In the field of AI-powered image processing, a mysterious model called Nano Banana quietly emerged, quickly garnering attention for its astonishing image quality and character consistency. Initially appearing anonymously on LMArena, the world’s leading AI model competition, the model quickly climbed the charts for both image processing and image editing, ultimately securing the top spot through user “blind” voting.
Speculation surrounding Nano Banana was rampant, with some believing it was a secret experiment by OpenAI, while others speculated it was the work of an independent research team. It wasn’t until late August that Google officially claimed the model, revealing its true identity: Gemini 2.5 Flash Image. As an upgraded version of Gemini 2.0 Flash, Nano Banana not only maintains high character and image consistency across multiple edits but also supports natural language-driven, detailed local modifications and multi-image synthesis, making it an AI editor more closely aligned with real-world workflows.
Nano Banana’s core breakthrough lies in its novel “alternative generation” paradigm. By breaking complex instructions into multiple steps, the model makes only minor adjustments at each step, such as changing clothing first and then the background, ultimately adding up all the changes. This design avoids the “amnesia” problem associated with traditional models’ one-shot, random changes, ensuring that subject features remain stable across multiple rounds of editing. For example, a user can change the color of a jacket in a photo from blue to red or adjust a person’s pose without affecting facial features or overall proportions.
In terms of multi-image fusion, Nano Banana demonstrates powerful scene integration capabilities. Traditional models often produce stylistic inconsistencies, spatial distortion, or loss of detail when synthesizing two images. Nano Banana automatically maintains logical consistency between different images. For example, when merging a photo of a person with a beach background, the model ensures that the lighting and proportions of the person match naturally with the background, even adjusting the person’s pose to fit the new environment. Users can use natural language commands to perform actions like “move the person to Paris” or “replace the background with snow-capped mountains,” without having to manually draw masks or use specialized tools.
Another key feature of Nano Banana is its precise, natural language-driven editing capabilities. Users simply describe their needs, such as “remove a person in a photo,” “change the background to a forest,” or “adjust the person’s expression to a smile,” and the model will automatically make the changes while preserving all other details. Users can even use stick figures or sketches instead of text instructions, further simplifying the process. For example, a user can draw a stick figure pose, and the model will accurately apply it to a photo of a person, generating a logically appropriate new image.
Nano Banana supports contextual memory during multi-round conversational editing. Users can make incremental changes, such as adjusting a room’s color, then adding furniture, and finally changing the lighting. The model will remember all previous actions to avoid duplication or conflicts. Users can also experiment with style combinations, such as applying a petal texture to a shoe or transforming a butterfly wing pattern into a skirt design, creating images that are both creative and practical.
For security, Google adds a visible watermark and an invisible digital watermark, SynthID, to images generated by Nano Banana, ensuring traceability. This design not only protects original content but also provides a new approach to copyright management for AI-generated content.
Nano Banana is currently available to ordinary users through the Google Gemini application, Google AI Studio, the Gemini API, and the Vertex AI platform. Platforms such as Adobe and Lovart have also integrated it into creative tools. It generates images extremely quickly, allowing users to input commands and edit them in seconds. For example, replacing the background of a tourist photo with a Maldivian beach or changing the fur color of a pet to resemble a Tibetan mastiff can be achieved in a matter of seconds.
While Nano Banana excels in character consistency and multi-image fusion, it still has some limitations. For example, in complex, multi-turn conversations, the model may lose context, resulting in deviating from expected results. Users have reported that its image resolution needs improvement, and its artistic quality is slightly inferior to models like Midjourney. However, considering that it costs only $0.039 (approximately RMB 0.3) to generate a single image, its cost-effectiveness is widely recognized.
The release of Nano Banana marks a further step forward for Google in multimodal AI. From the Imagen series for image generation, the Veo series for video generation, to the Genie series for interactive world generation, Google has built a comprehensive product portfolio covering images, videos, and virtual worlds. In the future, Google may integrate more model capabilities through the Gemini platform, creating a multimodal portal for general users while also providing in-depth services for professional developers.
