Fixing Hallucinated Windows in Nano Banana 2 Facade Generations

When generating building facades with Nano Banana 2, users may occasionally encounter visual anomalies where windows appear as non-existent shapes, float randomly across the surface, or fail to align with the architectural grid. This phenomenon, often referred to as window hallucination, occurs when the model interprets vague structural descriptions as organic textures rather than rigid geometric elements. It is important to distinguish between a genuine rendering error and a prompt ambiguity. Known facts indicate that while Nano Banana 2 supports text-to-image workflows, prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. Consequently, if a prompt lacks specific spatial constraints, the model may generate plausible-looking but structurally incorrect window placements.

To address this, we must first separate plausible causes from verified limitations. A common misconception is that the model simply fails to render windows due to a bug. However, the underlying technology, identified by Google as Gemini 3.1 Flash Image for Nano Banana 2, relies heavily on the specificity of the input text. If the description focuses too much on the material (e.g., "brick wall") without defining the fenestration pattern, the AI fills the voids with random artifacts. Another factor is the distinction between model variants. While Nano Banana Pro utilizes Gemini 3 Pro Image, Nano Banana 2 Lite is focused on speed and cost and is not optimized for complex multi-turn sequential editing or multiple reference inputs. Using the Lite version for highly detailed architectural grids without understanding these limitations can exacerbate alignment issues.

Defining Grid Patterns and Spacing Constraints

The most effective method to prevent window hallucinations is to explicitly define the geometry within the prompt. Instead of asking for a "building with windows," which allows the model to interpret the number and placement freely, users should specify a grid pattern. For example, instructing the generator to create a "symmetrical 4x5 grid of rectangular windows" provides a mathematical framework that the model can follow more strictly. This approach minimizes random architectural artifacts by reducing the degrees of freedom the AI has during generation.

Spacing is equally critical. Ambiguity in distance between openings often leads to merging or floating elements. Users should include precise descriptors such as "uniform vertical spacing" or "consistent horizontal intervals." By treating the facade as a structured layout rather than a freeform texture, the output becomes more predictable. It is worth noting that while the prompt library offers example prompts that users can copy, these examples are generic and unbranded. They serve as starting points but may need customization for specific architectural styles. When adapting these examples, ensure that the language remains descriptive of the outcome rather than assuming the model will preserve specific labels or exact dimensions automatically.

Selecting the Appropriate Model Variant

Choosing the right tool within the Nano Banana ecosystem is vital for troubleshooting complex structural issues. As documented by Google, Nano Banana 2 corresponds to Gemini 3.1 Flash Image, whereas Nano Banana Pro uses Gemini 3 Pro Image. The Pro variant generally offers higher fidelity for intricate details, which can be beneficial for maintaining strict window alignments. Conversely, Nano Banana 2 Lite is designed for speed and cost efficiency. It is not optimized for multiple reference inputs or multi-turn sequential editing. Therefore, relying on the Lite version for a task requiring high precision in facade generation, especially when correcting previous errors through iteration, may yield suboptimal results.

Users should avoid assuming that all versions on the website offer identical capabilities. The presence of a page named Nano Banana Lite does not establish support for Google Nano Banana 2 Lite features if the underlying implementation differs. Always verify the model being used against the specific requirements of the task. For tasks involving strict geometric adherence like window placement, the standard Nano Banana 2 or Pro models are typically more reliable than the Lite variant, provided the prompt constraints are sufficiently tight.

Verifying and Refining Your Output

After generating an image, verification involves checking for consistency in the window grid. Look for any instances where windows appear distorted, partially cut off, or misaligned with the building's edges. If hallucinations persist, refine the prompt by adding negative constraints or increasing the density of positive geometric descriptors. Remember that prompt instructions describe desired outcomes; they do not guarantee identity or perfect preservation. Iterative refinement is key. You might try rephrasing the request to emphasize "rigid structure" or "orthogonal lines" to steer the model away from organic distortions.

For those looking to experiment with these techniques immediately, you can Try Nano Banana to test different prompt structures. By combining clear geometric definitions with the appropriate model selection, users can significantly reduce the frequency of non-existent or misaligned windows in their facade generations. This systematic approach ensures that the generated images remain faithful to the intended architectural design without relying on chance.