Nano Banana

Google’s state-of-the-art image generation and editing model designed for fast, conversational, and multi-turn creative workflows.

Overview

Gemini 2.5 Flash Image (internally codenamed “nano-banana”) is a multimodal model that natively understands and generates images. This model combines high-quality image generation with powerful editing capabilities, all controlled through natural language prompts. It’s designed for creators who need precise control over their visual content while maintaining efficiency and ease of use.

Key Features

Character and Style Consistency

Maintain the same character, object, or style across multiple prompts and images. Place a character in different environments, showcase products from multiple angles, or generate consistent brand assets without time-consuming fine-tuning.

Multi-Image Fusion

Seamlessly blend multiple input images into a single, cohesive visual. Integrate products into new scenes, restyle environments by combining different elements, or merge reference images to create unified compositions.

Conversational Editing

Make precise, targeted edits using natural language descriptions. Blur backgrounds, remove objects, alter poses, add color to black-and-white photos, or make any other transformation by simply describing what you want.

Visual Reasoning

Leverage Gemini’s deep world knowledge for complex tasks that require genuine understanding. The model can interpret hand-drawn diagrams, follow multi-step instructions, and generate images that adhere to real-world logic and context.

Native Image Understanding

The model natively understands and generates images as part of its core architecture, enabling seamless workflows for both creation and editing without switching between different tools or models.

What Makes It Special

Gemini 2.5 Flash Image stands out for its ability to understand context and maintain visual coherence across edits. Unlike traditional image generation models that excel only at aesthetics, this model benefits from Gemini’s extensive world knowledge, allowing it to handle tasks like reading hand-drawn diagrams, understanding spatial relationships, and following complex creative directions.

The model is particularly effective at preserving subject identity across generations. Whether you’re creating a series of marketing images featuring the same product or developing character-consistent artwork for storytelling, the model maintains recognizable features without requiring additional training or fine-tuning.

Intended Use

This model is designed for:

• Creative professionals who need consistent visual assets across campaigns
• Product designers visualizing items in different contexts and angles
• Marketers creating cohesive brand materials with consistent styling
• Content creators generating character-consistent imagery for storytelling
• Developers building applications that require conversational image editing
• Educators creating visual materials that require semantic understanding

Limitations

While Gemini 2.5 Flash Image is highly capable, there are some areas where it may not always deliver perfect results:

• Small faces and fine facial details may occasionally lack precision
• Complex text rendering within images may sometimes have spelling inconsistencies
• Character consistency, while strong, may not be 100% reliable in all scenarios
• Very intricate fine details may require multiple refinement iterations

The model is actively being improved to address these limitations.

How It Works

The model processes both text and image inputs through its multimodal architecture. When generating or editing images, it uses its understanding of the Gemini model family’s world knowledge to interpret requests contextually. For editing tasks, it can analyze existing images and apply transformations based on natural language descriptions. For generation tasks, it can reference multiple input images to maintain consistency or blend elements together.

All images created or edited with this model include SynthID watermarking technology, which embeds an invisible digital watermark to help identify AI-generated or AI-edited content.

Performance

Gemini 2.5 Flash Image demonstrates state-of-the-art performance in image editing tasks, as validated by LMArena benchmarks where it tested under the codename “nano-banana.” The model generates images 2-3 times faster than comparable models while maintaining high quality, making it particularly well-suited for applications requiring quick iteration and real-time creative workflows.

Ethical Considerations

Google applies extensive filtering and data labeling to minimize harmful content in training datasets and reduce the likelihood of harmful outputs. The model undergoes red teaming and safety evaluations including content safety, child safety, and representation assessments.

The built-in SynthID watermarking ensures transparency by allowing AI-generated and AI-edited images to be identified, promoting responsible use of AI-generated visual content.

Tips for Best Results

• Be specific with descriptions: Detailed prompts yield more accurate results
• Use natural language: Describe edits conversationally as you would to a human designer
• Iterate progressively: Make changes step-by-step rather than requesting complex multi-part edits at once
• Reference visual templates: When maintaining consistency, use the same reference images across generations
• Leverage multi-image fusion: Combine up to three images to achieve complex compositions
• Experiment with aspect ratios: The model supports multiple aspect ratios for different use cases

Additional Resources

For detailed API documentation and implementation guides, visit the Gemini API documentation.

Try the model yourself on the Replicate Playground to explore its capabilities and see how it can enhance your creative workflow.