Fixing Inconsistent Label Placement in Nano Banana 2 Text-to-Image Mockups

When generating product packaging mockups using text-to-image workflows, users often encounter a specific set of visual inconsistencies. The primary symptom is the appearance of gibberish text or nonsensical character clusters where brand names or labels should appear. Instead of clear, legible typography, the generated image may display squiggly lines, random symbols, or distorted letter shapes that vaguely resemble words but are unreadable. Additionally, the placement of these graphical elements can be erratic. A label might appear tilted, partially cut off at the edge of the container, or floating in mid-air rather than adhering to the curvature of the bottle or box.

This behavior is particularly frustrating when creating professional marketing assets where brand identity must remain intact. Users expect the AI to render the exact text provided in the prompt onto the physical surface of the product. However, the reality of current generative capabilities means that while the tool excels at creating realistic textures, lighting, and general composition, it does not guarantee the precise rendering of specific text strings or their perfect geometric alignment on complex surfaces. Recognizing this limitation is the first step toward finding a reliable workflow solution.

Separating Plausible Causes from Known Facts

It is easy to assume that inconsistent label placement stems from a bug in the software or an error in the user's prompt construction. While poor prompting can influence the overall quality of an image, the core issue here is rooted in the fundamental architecture of the underlying models. It is crucial to distinguish between what the tool attempts to do and what it is technically capable of guaranteeing.

The known facts regarding the Nano Banana 2 system clarify that prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. This means that even with highly detailed prompts specifying font styles, exact wording, and precise positioning, the model may still hallucinate text or fail to align it perfectly with the 3D geometry of the mockup. The tool is designed to generate images based on patterns learned from vast datasets, not to act as a vector graphics editor that enforces strict typographic rules.

Furthermore, there is a distinction between the different models available. Google documents Nano Banana 2 as Gemini 3.1 Flash Image, while Nano Banana Pro corresponds to Gemini 3 Pro Image. These are distinct models with varying capabilities. 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 complex labeling tasks without understanding these limitations can exacerbate alignment issues. Therefore, the inconsistency is not necessarily a failure of the user but a reflection of the probabilistic nature of the generation process.

Diagnosing the Workflow Limitations

To diagnose why your labels are failing, you must evaluate the constraints of the text-to-image workflow itself. The diagnosis reveals that the AI interprets text within a prompt as a visual concept rather than a string of characters to be printed. When the model tries to "draw" the word "Brand Name," it generates pixels that look like letters based on its training data, rather than retrieving and placing actual text glyphs. This leads to the gibberish effect when the model cannot confidently reconstruct the specific shape of the requested word.

Additionally, the spatial relationship between the text and the object surface is handled through artistic approximation. The model estimates where a label should go based on perspective cues, but it lacks the precision to ensure the text wraps perfectly around a cylindrical bottle or sits flush against a flat box corner. This results in the misalignment symptoms observed. Since the tool does not guarantee typography preservation, expecting pixel-perfect text rendering directly from the generator is setting up for disappointment. The diagnostic conclusion is that direct text generation within the image pipeline is inherently unreliable for branding purposes.

Fixing Alignment with External Overlays

Since the internal generation process cannot guarantee accurate text rendering, the most effective fix involves a two-step workflow that separates image generation from text application. Instead of relying on the AI to write the label, use Nano Banana 2 to generate the high-quality base image of the unbranded or generic product packaging. Once you have a clean mockup with the correct lighting, texture, and perspective, apply your branding externally.

This approach ensures that your typography remains crisp, legible, and perfectly aligned. You can use standard graphic design software to overlay your logo and text onto the generated image. By doing so, you maintain full control over the font choice, kerning, and placement. The AI handles the heavy lifting of creating the realistic environment and product form, while your design tools handle the precise branding requirements. This hybrid method bypasses the AI's inability to preserve specific text identities.

For users looking to explore the capabilities of the platform before applying this workflow, Try Nano Banana offers access to the text-to-image features needed to create the base mockups. Remember that while the prompt library provides examples to guide your creative direction, these examples serve as inspiration and do not guarantee that specific text will be rendered correctly in the output.

Verifying Your Results

After implementing the external overlay strategy, verification becomes straightforward. Inspect the final composite image to ensure the text is sharp and free of the distortion seen in direct generation. Check that the label follows the natural curvature of the product if necessary, though modern design tools allow for warping effects to achieve this seamlessly. Compare the new result against your original goal: a professional-looking mockup with accurate branding.

If you notice any residual artifacts from the base image generation, such as shadows that don't match the added text, you may need to adjust the lighting in your external editor. However, the primary issue of gibberish text and misalignment should be resolved by removing the text generation step from the AI pipeline entirely. By accepting the tool's strengths in visual creation and its limitations in typography, you can consistently produce high-fidelity mockups that meet professional standards without relying on unguaranteed AI outputs.