Nano Banana 2 - Google's next-generation image generation model

What is Nano Banana 2?

Nano Banana 2 is a next-generation image generation model (Gemini 3.1 Flash Image) from Google DeepMind. The model integrates with the Gemini knowledge base and real-time web search, accurately rendering realistic scenes and generating multilingual text. It supports maintaining consistency across 5 characters or 14 items in a single generation. The model's resolution ranges from 512px to 4K, and its API price is only half that of the previous generation Nano Banana Pro. The model is fully integrated with platforms such as the Gemini App, Google API, and Vertex AI, providing developers and creators with a cost-effective visual generation solution.

Main functions of Nano Banana 2

• World Knowledge EnhancementIt integrates with the Gemini knowledge base and real-time web search, enabling it to accurately understand and draw real-world landmarks, buildings, and scenes.
• Infographic generationIt can convert notes and data into professional diagrams, popular science illustrations, and data visualizations.
• Multilingual text renderingIt supports accurate generation of text in multiple languages such as Chinese and English, eliminating the "scribbles" problem of traditional AI-generated images.
• In-image translation localizationIt can directly translate and adjust visual elements in images, enabling one-click global adaptation of content such as advertisements.
• Maintaining role consistencyIn a single generation process, the facial features and appearance of up to 5 characters can be kept completely consistent.
• Maintaining item consistencyA single generation can ensure that the appearance features of up to 14 items are not deformed or altered.
• Multiple resolution outputsIt supports four resolutions: 512px, 1K, 2K, and 4K, to meet the efficiency and quality requirements of different scenarios.
• Flexible aspect ratio adaptationIt natively supports extreme aspect ratios such as 4:1, 1:4, 8:1, and 1:8, without the need for post-processing cropping.
• Configurable thinking levelsIt offers three levels of inference depth: Minimal, High, and Dynamic, balancing generation speed with the accuracy of prompt word adherence.
• Digital watermark tracingIt integrates SynthID and C2PA technologies to tag AI-generated content and support source verification.

The technical principle of Nano Banana 2

• Underlying architectureBased on the Gemini 3.1 Flash multimodal large model, it adopts native multimodal design, and text and images are jointly modeled in a unified representation space, rather than being stitched together later.
• Knowledge EnhancementBy enhancing the generation mechanism through retrieval, the Gemini knowledge base is invoked in real time and combined with network image search to inject real-world visual references into the generation process.
• Diffusion optimizationIntroducing configurable thinking levels in diffusion sampling allows for dynamic adjustment of inference computation, achieving a flexible trade-off between speed and quality.
• Consistency maintenanceThe model employs object-level feature caching technology to lock the high-dimensional semantic features of the subject in a single generation, ensuring stable appearance for multiple characters and items.
• Text renderingAn independent glyph-aware decoding branch decouples text localization, structure prediction, and style rendering, significantly improving the accuracy of multilingual text generation.
• Security traceabilityThe SynthID digital watermark is embedded in the latent space and bound to C2PA metadata signature to realize the source verification and tracking of generated content.

How to use Nano Banana 2

Nano Banana 2 project address

• Project official website: https://blog.google/innovation-and-ai/technology/ai/nano-banana-2/

Application scenarios of Nano Banana 2

• Advertising and MarketingThe model can quickly generate multilingual localized advertising creatives, adapting to different languages and cultural scenarios in the global market with a single click.
• e-commerce designConvert low-quality product images into professional-grade display images, and mass-produce product main images and detail pages with a unified style.
• Game developmentThe model can generate high-precision game UI interfaces, character concept art, and scene original artwork, and supports consistent narrative design for multiple characters.
• Comic creationIt supports maintaining stable facial features of characters and continuously generating storyboard pages, significantly shortening the production cycle of serialized comics.
• Education and TrainingThe model can transform knowledge points into infographics and diagrams, creating intuitive and easy-to-understand teaching materials and popular science content.