Fixing Garbled Characters in Nano Banana 2: A Troubleshooting Guide

When users attempt to generate images containing specific text using Nano Banana 2, they sometimes encounter a frustrating issue where the final output displays garbled characters or unreadable symbols. This phenomenon is often described as character corruption. It is crucial to approach this symptom with the correct understanding of how the underlying technology functions. The primary cause of these issues lies in the fundamental design philosophy of the tool rather than a software bug or a failure of the user's prompt.

Nano Banana refers to the AI image generation and editing tool discussed here. It is distinct from any skincare brand, bottle, jar, or physical subject. When you input a request for text, the system does not process it as a database entry or a string of code that must be preserved exactly. Instead, the model treats text as visual patterns. It analyzes the shapes, strokes, and spacing associated with letters to recreate their appearance within the generated image. Because the goal is visual fidelity rather than data integrity, the tool cannot guarantee identity, label, object, or typography preservation. This distinction explains why specific words might appear distorted or nonsensical even when the prompt was clear.

Separating Symptoms from Known Facts

To effectively troubleshoot garbled outputs, one must separate the observed symptom from the known facts about the platform. The symptom is straightforward: the text in the generated image looks like gibberish, random glyphs, or broken fonts. However, the fact behind this symptom is that prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation.

It is important to note that 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 different capabilities. While the Pro version may offer higher resolution or better coherence in complex scenes, neither model is designed to function as a typesetting engine. The limitation is inherent to the generative nature of the technology. The website supports text-to-image and image-to-image workflows, but the text component is always subject to the interpretation of the visual pattern generator.

Users should also be aware that the prompt library offers example prompts that users can copy or take into the generator. These examples demonstrate creative possibilities but serve only as inspiration. They do not establish a precedent for perfect text rendering. If an example shows readable text, it is a result of the model successfully approximating the visual shape of those letters, not because the system has locked in the exact character data. Assuming that a successful example guarantees success for your own specific text requests is a common misconception that leads to confusion when results vary.

Diagnosing the Root Cause of Corruption

The diagnosis for garbled characters in Nano Banana 2 outputs points directly to the probabilistic nature of the model. Since the AI predicts pixel values based on learned visual patterns, it attempts to "guess" what the letters should look like based on context. If the requested text is complex, unusual, or placed in a difficult position within the image, the model may struggle to align the strokes correctly. This results in the characteristic corruption seen by users.

This behavior is not unique to a single version of the tool. Whether you are using the standard Nano Banana 2 workflow or the Pro variant, the core mechanism remains the same regarding text handling. The tool prioritizes the overall aesthetic and composition over the semantic accuracy of the written word. Therefore, the presence of garbled text is not necessarily a sign of a broken connection or a corrupted file. It is a predictable outcome when asking a visual generation engine to perform a task it was not explicitly optimized for: precise typography.

Furthermore, users should avoid confusing the availability of different product tiers with text capabilities. For instance, 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. Similarly, having access to a Nano Banana Pro page at /nanobananapro does not imply that text rendering is flawless. The website has a Nano Banana 2 product page at /nanobanana2 and supports various workflows, but the fundamental constraint on text preservation applies across the board unless otherwise specified by the model architecture itself.

Practical Steps to Mitigate Text Issues

While you cannot force the AI to render text with 100% accuracy due to its design as a visual pattern matcher, there are strategies to mitigate the issue. First, simplify your text requests. Shorter phrases with common letter combinations are more likely to be rendered correctly than long sentences or obscure words. Second, consider the placement of text. Text integrated naturally into the scene, such as a sign on a building or a label on a product, often fares better than floating text overlays that require perfect alignment.

If the generated text remains garbled despite these adjustments, the most reliable solution is to treat the image as a base layer and add text using external graphic design tools. This approach separates the visual generation from the typographic requirement, ensuring the final output meets your readability standards. You can use the generated image as a background and overlay the necessary text with precision using standard editing software.

For those looking to experiment with the tool's capabilities, you can explore the prompt library for ideas on how to structure your requests for better visual harmony. Remember that these are untested prompt examples intended to guide your creativity, not to guarantee specific text outcomes. By adjusting expectations and utilizing post-processing techniques, you can work around the limitations of the current technology.

If you are ready to try generating images and want to see how the tool handles your specific visual concepts, Try Nano Banana.

Verifying Your Results

After applying these troubleshooting steps, verify your results by comparing the new output against your original intent. Check if the text is more legible or if the visual composition has improved. If the text is still garbled, acknowledge that this is a known limitation of treating text as visual patterns rather than data. There is no setting or toggle that switches the mode to "typography preservation." The verification process confirms whether the image meets your artistic goals, even if the text requires manual correction later.

Ultimately, understanding that Nano Banana names the image tool, never the depicted cosmetic brand or physical product, helps clarify the scope of the application. The tool creates images; it does not print labels. By recognizing this boundary, users can focus on leveraging the AI's strengths in composition and style while managing its weaknesses in text accuracy through careful prompting and external editing.