Mastering Nano Banana 2: Prompt Structure for Removing Complex Reflections

When editing images of glass windows, metallic car bodies, or polished jewelry, reflections often obscure the true subject. Users frequently struggle to remove these distracting layers while keeping the underlying shape intact. This is a common challenge in image-to-image workflows where the AI must distinguish between surface glare and actual object features. Nano Banana 2 offers a robust environment for this task, but success relies heavily on how you structure your text instructions. The tool does not automatically know which pixels are reflections and which are part of the object; it requires explicit guidance to separate these visual elements.

Distinguishing Symptoms from Plausible Causes

Before attempting a fix, it is crucial to understand what is happening during the generation process. The primary symptom users report is that when they ask Nano Banana 2 to "remove the reflection," the resulting image either retains the glare, distorts the object's geometry (making a straight window look curved), or invents new details that were not present in the original scene.

It is important to separate plausible user assumptions from known facts about the model's behavior. A common assumption is that simply stating "no reflection" will work universally. However, prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. Therefore, a vague command often leads the model to hallucinate a new background or warp the physical structure of the item to satisfy the request. Another plausible cause for failure is using the wrong model variant. For instance, Google describes Nano Banana 2 Lite as focused on speed and cost. It is not optimized for multiple reference inputs or multi-turn sequential editing. If a user attempts complex layer isolation on Nano Banana 2 Lite, the result may be inconsistent because the model lacks the capacity for the nuanced reasoning required for reflection removal.

Diagnosing the Prompt Structure

To diagnose why a previous attempt failed, analyze the specificity of your input. If the prompt was generic, such as "clean up the glass," the model likely treated the entire surface as a single texture to be smoothed. The diagnosis here is a lack of layer separation in the instruction set. The model needs to understand that the image contains two distinct conceptual layers: the physical object underneath and the optical phenomenon of the reflection on top.

The correct approach involves an image-to-image workflow where you upload the source image and provide a prompt that explicitly isolates the reflection layer. You must instruct the AI to ignore the specular highlights while reconstructing the occluded geometry. This requires a structured prompt that defines the target state rather than just describing the problem. For example, instead of saying "remove the reflection," a more effective structure would be "isolate the reflection layer and replace it with the visible background behind the glass, preserving the frame geometry." This directs the model to perform a specific reconstruction task rather than a general cleanup.

Implementing the Fix and Verifying Results

To fix the issue, construct your prompt with a focus on geometric preservation and layer isolation. Start by identifying the material properties in the prompt, such as "polished steel" or "transparent glass." Then, explicitly state the action regarding the reflection: "remove the sky reflection from the surface" or "eliminate the light glare on the metal." Crucially, add a constraint clause to protect the object's form, such as "maintain the original curvature of the bottle" or "keep the window frame straight."

After generating the image, verify the results by checking for three key indicators: first, ensure the reflection is gone or significantly reduced; second, confirm that the underlying object has not been warped or stretched; and third, check if the background behind the glass looks natural and consistent with the scene. If the geometry is distorted, refine the prompt to emphasize structural integrity more strongly. Remember that Nano Banana refers to the AI image generation/editing tool in these articles. It is not a skincare brand, bottle, jar or physical subject. Example products are generic and unbranded. If the initial result is unsatisfactory, try adjusting the emphasis on the "background visibility" aspect of the prompt.

For those looking to experiment with these advanced prompting techniques, you can access the necessary tools directly through the platform. Try Nano Banana to start your image-to-image workflow. By carefully structuring your prompts to separate reflection layers from object geometry, you can achieve cleaner edits that respect the physical reality of the photographed subject. While prompt instructions describe desired outcomes, they do not guarantee identity, label, object or typography preservation, so always review the output critically before finalizing your edit.