Nano Banana 2: Avoiding Unintended Text Artifacts in Image-to-Image Workflows

Understanding the Symptom of Gibberish Text

When utilizing Nano Banana 2 for image-to-image workflows, users may occasionally encounter a specific visual anomaly where intended labels, signs, or typography within an image are replaced by nonsensical characters, random symbols, or distorted letterforms. This phenomenon is often described as generating "gibberish text" rather than preserving the original semantic meaning of the words. Instead of seeing a clear "Open" sign on a shop window or a legible price tag on a product, the output might display a string of unreadable glyphs that vaguely resemble letters but lack coherent structure.

This symptom is particularly noticeable when the user attempts to modify other elements of an image while keeping existing text intact. The AI model processes the entire visual context, and if the prompt instructions do not explicitly prioritize text fidelity, the generation process may treat the text as just another texture or pattern to be reinterpreted, leading to the loss of legibility. It is crucial to recognize this behavior not as a software bug, but as a characteristic limitation of the current generative capabilities regarding precise typography.

Separating Plausible Causes from Known Facts

To effectively troubleshoot this issue, it is necessary to distinguish between what users might assume causes the problem and the verified facts provided by the developers. A common misconception is that the tool fails because of a glitch in the rendering engine or a temporary network error. However, the known facts indicate that the root cause lies in the fundamental design of the prompt instructions and the model's architecture.

Prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. This means that even if a user includes a command like "keep the text exactly the same," the system interprets this as a stylistic preference rather than a strict constraint. The AI is designed to generate new pixels based on the prompt, and without explicit guarantees, it will prioritize visual coherence over textual accuracy. Furthermore, Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image), which is distinct from other models like Nano Banana Pro or Nano Banana 2 Lite. Each model has different optimization goals, and none are explicitly marketed as tools for perfect OCR or typography retention in editing scenarios.

It is also 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. Confusing the tool with a physical product can lead to unrealistic expectations about its ability to manipulate digital text with the precision of a vector graphics editor. The tool generates images, and while it can mimic text, it does not possess a built-in mechanism to lock down character strings during the diffusion process.

Diagnosing and Fixing Typography Issues

Diagnosing the issue involves reviewing the input workflow. If the goal is to edit an image containing text, the user must first acknowledge that the tool does not guarantee typography preservation. The diagnosis should focus on whether the prompt was too vague or if the user expected a level of control that the current version of the model does not offer. For instance, using a prompt that focuses heavily on changing the background or lighting might inadvertently cause the model to "hallucinate" new text patterns where old ones existed.

To fix or mitigate these artifacts, users should adjust their expectations and refine their approach. Since the tool cannot guarantee that labels will remain unchanged, the most effective strategy is to use the image-to-image feature to make broad compositional changes while accepting that any text will likely need to be added back later using external graphic design software. Alternatively, users can try providing very specific negative prompts, though this is not a guaranteed solution given the model's limitations.

For workflows requiring high fidelity in text, users might consider if a different model variant is more suitable, though documentation notes that Nano Banana 2 Lite is focused on speed and cost and is not optimized for multiple reference inputs or multi-turn sequential editing. Therefore, relying on the Lite version for complex text-heavy edits is not recommended without understanding these constraints. The best practice is to treat the generated image as a base layer and add text separately if legibility is critical.

Verifying Results and Next Steps

After applying these adjustments, verification requires a close inspection of the output. Users should check if the text artifacts have been reduced or if the gibberish has been replaced by something closer to the original intent. However, it is vital to avoid claims of guaranteed outcomes. Even with careful prompting, some degree of distortion may persist. If the text remains illegible, the user should conclude that the current workflow is insufficient for their needs and consider post-processing the image.

The community and documentation emphasize that prompt instructions describe desired outcomes but do not guarantee identity or typography preservation. By understanding this boundary, users can better navigate the tool's capabilities. For those looking to explore the full range of features available in the ecosystem, including different model options, you can Try Nano Banana. Remember that while Nano Banana 2 is powerful for creative image generation, it operates differently from traditional text-editing software, and managing expectations is key to successful image-to-image workflows.