Fixing Text Wrapping on Curved Mountain Signs in Nano Banana 2

When generating images of mountain signage or rock faces using Nano Banana 2, users often encounter a specific visual artifact where the text fails to wrap correctly along the intended curve. Instead of following the natural contour of the terrain, the letters may appear stretched, compressed, or floating above the surface. This issue is particularly common when the prompt attempts to simulate complex three-dimensional perspective on uneven geological features. The symptom manifests as illegible characters that do not adhere to the physical logic of the sign's placement, breaking the immersion of the generated scene.

It is important to distinguish between the tool's capabilities and user expectations regarding typography preservation. Prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. Consequently, expecting perfect text adherence to a highly irregular surface like a jagged mountain face without iterative adjustments is often unrealistic. The AI interprets the curvature as a geometric challenge rather than a strict typographic constraint, leading to the wrapping issues described.

Separating Plausible Causes from Known Facts

To effectively troubleshoot this problem, one must separate plausible causes based on user behavior from the known facts provided by the model documentation. A common assumption is that the model lacks the ability to render text on curves at all. However, the documented facts state that Google describes Nano Banana 2 as Gemini 3.1 Flash Image, which supports text-to-image workflows. The distortion is likely not a total failure of the engine but a result of conflicting instructions within the prompt.

Another plausible cause is the complexity of the prompt itself. When users request a "curved mountain sign" with detailed text wrapping simultaneously, the model may struggle to balance the structural integrity of the landscape with the precise alignment of the glyphs. It is crucial to note that prompt instructions are suggestions for the generator, not binding commands. Therefore, if the prompt is too dense with conflicting spatial requirements, the output will reflect the most dominant visual cue, often resulting in distorted text.

Known facts also clarify that different models have distinct optimizations. 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. If a user attempts to fix a complex text issue by uploading multiple reference images or engaging in long, multi-turn conversations, they might be using a version of the tool that cannot handle the necessary context retention. This limitation can exacerbate text wrapping errors because the model loses the thread of the original design intent during the generation process.

Simplifying Typography Prompts for Better Alignment

The primary strategy for resolving text wrapping issues involves simplifying the typography prompts. Instead of describing the exact path the text should take in minute detail, users should focus on the general orientation and the environment. For example, rather than commanding the AI to "wrap the word 'SUMMIT' perfectly around every ridge of the mountain," a more effective approach is to specify "a wooden sign mounted on a rocky cliff with the word SUMMIT visible." By reducing the specificity of the text path, the model can prioritize the overall composition and place the text in a way that naturally aligns with the surface geometry.

Users can explore the prompt library available on the website to see how other creators structure their requests. These example prompts offer a starting point that users can copy or adapt. Taking these examples into the generator allows users to observe how simplified language yields better results. It is essential to remember that these are untested prompt examples meant to illustrate concepts; they serve as a guide rather than a guaranteed solution for every unique image request.

Adjusting perspective parameters is another critical step. If the text appears to float or stretch, try modifying the camera angle description in the prompt. Describing a "slightly elevated view" or a "straight-on shot" can help the model understand the relationship between the viewer and the sign, often resulting in text that sits flush against the rock face. Avoid over-specifying the curvature unless it is a simple arc; complex topography requires simpler textual descriptions to achieve coherence.

Verifying Fixes and Selecting the Right Model

After implementing these changes, verification is key. Generate the image and inspect the text closely. Does it follow the general slope of the mountain? Is it legible? If the text still appears distorted, consider iterating with slight variations in the prompt wording rather than changing the entire concept. The goal is to find a balance where the text enhances the scene without fighting the background geometry.

If the initial attempts fail, verify that you are using the correct model version. As noted in the documentation, Nano Banana 2 corresponds to Gemini 3.1 Flash Image. Using Nano Banana 2 Lite for complex tasks involving detailed text and perspective might lead to suboptimal results due to its optimization for speed rather than precision. Ensure you are accessing the standard Nano Banana 2 workflow to maximize your chances of success.

For those ready to experiment with these troubleshooting techniques, you can Try Nano Banana directly. Remember that while these steps significantly improve the likelihood of successful text rendering, the AI does not guarantee specific outcomes. Patience and iterative refinement remain the most reliable tools for mastering text curvature in AI-generated imagery.