Fixing Broken Ligatures in Nano Banana 2 Decorative Scripts

When generating images with Nano Banana 2, users often encounter a specific visual artifact where decorative script fonts lose their intended flow. Instead of smooth, connected strokes typical of calligraphy or cursive styles, the letters appear disjointed. The connecting lines between characters, known as ligatures, are severed or missing entirely. This issue disrupts the aesthetic integrity of the text, making it look jagged rather than elegant. Understanding that prompt instructions describe desired outcomes without guaranteeing typography preservation is crucial when addressing this behavior.

Distinguishing Symptoms from Known Model Behaviors

It is important to separate the observed symptom from the underlying technical reality. The symptom is clear: decorative cursive connections are broken, resulting in isolated letterforms instead of a unified word. However, this does not necessarily indicate a software bug or a failure of the image generation engine itself. According to verified documentation, Google describes Nano Banana 2 as Gemini 3.1 Flash Image. While powerful, the model interprets prompt instructions as descriptions of visual scenes rather than strict typographic renderings.

Known facts state that prompt instructions do not guarantee identity, label, object, or typography preservation. Therefore, the breaking of ligatures is often a result of the model prioritizing overall image composition over precise character connectivity. This is distinct from a rendering error where pixels are missing due to corruption. In this case, the AI has likely interpreted the request for "cursive" or "script" as a stylistic suggestion rather than a structural requirement for continuous ink flow. Recognizing this distinction helps users avoid futile troubleshooting steps like restarting the application or checking file integrity, which will not resolve a generative interpretation issue.

Diagnosing the Cause Through Prompt Descriptors

The primary cause of broken ligatures lies in the specificity of the prompt descriptors used during generation. When a user requests a "decorative script font," the model may generate individual characters that resemble the style but fail to connect them physically. This happens because the model treats text generation as an artistic interpretation rather than a typesetting task. Without explicit guidance on continuity, the AI defaults to creating distinct glyphs.

To diagnose this, review the prompt for vague terms like "fancy writing" or "handwritten style." These broad descriptors lack the necessary constraints to enforce ligature formation. The diagnosis points to a need for more rigorous linguistic cues that emphasize connection. Users must understand that the tool is an image generator, not a word processor. Consequently, the solution requires adjusting the language to explicitly demand the physical joining of strokes. This involves moving beyond general style keywords to specific structural instructions that guide the model toward maintaining the integrity of the cursive flow.

Practical Fixes and Verification Strategies

To fix instances where decorative cursive connections are severed, you must refine your prompt descriptors to prioritize connectivity. Start by adding specific phrases such as "continuous ink flow," "unbroken ligatures," or "fully connected cursive strokes." Explicitly stating that the letters should be joined can help steer the model away from generating isolated characters. For example, instead of simply asking for "a sign with script text," try "a sign with fully connected cursive script where every letter flows into the next without gaps."

If the initial results still show breaks, consider iterating on the prompt with stronger emphasis on the visual continuity of the text. You might also experiment with different phrasing that describes the texture of the ink, such as "wet ink connecting the letters," which can sometimes reinforce the visual link between characters. It is essential to remember that these adjustments are examples of how to modify prompts; they do not guarantee a perfect outcome every time due to the probabilistic nature of the model.

After applying these changes, verify the output by closely inspecting the transitions between letters. Look specifically at the junction points where one stroke ends and another begins. If the ligatures remain broken, the prompt may still lack sufficient constraint, or the specific model variant being used may have limitations regarding complex typography. For workflows requiring high precision in multiple reference inputs or sequential editing, note that Nano Banana 2 Lite is focused on speed and cost and is not optimized for those tasks. Always ensure you are using the appropriate model version for your needs.

By carefully crafting prompts that explicitly demand connected strokes, users can significantly reduce the occurrence of broken ligatures. While the tool cannot guarantee typography preservation, strategic descriptor adjustments offer the best path to achieving the desired decorative effect. Try Nano Banana to experiment with these refined prompt techniques and observe how specific wording influences the final image structure.