Nano Banana 2 Lite: Avoiding Hallucinated Objects in Architectural Renders

When working with architectural visualization, precision is paramount. Users of Nano Banana 2 Lite (identified as Gemini 3.1 Flash Lite Image) often encounter a specific challenge where the generated image includes elements that contradict the intended design. These are known as hallucinations. In an architectural context, this might manifest as floating furniture, windows appearing on load-bearing walls, doors opening into solid concrete, or structural columns merging unexpectedly with floor plans.

The symptom is not merely a stylistic error but a fundamental breakdown in spatial logic. The model may generate a plausible-looking room, yet upon closer inspection, the geometry defies physics or the user's explicit description. For instance, a prompt asking for a "modern living room with a large window behind the sofa" might result in the sofa being placed in front of a wall, or the window being distorted into an abstract shape. This issue is particularly prevalent when the AI attempts to infer relationships between objects rather than following strict instructions.

Distinguishing Plausible Causes from Known Facts

To effectively troubleshoot these issues, it is crucial to separate what we know about the tool's capabilities from assumptions about its behavior. A common misconception is that all versions of the Nano Banana family perform identically regarding complex scene composition.

Known Facts:

• Model Identity: Google documents Nano Banana 2 Lite specifically as the Gemini 3.1 Flash Lite Image model. It is distinct from the standard Nano Banana 2 (Gemini 3.1 Flash Image) and Nano Banana Pro (Gemini 3 Pro Image).
• Optimization Focus: The official documentation states that Nano Banana 2 Lite is focused on speed and cost efficiency. Crucially, it is not optimized for multiple reference inputs or multi-turn sequential editing.
• Prompt Limitations: Prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. The model interprets text probabilistically rather than executing rigid code-like commands.

Plausible but Unverified Assumptions:

• It is often assumed that adding more descriptive words will automatically fix structural errors. However, without understanding the model's specific optimization for speed, verbose prompts may simply increase processing time without improving geometric accuracy.
• Users might assume that because the tool supports text-to-image, it can handle complex architectural blueprints as well as the Pro version. Since Nano Banana 2 Lite lacks optimization for multi-reference workflows, relying on it to interpret detailed technical drawings alongside text prompts is likely to yield inconsistent results.

Understanding that this specific model prioritizes rapid generation over high-fidelity structural adherence helps set realistic expectations. The hallucinations occur because the model fills in gaps based on general training data rather than strict spatial constraints provided in the prompt.

Diagnosing and Fixing Spatial Errors

Diagnosing the root cause usually involves analyzing the prompt's specificity regarding spatial relationships. If the output contains floating objects or merged structures, the diagnosis is often that the prompt failed to define the relationship between the subject and its environment clearly enough for the Lite model to resolve.

To fix these issues, users must adopt a strategy of extreme specificity. Instead of describing an object in isolation, you must explicitly define its position relative to other fixed elements. For example, rather than prompting for "a chair," use "a wooden chair positioned directly under the hanging lamp, touching the rug." This forces the model to calculate the intersection of objects before rendering them.

Since Nano Banana 2 Lite does not support multi-turn sequential editing effectively, you cannot easily correct one part of the image after generation without regenerating the whole scene. Therefore, the fix must happen entirely within the initial prompt construction. Break down the architectural scene into discrete, non-overlapping spatial zones. Describe the floor plane first, then place walls, then add furniture with explicit coordinates like "against the north wall" or "centered on the axis of the door."

Avoid relying on vague adjectives like "realistic" or "detailed" to solve structural problems. These terms influence texture and lighting but do not enforce geometry. Instead, focus on prepositions and locative phrases. Use negative constraints if the interface allows, such as "no floating objects" or "walls must be vertical," though remember that the model does not guarantee the preservation of these constraints.

Verifying Results and Managing Expectations

After adjusting your prompts to be hyper-specific about spatial relationships, verification becomes the final step. Generate the image and scrutinize the junctions where objects meet surfaces. Look for subtle distortions in perspective lines or misaligned edges. If the image still contains hallucinated objects, it indicates that the current prompt complexity exceeds the Lite model's capacity for maintaining structural integrity in a single pass.

In such cases, consider whether the task requires the higher fidelity of Nano Banana Pro or the standard Nano Banana 2, which may offer better handling of complex scenes. However, if speed and cost are the primary drivers, accepting minor imperfections or iterating through several variations with slightly different phrasing is the most viable path.

Remember that while the prompt library offers examples, they serve as inspiration and do not guarantee identical results for every user or scenario. The goal is to work with the model's limitations by providing the clearest possible map of the desired reality. By treating the prompt as a precise set of spatial instructions rather than a creative narrative, you can significantly reduce the frequency of structural hallucinations in your architectural renders.

For those ready to experiment with these refined techniques, Try Nano Banana to access the generator and apply these strategies to your own projects.

While no method guarantees perfect outcomes due to the probabilistic nature of AI generation, adhering to strict spatial definitions remains the most effective way to minimize errors in architectural visualization using Nano Banana 2 Lite.