Fixing Text Distortion in Nano Banana 2: A Guide to Clean Labels

When working with AI image generation tools, users often encounter a specific challenge: the inability to render legible, accurate text within an image. This is particularly relevant when creating product mockups or packaging designs where labels must be clear. If you have noticed that your generated images feature garbled characters, misspelled words, or distorted typography, you are experiencing a known limitation rather than a software error.

Nano Banana refers to the AI image generation and editing tool in these articles. It is not a skincare brand, bottle, jar, or physical subject. While the visual output may depict generic products, the underlying technology operates differently from traditional graphic design software. Prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. Consequently, expecting the model to perfectly replicate specific text strings or maintain exact brand typography during the generation process is currently beyond its capabilities. This behavior is consistent across the model family, including the versions identified as Gemini 3.1 Flash Image and Gemini 3 Pro Image by Google documentation.

Separating Plausible Causes from Known Facts

It is easy to assume that text distortion stems from user error, such as using vague prompts or incorrect settings. However, it is crucial to separate plausible but incorrect assumptions from verified technical facts. Many users believe that increasing prompt specificity or adjusting negative prompts will force the AI to spell words correctly. Unfortunately, this is not the case.

The core issue lies in how the model processes visual data. The system is designed to understand concepts and visual styles, not to act as a typesetting engine. When a prompt requests a "label" or "text," the model attempts to approximate the visual appearance of text rather than generating actual characters. This results in symbols that look like letters but lack semantic meaning. Furthermore, while the website supports text-to-image and image-to-image workflows, the prompt library offers example prompts that users can copy or take into the generator. These examples illustrate potential outputs but do not guarantee identity or typography preservation.

Additionally, some users might confuse the different tiers of the service. Google documents 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. Even the more advanced versions, such as Nano Banana Pro, which corresponds to Gemini 3 Pro Image, share the fundamental constraint regarding precise text rendering. The presence of a 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. Therefore, switching models alone will not solve the text distortion problem if the goal is perfect typographic accuracy.

Strategies for Generating Clean Base Images

Since the tool cannot reliably produce text, the most effective troubleshooting strategy involves changing your workflow. Instead of trying to fix the text within the generation, focus on generating a flawless base image and adding text externally. This approach separates the creative visual work from the technical limitations of the model.

Start by crafting a prompt that focuses entirely on the visual elements you need: the shape of the container, the lighting, the background texture, and the color palette. Be explicit about the absence of text. For instance, you might request a "clean, empty white bottle with soft studio lighting and no visible text." By explicitly stating what you do not want, you guide the model away from attempting to hallucinate letter forms. This ensures the generated asset is a high-quality canvas ready for post-processing.

Once you have the base image, use dedicated graphic design software or online editors to overlay your label text. This method guarantees that your typography is crisp, legible, and exactly as intended. You can adjust fonts, kerning, and alignment with precision that the AI cannot match. This workflow is applicable whether you are using the standard Nano Banana 2 interface or exploring other variations. For users looking to experiment with different styles before finalizing their design, Try Nano Banana provides a platform to generate these base assets quickly.

Diagnosing and Verifying Your Workflow

To diagnose if you are facing text distortion issues, simply review your generated outputs. If the text appears as squiggly lines, gibberish, or inconsistent shapes, the diagnosis is confirmed: the model attempted to render text visually but failed to preserve character integrity. This is a known fact of the current model architecture.

Verification of your fix comes from comparing the AI-generated base against your final composite. If the base image looks correct (no text artifacts) and the added text is sharp and readable, your workflow is successful. Remember that the goal is to leverage the AI's strength in visual composition while bypassing its weakness in typography. By adopting this hybrid approach, you ensure professional-grade results without fighting against the inherent limitations of the generation engine. Always remember that prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. Accepting this reality allows you to streamline your design process and achieve the clarity your projects require.