Fixing Distorted Caps on Bottle Images in Nano Banana 2

When working with image-to-image workflows in Nano Banana, users often encounter specific geometric anomalies during scaling operations. A common symptom involves the cap of a bottle becoming misshapen, stretched, or completely disconnected from the neck as the image dimensions change. This issue typically arises because the AI model struggles to maintain the rigid structural relationship between the container body and its closure when the aspect ratio or scale is altered significantly. It is important to clarify that Nano Banana refers strictly to the AI image generation and editing tool; it is not a skincare brand, nor does it produce physical bottles, jars, or cosmetic products. The visual artifacts described here are digital rendering challenges inherent to the generative process.

Understanding the Symptom and Known Facts

The primary symptom of this issue is a loss of structural coherence in the upper portion of the generated object. As you attempt to scale an image of a bottle up or down, the cap may appear to melt into the neck, float above it, or take on an irregular, non-cylindrical shape. This is distinct from general blurriness or texture loss; the geometry itself is compromised.

It is a known fact that prompt instructions describe desired outcomes but do not guarantee the preservation of identity, labels, objects, or typography. Therefore, expecting the AI to perfectly retain the exact geometric proportions of a complex object like a bottle cap without additional guidance can lead to these distortions. Furthermore, while Google documents Nano Banana 2 as Gemini 3.1 Flash Image, Nano Banana Pro as Gemini 3 Pro Image, and Nano Banana 2 Lite as Gemini 3.1 Flash Lite Image, these are distinct models with different capabilities. Specifically, Nano Banana 2 Lite is focused on speed and cost and is not optimized for multiple reference inputs or multi-turn sequential editing. Users attempting complex scaling tasks should be aware that using the Lite version may exacerbate structural issues due to these limitations.

Separating Plausible Causes from Verified Limitations

To effectively troubleshoot, one must separate plausible user errors from the verified technical constraints of the platform. A common misconception is that simply adding more descriptive text about the "shape" of the cap will fix the geometry. While prompts guide the output, they do not guarantee object preservation. Another plausible cause is the selection of an inappropriate model for the task. If a user attempts a complex scaling operation requiring high fidelity on structural details using Nano Banana 2 Lite, the result is likely to be poor because that model lacks the optimization for such detailed reference handling.

However, the verified limitation lies in how the model interprets scaling transformations. Without explicit weighting of reference elements, the model treats the image as a canvas to be reinterpreted rather than a rigid structure to be transformed. The AI may prioritize the overall aesthetic or new composition over the strict mechanical connection between the cap and the neck. This is why generic scaling often results in the cap appearing detached or warped. It is crucial to remember that no external links or third-party tools are required to solve this; the solution lies within the native parameters of the Nano Banana interface.

Diagnosing and Fixing the Issue via Reference Weights

The diagnosis for distorted cap geometry usually points to insufficient emphasis on the reference image's structural anchors. To fix this, you must adjust the reference weights to prioritize the structural integrity of the bottle components. Instead of relying solely on the visual input, explicitly instruct the model to maintain the connection between the neck and the cap. You can use example prompts to test this approach, though please note that these are examples and do not guarantee specific results.

Try modifying your prompt to include specific directives about maintaining the "rigid connection" or "structural alignment" of the cap relative to the bottle neck. In the settings, if available, increase the weight assigned to the reference image to ensure the AI adheres closer to the original geometry. If you are currently using Nano Banana 2 Lite, consider switching to Nano Banana 2 or Nano Banana Pro, as the Lite version is not optimized for the multi-step precision required for this type of editing. By balancing the prompt instructions with higher reference weights, you signal to the model that the cap's position and shape are critical constraints, not optional artistic interpretations.

Verifying the Solution

After applying these adjustments, verify the outcome by generating a few variations of the scaled image. Look specifically for the seam where the cap meets the neck. If the geometry is restored, the cap should sit flush and maintain its intended cylindrical or defined shape without floating or melting. If the distortion persists, try reducing the scale factor incrementally rather than making large jumps, as extreme scaling increases the difficulty for the model to preserve fine details. Remember that while these steps address the most common causes, AI generation involves probabilistic outcomes, so slight variations may still occur. For further exploration of the tool's capabilities, you can Try Nano Banana to experiment with these settings directly.

By understanding the distinction between the tool and physical products, recognizing the limitations of different model versions, and correctly applying reference weights, you can significantly reduce cap distortion issues. This approach ensures that your bottle images remain structurally sound and visually coherent during any scaling operation.