Nano Banana 2: How to Avoid Label Hallucination in Commercial Product Shots

The Symptom: Unwanted Text and Distorted Labels

When creating commercial product shots using Nano Banana 2, a common frustration arises when the AI generates text that was not requested or distorts existing labels on the product. This phenomenon, known as label hallucination, occurs when the model invents brand names, slogans, or gibberish characters that look like text but are semantically meaningless. In a professional context, this is particularly problematic because it compromises the integrity of the product image. Users often expect the tool to preserve specific typography or avoid adding random text overlays, yet the output may include floating words, misspelled logos, or completely fabricated branding elements. This symptom indicates a gap between the user's intent for a clean, accurate product representation and the model's probabilistic generation of visual details.

Separating Plausible Causes from Known Facts

To effectively troubleshoot this issue, it is crucial to distinguish between what users might assume causes the problem and the verified technical facts provided by the platform. A plausible cause often cited by users is that the prompt instructions are too vague, leading the AI to "fill in the blanks" with text. While this is partially true, the core reality lies in how the underlying models function. Prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. This means that even with highly detailed prompts specifying "no text," the model may still generate text-like artifacts if the training data associates certain shapes with commercial packaging.

Furthermore, there is a distinction between the different versions of the tool available. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image), while Nano Banana Pro corresponds to Gemini 3 Pro Image (gemini-3-pro-image). These are distinct Google image models with varying capabilities. It is important to note that Nano Banana refers to the AI image generation/editing tool in these articles. It is not a skincare brand, bottle, jar, or physical subject. Example products used in documentation are generic and unbranded to avoid confusion. Users should be aware that the website has a Nano Banana 2 product page at /nanobanana2 and supports text-to-image and image-to-image workflows, but the specific behavior regarding text rendering depends heavily on the selected model version.

Diagnosis and Strategic Fixes

Diagnosing label hallucination requires analyzing the workflow and the specific model being used. If you are attempting to generate multiple reference inputs or perform multi-turn sequential editing, you must verify which model you are utilizing. Google describes Nano Banana 2 Lite as focused on speed and cost. It is not optimized for multiple reference inputs or multi-turn sequential editing. Do not recommend it for those workflows without explaining this limitation. Using Nano Banana 2 Lite for complex commercial shots where label accuracy is paramount may increase the likelihood of hallucinations due to its optimization focus rather than precision.

The primary strategy to mitigate this issue involves refining your approach to prompt engineering and understanding the limitations of the current technology. Since prompt instructions do not guarantee typography preservation, users should explicitly state negative constraints in their prompts, such as requesting "clean packaging with no visible text" or "generic design." However, users must understand that these are examples of how to frame requests, not guarantees of outcome. The prompt library offers example prompts that users can copy or take into the generator, but these serve as starting points rather than absolute solutions. For high-stakes commercial work, relying solely on text prompts may be insufficient. Consider using image-to-image workflows where you upload a reference image of the product without any text, though success is not guaranteed.

For users requiring higher fidelity, exploring the Nano Banana Pro page at /nanobananapro might offer different results compared to the standard Nano Banana 2 interface. Note that the website has a Nano Banana Pro page at /nanobananapro. Its page named Nano Banana Lite at /nanobananalite does not by itself establish support for Google Nano Banana 2 Lite. Google model names and capabilities must not be presented as proof of availability or identical features on this website. Always cross-reference the specific capabilities of the model you select against your need for text control.

Verification and Managing Expectations

After applying these strategies, verification is essential. Generate multiple variations of the same prompt and inspect each result closely for subtle text artifacts. Look for gibberish strings, distorted letterforms, or unexpected brand markers. If hallucinations persist, it is likely a fundamental limitation of the current generative process rather than a user error. It is vital to manage expectations regarding text rendering. No current method guarantees perfect text avoidance or preservation without post-processing. Users should view the generated images as drafts that may require manual editing in external software to remove unwanted text or correct labels.

By understanding that prompt instructions do not guarantee identity or typography preservation, users can better navigate the tool's capabilities. Remember that Nano Banana names the image tool, never the depicted cosmetic brand or physical product. For those ready to experiment with these techniques, Try Nano Banana to access the latest generation capabilities and test your own strategies for minimizing label hallucination in your commercial projects.

Ultimately, avoiding label hallucination is an iterative process involving careful prompt construction, appropriate model selection, and realistic expectations about AI-generated text. By adhering to these guidelines and leveraging the specific strengths of the Nano Banana 2 ecosystem, users can significantly reduce errors and produce cleaner, more professional product imagery.