Fixing Fake Labels: How to Stop Nano Banana 2 Lite from Hallucinating Text

Understanding the Symptom of Hallucinated Product Labels

When using AI image generation tools, users often encounter a specific visual artifact where the model invents text that does not exist. In the context of Nano Banana 2 Lite, this manifests as the tool generating fake brand names, gibberish lettering, or illegible text on product packaging. Instead of a clean, generic bottle or jar, the output might display a logo that looks like "Nana" or random characters resembling a barcode. This is known as label hallucination.

It is crucial to distinguish between what the tool is designed to do and what it occasionally produces. The symptom is clear: the generated image contains typography that was not requested or is nonsensical. This occurs because the underlying model attempts to complete patterns associated with commercial products, assuming they must have labels. However, these invented details are errors in the generation process rather than intentional design choices. Users should not interpret these fake labels as features or accurate representations of real-world brands.

Separating Plausible Causes from Known Facts

To resolve this issue, one must separate plausible user assumptions from the verified technical facts provided by Google. A common misconception is that the model can be instructed to preserve existing text perfectly or that it will automatically know which brand to depict based on a vague description. This is not supported by the current capabilities of the system.

Verified facts indicate that Nano Banana refers strictly to the AI image generation and editing tool. It is not a skincare brand, nor is it a physical bottle or jar. The prompt instructions describe desired outcomes but do not guarantee the preservation of identity, specific objects, or typography. Furthermore, Google documents Nano Banana 2 Lite as Gemini 3.1 Flash Lite Image. This specific model is optimized for speed and cost efficiency. Crucially, it is not optimized for multiple reference inputs or multi-turn sequential editing. Because of these architectural limitations, relying on the model to handle complex text rendering or precise brand replication is likely to result in hallucinations.

Another fact to consider is the distinction between the website's product pages and the actual model capabilities. While there is a Nano Banana 2 product page at /nanobanana2, the existence of a page named Nano Banana Lite does not automatically establish support for the specific Google Nano Banana 2 Lite model features. Model names and capabilities described by Google must not be presented as proof of identical features on this website. Therefore, expecting the Lite version to perform high-fidelity text tasks without explicit constraints is a mismatch between expectation and reality.

Diagnosing the Root Cause of Typography Errors

The root cause of these hallucinated labels lies in the model's training data and its optimization goals. Since Nano Banana 2 Lite prioritizes speed and cost, it may default to generating generic commercial aesthetics, including text, even when not explicitly asked. The model assumes a product needs a label to look realistic. Without strict negative constraints, the AI fills the visual space with text-like patterns.

Additionally, the prompt library offers example prompts that users can copy. These examples describe desired outcomes but do not guarantee object or typography preservation. If a user copies a prompt that implies a branded product without specifying the absence of text, the model will hallucinate a brand name. The diagnosis is straightforward: the prompt lacks a definitive instruction to exclude all typography, and the model's inherent tendency to complete product imagery overrides the user's silent intent for a blank label.

Fixing the Issue with Explicit Negative Prompting

The most effective fix is to explicitly instruct the model to omit all typography. When crafting your prompt for Nano Banana 2 Lite, you must clearly state that the product should be unbranded and free of any text. Use phrases such as "no text," "blank label," "unbranded packaging," or "generic container." Do not rely on the model to infer this; you must state it as a hard constraint.

For instance, instead of prompting for "a luxury shampoo bottle," prompt for "a luxury shampoo bottle with no text and a plain white label." This direct instruction helps guide the model away from its default behavior of adding fake logos. Remember that prompt instructions describe desired outcomes but do not guarantee identity or typography preservation. Therefore, being repetitive and clear about the absence of text is essential. You can also try describing the texture of the label as "smooth" or "matte" to further discourage the model from attempting to render letters.

If you find that the Lite version continues to struggle with this despite clear instructions, consider that the model's focus on speed may limit its ability to adhere to complex negative constraints compared to other versions. However, for most cases, explicit exclusion of text is the primary solution. Try Nano Banana to experiment with these refined prompts and observe the difference in output quality.

Verifying the Solution and Managing Expectations

After applying the new prompts, verify the results by inspecting the generated images closely. Look specifically at the product surface for any stray characters, logos, or barcode-like patterns. If the image remains clean and free of text, the fix has been successful. If hallucinations persist, refine your prompt by adding more descriptive terms about the lack of branding, such as "commercially generic" or "placeholder design."

It is important to maintain realistic expectations regarding the outcome. No AI tool can guarantee perfect results every time, especially when dealing with complex constraints like text removal. The goal is to significantly reduce the frequency of hallucinations, not necessarily to eliminate them entirely in every single generation. By understanding the limitations of Nano Banana 2 Lite and using precise language, you can effectively avoid fake brand names and achieve the clean, unbranded product visuals you need.