Fixing Inconsistent Scale in Nano Banana 2: Foreground vs Background

When generating images with Nano Banana 2, users may encounter a common visual artifact where foreground objects appear disproportionately large or small compared to distant landmarks. This inconsistency disrupts the sense of realism and breaks the viewer's immersion. The symptom is often described as a "floating" object that does not feel anchored to the ground plane, or a background that seems unnaturally close despite being described as distant. This issue stems from how the model interprets spatial relationships within the text prompt rather than a failure of the rendering engine itself.

It is important to distinguish between plausible causes and known facts regarding this behavior. While some users might suspect a bug in the image generation pipeline, the documented behavior indicates that prompt instructions describe desired outcomes without guaranteeing specific identity, label, or spatial preservation. The model relies heavily on the clarity of depth cues provided in the input. If the prompt lacks explicit markers for distance, size, or perspective, the AI may prioritize the most prominent subject (the foreground) at the expense of accurate scaling relative to the environment.

Separating Symptoms from Model Limitations

To effectively troubleshoot this issue, one must first separate the observed symptom from the underlying mechanics. The symptom is the visual mismatch: a car that looks like a toy next to a mountain, or a person who appears giant compared to a city skyline. This is not necessarily a defect in the Nano Banana 2 tool, but rather a reflection of how text-to-image models interpret ambiguous spatial data.

Known facts about the system clarify that Nano Banana 2 supports both text-to-image and image-to-image workflows. However, the prompt library offers example prompts that serve as starting points; they do not guarantee perfect adherence to complex spatial constraints. When a user requests a scene with multiple depth layers, the model attempts to balance these elements based on token weighting and semantic association. If the description of the background is vague, the model may default to placing it closer than intended to ensure it remains visible, resulting in a compressed scale.

Furthermore, while Google documents Nano Banana 2 as Gemini 3.1 Flash Image, it is distinct from other variants like Nano Banana Pro or Nano Banana 2 Lite. Users should be aware that Nano Banana 2 Lite is focused on speed and cost and is not optimized for multiple reference inputs or multi-turn sequential editing. Attempting to fix complex scale issues using the Lite version without understanding these limitations could lead to further inconsistencies. For precise control over scale, the standard Nano Banana 2 workflow is generally more robust, though results are never guaranteed.

Refining Depth Cues for Accurate Spatial Scale

The primary solution to inconsistent scale lies in refining the depth cues within your prompt. To establish proper spatial scale, you must explicitly define the relationship between objects rather than assuming the model will infer it. Instead of simply listing objects, describe their relative positions and sizes using comparative language.

For instance, rather than prompting for "a dog in front of a castle," try "a small dog standing on grass in the immediate foreground, with a massive stone castle appearing tiny and far away in the background due to atmospheric perspective." By introducing terms like "immediate foreground," "distant horizon," and "atmospheric perspective," you provide the model with stronger directional signals. These cues help the algorithm understand that the castle is meant to be far away, which naturally forces it to render smaller relative to the dog.

Another effective strategy is to anchor the scene with environmental context. Mentioning elements like "fog obscuring the base of the mountains" or "shadows stretching across the field" adds physical weight to the scene. These details force the model to calculate light and shadow interactions, which inherently requires a consistent scale. If the shadows do not match the object sizes, the image will look incorrect, so the model adjusts the scale to maintain logical consistency in lighting.

Users can also utilize the prompt library examples found on the website as a reference for structure. Copying successful patterns that emphasize depth can provide a template for your own prompts. Remember that prompt instructions describe desired outcomes; they do not guarantee identity or typography preservation, so flexibility is key. If the first attempt fails, iterate by adding more specific descriptors for the background layer, such as "faint outlines of trees" or "hazy blue sky," to push the background further into the distance.

Verifying Fixes and Iterative Adjustments

Once you have adjusted your prompt to include stronger depth cues, verify the output by checking the alignment of shadows, occlusion, and relative sizes. Does the foreground object cast a shadow that interacts correctly with the ground? Is the background element clearly separated by a sense of distance? If the scale still feels off, consider breaking the prompt into two parts: one focusing on the foreground subject and another on the background environment, then combining them with clear transition words.

It is crucial to avoid claims of guaranteed outcomes. Even with refined prompts, AI generation involves probabilistic processes. If the result remains inconsistent, try simplifying the scene to fewer elements to see if the scale stabilizes. Complex scenes with many interacting objects increase the likelihood of spatial errors. Once a satisfactory image is generated, you can use the image-to-image workflow to refine details further, ensuring the scale remains consistent through iterations.

For those seeking to explore these capabilities further, Try Nano Banana to experiment with different prompt structures and observe how varying depth cues affect the final composition. By treating the prompt as a set of spatial instructions rather than just a list of objects, you can significantly improve the consistency of scale between foreground and background elements in your generated images.