Fixing Unexpected Typography in Nano Banana 2 Generated Images

When using the Nano Banana image generation tool, you may occasionally encounter a frustrating issue where the output contains unexpected typography. Instead of a clean visual scene, the generated image might display garbled characters, nonsensical lettering, or random text strings that were never part of your original vision. This symptom often appears as distorted scribbles across the center of an image or illegible words floating in the background. It is important to distinguish this behavior from intentional design choices; when the goal is a purely graphical composition, any form of text is considered an error.

It is crucial to separate plausible user expectations from known technical facts regarding this specific tool. While many users assume that AI models can perfectly ignore text requests if they are not explicitly asked for it, the reality is that these systems sometimes hallucinate textual elements even when prompted to avoid them. The prompt instructions provided by the system describe desired outcomes but do not guarantee the preservation or exclusion of identity, labels, objects, or typography. Therefore, finding random text in an image is not necessarily a bug in the code, but rather a limitation in how the underlying model interprets negative constraints within complex visual scenes.

Understanding the Root Causes of Garbled Text

To effectively troubleshoot this issue, one must first understand why the Nano Banana 2 model generates text when none was requested. The primary cause is often ambiguity in the prompt structure. If a prompt includes words that are commonly associated with signage, labels, or writing—such as "storefront," "menu," "sign," or even abstract concepts like "message"—the model may interpret these as instructions to render visible text. Even subtle descriptors can trigger the generation engine to create typographic artifacts.

Another contributing factor involves the specific version of the model being used. Google documents Nano Banana 2 as Gemini 3.1 Flash Image, while Nano Banana Pro corresponds to Gemini 3 Pro Image. These are distinct models with different capabilities. For instance, Nano Banana 2 Lite is focused on speed and cost and is not optimized for multiple reference inputs or multi-turn sequential editing. Using a model variant that is not suited for high-fidelity control over specific elements like typography can increase the likelihood of errors. Furthermore, the prompt library offers example prompts that users can copy, but these examples are untested for every specific use case and should be treated as starting points rather than guaranteed solutions.

It is also worth noting that the website supports both text-to-image and image-to-image workflows. In image-to-image scenarios, if the source image contains any text, the model might attempt to preserve or replicate it unless explicitly told otherwise. However, since prompt instructions do not guarantee object preservation, relying on the model to automatically strip text from a reference image without clear negative prompting can lead to the same unexpected results seen in text-to-image generation.

Strategies to Eliminate Typography Completely

The most effective way to fix unexpected typography is to adjust your prompts to exclude textual elements entirely. This requires a proactive approach to prompt engineering. Start by removing any nouns or adjectives that imply the presence of writing. Instead of describing a "busy street sign," describe the "colorful urban background." By focusing purely on visual attributes like lighting, texture, color palette, and composition, you reduce the probability of the model generating text.

If you have already generated an image with unwanted text, try regenerating the image with a more restrictive prompt. Explicitly state what you do not want included. Phrases such as "no text," "no letters," "no writing," or "purely graphical" can help steer the model away from typographic outputs. However, remember that these instructions do not guarantee identity or label preservation, so absolute certainty is not possible. You may need to iterate several times to achieve the desired result.

For users who require high precision, consider switching between the available product tiers. If you are currently using a standard workflow, exploring the features on the Nano Banana 2 product page at /nanobanana2 might reveal advanced settings or better-suited model options. Alternatively, Try Nano Banana to access the latest tools designed for cleaner generation. Always ensure you are selecting the correct model variant for your needs, as Nano Banana 2 Lite, for example, has specific limitations regarding multi-turn editing that could affect complex troubleshooting tasks.

Verifying Your Results and Next Steps

Once you have adjusted your prompts, verify the outcome by reviewing the generated images closely. Look specifically for any residual artifacts, faint outlines of letters, or distorted shapes that resemble text. If the image still contains unwanted typography, refine your prompt further by adding more descriptive visual details to distract the model from textual patterns. You can also consult the prompt library for inspiration, keeping in mind that these are examples and not tested guarantees for your specific scenario.

If issues persist despite careful prompt engineering, it may be necessary to evaluate whether the current model version is the right fit for your project. Remember that Google describes Nano Banana 2 as Gemini 3.1 Flash Image, and its capabilities differ from other variants. Do not assume that all versions handle text exclusion equally well. By systematically adjusting your inputs and understanding the distinction between the available models, you can significantly reduce the occurrence of garbled text and produce the clean, professional visuals you intend.

Ultimately, successful troubleshooting relies on a combination of precise language and an understanding of the tool's inherent limitations. By treating text generation as a variable to be managed rather than ignored, you can maintain full creative control over your Nano Banana projects.