers can use a unified API interface to call multiple image generation models. You can switch quickly without changing your code. When one service is blocked, you can seamlessly switch to a backup.

Strategy 4: Establish a Multi-Model Fault-Tolerance Mechanism

Implement automatic fallback logic in your application:

import openai

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

def generate_image_with_fallback(prompt, models=None):
    """Image generation function with fault tolerance"""
    if models is None:
        models = ["nano-banana-pro", "dall-e-3", "flux-schnell"]

    for model in models:
        try:
            response = client.images.generate(
                model=model,
                prompt=prompt,
                size="1024x1024"
            )
            return {
                "success": True,
                "model_used": model,
                "image_url": response.data[0].url
            }
        except Exception as e:
            error_msg = str(e)
            if "IMAGE_SAFETY" in error_msg or "SAFETY" in error_msg:
                print(f"{model} blocked by safety filter, trying next model...")
                continue
            else:
                print(f"{model} call failed: {error_msg}")
                continue

    return {"success": False, "error": "All models failed to generate this content"}

# Usage example
result = generate_image_with_fallback("fashion product photography, elegant dress")

import openai
import time
import logging
from typing import Optional, List, Dict, Any

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

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

class ImageGenerationService:
    """Image generation service with fault tolerance and retries"""

    DEFAULT_MODELS = ["nano-banana-pro", "dall-e-3", "flux-schnell", "sd-xl"]

    def __init__(self, models: Optional[List[str]] = None, max_retries: int = 2):
        self.models = models or self.DEFAULT_MODELS
        self.max_retries = max_retries
        self.stats = {"success": 0, "safety_blocked": 0, "errors": 0}

    def generate(self, prompt: str, size: str = "1024x1024") -> Dict[str, Any]:
        """Generate images, automatically switching models on failure"""

        for model in self.models:
            for attempt in range(self.max_retries):
                try:
                    logger.info(f"Attempting image generation with {model} (Attempt {attempt + 1})")

                    response = client.images.generate(
                        model=model,
                        prompt=prompt,
                        size=size
                    )

                    self.stats["success"] += 1
                    logger.info(f"✅ Successfully generated image using {model}")

                    return {
                        "success": True,
                        "model_used": model,
                        "image_url": response.data[0].url,
                        "attempts": attempt + 1
                    }

                except Exception as e:
                    error_msg = str(e)

                    if "IMAGE_SAFETY" in error_msg or "SAFETY" in error_msg:
                        self.stats["safety_blocked"] += 1
                        logger.warning(f"⚠️ {model} blocked by safety filter")
                        break  # Don't retry on safety blocks, just switch models

                    elif "rate_limit" in error_msg.lower():
                        logger.warning(f"⏳ {model} rate limited, retrying after wait...")
                        time.sleep(2 ** attempt)  # Exponential backoff
                        continue

                    else:
                        self.stats["errors"] += 1
                        logger.error(f"❌ {model} call failed: {error_msg}")
                        break

        return {
            "success": False,
            "error": "All models failed to generate this content",
            "stats": self.stats
        }

    def get_stats(self) -> Dict[str, int]:
        """Get statistics"""
        return self.stats

# Usage example
service = ImageGenerationService()
result = service.generate("professional fashion photography, model in elegant evening dress")

if result["success"]:
    print(f"Success! Model used: {result['model_used']}")
    print(f"Image URL: {result['image_url']}")
else:
    print(f"Failed: {result['error']}")

Strategy 5: Adjust Business Strategies

For apps that rely heavily on well-known IPs, a fundamental pivot in business direction might be necessary:

FAQ

Industry Impact of Nano Banana Pro Policy Adjustments

Impact on the AI Image Generation Industry

This policy adjustment reflects a major turning point in the AI image generation industry:

Impact on the Developer Ecosystem

• A multi-model strategy is now a must: You can't rely on a single image generation service anymore.
• Fault-tolerance mechanisms need strengthening: Apps need to handle generation failures gracefully.
• Compliance awareness needs a boost: Developers need a better understanding of copyright and content safety boundaries.

💡 Development Tip: We recommend that developers use a unified interface platform like APIYI (apiyi.com) to access multiple image generation services. This reduces the risk of relying on a single provider and lets you flexibly switch to the best model for different scenarios.

Summary

The January 2026 policy adjustments for Nano Banana Pro brought two core changes: restrictions on generating famous IP and strengthened IMAGE_SAFETY filtering. This is driven by legal pressure from copyright holders like Disney and Google's proactive efforts to raise compliance standards.

Key Takeaways:

For developers, the most pragmatic approach is to accept and actively adapt to these changes. We recommend using APIYI (apiyi.com) to quickly test various image generation models and find the best solution for your specific business scenario. The platform provides a unified API interface and supports one-click switching between different models, offering a solid safeguard against policy changes from service providers.

References:

1. Google Generative AI Prohibited Use Policy: Official usage policy document

   • Link: policies.google.com/terms/generative-ai/use-policy

2. Vertex AI Imagen Safety Filtering Documentation: Safety settings configuration guide

   • Link: docs.cloud.google.com/vertex-ai/generative-ai/docs/image/responsible-ai-imagen

3. Google AI Developers Forum: Nano Banana Pro IMAGE_SAFETY discussion thread

   • Link: discuss.ai.google.dev/t/nano-banana-pro-suddenly-blocking-non-nsfw-ecommerce-underwear-images-with-image-safety-error/113109

4. Disney vs Google Reporting: Disney sends cease and desist letter to Google

   • Sources: Hollywood Reporter, The Wrap (December 2025)

📝 Author: APIYI Team
📅 Release Date: 2026-01-26
🔗 Technical Support: APIYI apiyi.com – Your One-Stop AI Model API Service Platform