Nano Banana 2 Troubleshooting: Text Preservation in Illustrated Postcards

When creating illustrated travel postcards with Nano Banana 2, users often encounter a frustrating issue where specific typography, labels, or handwritten notes do not appear exactly as intended. Instead of preserving the original text from an uploaded reference image or generating new text that matches a requested style, the AI might distort the letters, omit them entirely, or replace them with gibberish. This behavior is particularly common when working with complex backgrounds or detailed artistic styles typical of vintage postcards.

It is crucial to understand that this limitation stems from the fundamental design of the underlying technology. While Nano Banana 2 excels at generating cohesive visual narratives and blending artistic elements, it does not treat text as a fixed, immutable object. The system prioritizes aesthetic harmony and visual flow over strict character-level fidelity. Consequently, even if a user provides a clear instruction to keep a specific label intact, the model interprets this as a stylistic suggestion rather than a hard constraint. Users should note that prompt instructions do not guarantee identity, label, object, or typography preservation in this model.

Distinguishing Plausible Causes from Known Facts

To effectively troubleshoot these issues, one must separate intuitive assumptions about how AI works from the verified capabilities of the tool. A common misconception is that providing a high-resolution reference image with clear text will force the AI to copy that text pixel-for-pixel. While this approach often yields better results than vague prompts, it is not a guaranteed solution. The known facts indicate that prompt instructions describe desired outcomes but do not guarantee the preservation of specific textual elements.

Another plausible cause users might suspect is a glitch in the rendering engine or a temporary server error. However, there is no evidence suggesting that text loss is caused by technical instability or bugs in the current version. Instead, the issue is inherent to the model's architecture. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image), which is optimized for speed and creative generation rather than precise OCR-style text replication. When the model processes an image-to-image workflow, it re-interprets the visual data based on its training, which often leads to the modification of existing text to fit the new composition.

Furthermore, users sometimes confuse the capabilities of different tiers within the Nano Banana family. For instance, Nano Banana 2 Lite is focused on speed and cost and is explicitly not optimized for multiple reference inputs or multi-turn sequential editing. Relying on Lite versions for tasks requiring strict text adherence would likely exacerbate the problem, as these models lack the specialized context windows needed for such precision. It is important to remember that while the website has pages for Nano Banana Pro and Lite, their features are distinct and defined by Google's model specifications, not just by the product page titles.

Diagnosing the Root Cause of Typography Loss

Diagnosing why your postcard text is disappearing involves analyzing the interaction between your prompt and the model's interpretation of visual data. If you are using a text-to-image workflow, the diagnosis is straightforward: the model is generating text based on probability distributions, not copying a source. Even with a strong prompt like "write 'Paris' in cursive," the output is a probabilistic guess of what cursive looks like, not a transcription of the word "Paris."

In image-to-image scenarios, the diagnosis becomes more nuanced. If you upload a postcard with a visible stamp or address and ask the AI to redraw the scene while keeping the stamp, the model may view the stamp as part of the background texture rather than a distinct information layer. The AI attempts to blend the stamp into the new lighting or perspective, resulting in distortion. This happens because the model treats all pixels equally unless explicitly guided otherwise, and even then, the guidance is a preference, not a rule. The core issue is that the tool is designed to create art, not to function as a document editor or a text-recognition utility.

If you find that the text is consistently garbled regardless of the prompt complexity, the root cause is almost certainly the model's inability to lock onto specific glyphs. This is a known limitation of the Gemini 3.1 Flash Image architecture used by Nano Banana 2. It is not a failure of your input quality or internet connection. The model simply lacks the mechanism to enforce exact character retention across generative transformations.

Practical Workarounds and Verification Steps

Since direct preservation cannot be guaranteed, the most effective strategy is to adjust your workflow to accommodate the model's strengths. One approach is to generate the illustration first without any text, ensuring the composition and style are perfect. Once the image is finalized, add the necessary typography using external graphic design software. This separates the generative process from the typographic requirement, bypassing the AI's limitations entirely.

If you must attempt to generate text within the tool, try simplifying your request. Use short, common words and standard fonts in your prompt description. Avoid complex scripts or highly stylized handwriting unless you are willing to accept variations. You can also experiment with the prompt library provided on the site, taking example prompts as inspiration but modifying them to focus on the visual style rather than the text content itself. Remember that these examples are untested for specific text preservation and serve only as starting points for creativity.

For users who require higher fidelity in text handling, consider exploring other tools within the ecosystem, though availability varies. Google describes Nano Banana Pro as Gemini 3 Pro Image, which may offer different capabilities, but even this does not promise perfect text locking. Always verify your results immediately after generation. Check the output for legibility and accuracy before proceeding to further edits. If the text is incorrect, regenerate with a modified prompt or switch to the manual addition method.

While Nano Banana 2 is a powerful tool for artistic exploration, it is essential to manage expectations regarding text. By understanding that prompt instructions do not guarantee identity or typography preservation, you can avoid frustration and adopt workflows that leverage the AI's true potential. For those ready to explore the full range of creative possibilities, Try Nano Banana to start generating your next masterpiece.

Ultimately, successful postcard creation with Nano Banana 2 requires a hybrid approach. Use the AI for its unparalleled ability to visualize scenes and apply artistic filters, but rely on traditional design tools for the final touch of accurate typography. This combination ensures your travel postcards look professional and convey the correct message without compromising on the artistic vision.