Fixing Scrambled Text on Coffee Cups: Nano Banana 2 Typography Guide
Users frequently encounter a specific challenge when generating images of coffee cups with Nano Banana 2: the text labels appear distorted, scrambled, or completely illegible. This symptom manifests as letters merging together, characters flipping, or words becoming unrecognizable gibberish. While the visual composition of the cup, steam, and lighting may be perfect, the typography fails to render accurately. This is not a bug in the rendering engine but a known limitation of current AI image generation models regarding complex character placement.
It is crucial to separate plausible user expectations from verified technical facts. Many users assume that because the tool can generate realistic objects, it can also replicate specific brand logos or custom text with pixel-perfect accuracy. However, prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. The model generates pixels based on patterns rather than copying vector data. Therefore, seeing scrambled text is a standard behavior for this class of technology, not an error unique to your account or session.
Distinguishing Model Capabilities from User Expectations
To effectively troubleshoot this issue, one must understand the underlying architecture. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image). This distinction is vital because different models within the family have varying strengths. For instance, Nano Banana Pro is identified as Gemini 3 Pro Image, while Nano Banana 2 Lite is Gemini 3.1 Flash Lite Image.
A common mistake is attempting to use Nano Banana 2 Lite for tasks requiring high-fidelity text or multiple reference inputs. Google describes Nano Banana 2 Lite as focused on speed and cost. It is explicitly not optimized for multiple reference inputs or multi-turn sequential editing. Recommending it for complex typography workflows without explaining this limitation would be misleading. If you are using the Lite version, the likelihood of text distortion increases significantly compared to the standard Nano Banana 2 or Pro versions.
Furthermore, the website has a Nano Banana 2 product page at /nanobanana2 and supports text-to-image and image-to-image workflows. However, the existence of a Nano Banana Pro page at /nanobananapro or a page named Nano Banana Lite at /nanobananalite does not by itself establish support for identical features across all tiers. Google model names and capabilities must not be presented as proof of availability or identical features on this website. Users must verify which specific model they are invoking to ensure it aligns with their need for text accuracy.
Optimizing Prompts for Legible Branding
Since the model cannot guarantee typography preservation, the strategy shifts to minimizing errors through precise prompt engineering. Instead of demanding perfect text, focus on describing the layout and style. Use descriptive language to guide the visual arrangement rather than spelling out exact characters. For example, instead of writing "Write 'COFFEE' perfectly," try "A white ceramic coffee cup with a clean, centered rectangular label area containing stylized block letters."
The prompt library offers example prompts that users can copy or take into the generator. These examples often demonstrate how to structure requests for better results. When addressing the specific keyword of accurate typography placement, consider breaking the request into layers. Describe the cup material first, then the steam, and finally the label area. Label untested prompt examples as examples to manage expectations. Do not claim these will work every time, but they provide a structural baseline that reduces the probability of total scrambling.
For users seeking more robust handling of complex edits, exploring the broader ecosystem might help, though direct comparisons should be made carefully. You can explore the Try Nano Banana interface to test these variations. Remember that the goal is to create a convincing illusion of branding where the text looks intentional, even if the individual letters are slightly imperfect.
Verification and Final Workflow Adjustments
After applying new prompt structures, verification is the final step. Generate multiple variations of the same scene. Look for consistency in the label area. If the text remains illegible, adjust the complexity of the description. Reduce the number of words requested on the cup. Often, simpler text elements like single words or short phrases yield better results than long sentences.
It is important to avoid claims of guaranteed outcomes. No prompt can force the model to output perfect text if the underlying architecture prioritizes visual coherence over character recognition. If the result is still unsatisfactory, consider that the task may exceed the current optimization limits of the specific model version being used. In such cases, post-processing the image with external graphic design tools remains the most reliable method for achieving professional-grade typography on generated assets.
By understanding the distinction between the AI's generative nature and human expectations of text, users can better navigate the limitations of Nano Banana 2. Focus on the overall aesthetic and composition, treating the text as a visual element rather than a literal string of characters. This approach leads to more successful image generations that meet the intent of accurate typography placement within the constraints of the technology.