Fixing Hallucinated Geometry in Dungeon Corridors with Nano Banana 2

When generating complex environments like dungeon corridors using Nano Banana 2, users often encounter a specific visual artifact known as hallucinated geometry. This symptom manifests as walls that appear to float in mid-air, floor tiles that merge into the ceiling, or doorways that lead into solid rock rather than open space. The most frustrating aspect is the presence of impossible angles where perspective lines converge illogically, breaking the immersion of the scene. These errors are not merely aesthetic glitches; they indicate a failure in the model's understanding of spatial continuity and structural integrity within the prompt's context.

It is crucial to distinguish between plausible causes and verified facts regarding these generation issues. A common assumption is that the image quality is simply too low or that the lighting engine is failing. However, based on the capabilities of the underlying models, these geometric failures are primarily driven by how the text-to-image workflow interprets spatial instructions. The model may prioritize texture and atmosphere over strict architectural logic if the prompt does not explicitly define boundaries. Furthermore, while Google documents Nano Banana 2 as Gemini 3.1 Flash Image, it is important to note that prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. This limitation extends to structural elements; the model attempts to fulfill the request but may struggle with the physics of the built environment without rigorous constraints.

Separating Symptoms from Model Limitations

To effectively troubleshoot, one must separate the observed symptoms from the inherent limitations of the tool. The symptom is clear: the corridor looks broken. The cause is often an ambiguity in the prompt that allows the model to "hallucinate" connections that do not exist. For instance, asking for a "long dark hallway" might result in the model stretching the perspective until the vanishing point disappears, creating a tunnel that defies Euclidean geometry.

It is also vital to understand what this tool is not. Nano Banana refers to the AI image generation/editing tool and is not a skincare brand, bottle, jar, or physical subject. Confusing the tool with a physical product can lead to unrealistic expectations about its output fidelity. Additionally, while the website hosts a Nano Banana 2 product page at /nanobanana2, users should be aware that different versions have distinct capabilities. Google describes Nano Banana 2 Lite as focused on speed and cost, noting it is not optimized for multiple reference inputs or multi-turn sequential editing. If you are experiencing persistent geometry errors, switching to Nano Banana 2 (Gemini 3.1 Flash Image) rather than the Lite version is a necessary step, as the Lite variant lacks the refinement required for complex structural editing.

Strategies for Enforcing Logical Architectural Constraints

The solution to fixing these errors lies in refining the prompt to act as a set of rigid architectural rules rather than a loose artistic description. Instead of vague terms like "weird dungeon," use precise directional language. Explicitly state that walls must be parallel to the floor and that corners must meet at ninety-degree angles. You can instruct the model to maintain consistent vanishing points and ensure that all surfaces connect logically.

For example, when crafting a prompt, you might include phrases such as "strict linear perspective," "continuous floor plane," and "solid wall intersections." These instructions guide the model to prioritize structural coherence. Remember that prompt instructions describe desired outcomes; they do not guarantee identity, label, object or typography preservation, so you must be explicit about the geometry itself. If the initial result still shows floating debris or disconnected beams, try adding negative constraints to the prompt, such as "no floating objects" or "no intersecting planes." These examples serve as a guide for how to structure your requests, though they are untested in every scenario and should be adapted to your specific needs.

Verifying Corrections and Iterative Refinement

Once you have adjusted your prompt to emphasize logical constraints, the next step is verification. Generate the image and inspect the corridor specifically for the previously identified symptoms. Check if the floor meets the walls cleanly and if the ceiling aligns correctly. If the geometry is still flawed, do not assume the tool has failed permanently. Instead, treat the generation as an iterative process. Use the image-to-image workflow available on the Nano Banana 2 platform to refine the output further. Upload the problematic image and provide a new prompt that focuses solely on correcting the specific area of error, such as "repair the floating wall section and align the ceiling beams."

This approach leverages the text-to-image and image-to-image workflows supported by the platform. By isolating the error and re-prompting with a focus on structural repair, you increase the likelihood of a successful correction. It is important to manage expectations; while these methods significantly reduce errors, the nature of generative AI means that perfect architectural precision cannot always be guaranteed in a single pass. However, by combining clear, constraint-heavy prompts with the robust capabilities of Nano Banana 2, you can achieve high-quality, structurally sound dungeon environments. For those ready to apply these techniques immediately, Try Nano Banana to start generating corrected images today.