Fixing Typography Preservation Failures in Nano Banana 2

When using the AI image generation tool known as Nano Banana, users often encounter a specific challenge: the software fails to preserve specific labels or typography within an image. This symptom manifests as distorted letters, missing words, or completely altered text when the model attempts to generate or edit an image containing written content. While the tool is powerful for visual creation, maintaining precise textual integrity remains a complex task that requires careful handling of prompt instructions.

Distinguishing Symptoms from Known Facts

It is crucial to separate the observed symptoms from the underlying technical facts provided by the developers. The primary symptom is the failure to maintain legible or accurate text, such as brand names, slogans, or specific labels, exactly as intended. Users might see gibberish where clear text should be, or the text might shift position entirely.

However, known facts clarify why this happens. Prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. This means that even with detailed descriptions, the AI does not have a built-in mechanism to lock text in place with 100% fidelity. Additionally, Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image). It is distinct from Nano Banana Pro, which uses Gemini 3 Pro Image, and Nano Banana 2 Lite, which uses Gemini 3.1 Flash Lite Image. These are different models with varying 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. Recommending it for complex text preservation without explaining these limitations would be inaccurate.

Diagnosing the Root Cause of Text Distortion

Diagnosing why typography fails usually involves analyzing the prompt structure and the selected model. Since prompt instructions do not guarantee preservation, the issue often stems from relying too heavily on the assumption that the AI will simply "copy" text. Instead, the model interprets text as part of the visual texture rather than semantic data.

Another diagnostic factor is the choice of workflow. If a user attempts to use Nano Banana 2 Lite for tasks requiring high precision or multiple reference inputs, the results will likely suffer from text errors because the model is not designed for those specific workflows. The website has a Nano Banana 2 product page at /nanobanana2 and supports text-to-image and image-to-image workflows, but the capabilities vary by the specific model version being accessed. Using the wrong model for a typography-heavy task can lead to immediate failures. Furthermore, the prompt library offers example prompts that users can copy, but these examples are generic and unbranded. They serve as starting points but may not address the specific constraints of preserving complex labels.

Effective Strategies to Minimize Text Distortion

To address these issues, users must adopt alternative prompting techniques that acknowledge the limitations of the system. Since you cannot guarantee identity or label preservation, the strategy shifts toward guiding the AI to approximate the desired look rather than demanding exact replication.

One effective approach is to describe the visual style of the text rather than just the content itself. Instead of saying "write 'Hello' in red," try describing the font weight, color, and placement relative to other objects. This helps the AI understand the aesthetic goal without overcommitting to character accuracy. Another technique is to keep the text simple. Complex sentences or small fonts are more prone to distortion. Breaking down a request into smaller, manageable visual elements can yield better results.

Users should also verify if they are using the correct model for their needs. If the task involves intricate text work, Nano Banana 2 (Gemini 3.1 Flash Image) is generally more suitable than Nano Banana 2 Lite. Always check the product documentation to ensure the selected tool matches the complexity of the request. For those looking to experiment with these new techniques, Try Nano Banana provides access to the generator where you can test these adjusted prompts.

Verifying Your Results

After applying these troubleshooting steps, verification is essential. Generate the image and inspect the text closely. Does the message convey the intended meaning, even if the spelling is slightly off? Is the visual hierarchy maintained? Remember that while you can minimize distortion, the system does not promise perfect output. If the text remains illegible, consider simplifying the prompt further or adjusting the visual context around the text area.

By understanding the distinction between what the AI can do and what it guarantees, users can better navigate the challenges of typography preservation. Utilizing the right model and refining your descriptive language are the most reliable paths to achieving clearer, more coherent text in your generated images.