Fixing Inconsistent Object Scale in Nano Banana 2 Backgrounds

When using the Nano Banana 2 image generation tool, users may encounter a specific visual artifact where objects within the background appear disproportionately large or small compared to the primary subject in the foreground. This inconsistency disrupts the sense of depth and realism, making the scene look unnatural. The core symptom involves a mismatch in perspective: a distant tree might loom larger than a person standing nearby, or a background building might dwarf the entire composition. This issue typically arises during workflows that combine distinct elements or rely on complex spatial descriptions.

It is crucial to distinguish between known facts about the tool's capabilities and potential user errors. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image). While this model supports text-to-image and image-to-image workflows, prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. Consequently, the AI interprets spatial relationships based on the statistical likelihood of word associations rather than a strict understanding of physical laws. If the prompt lacks explicit constraints regarding size ratios, the model may prioritize semantic relevance over geometric accuracy, leading to the observed scaling errors.

Separating Plausible Causes from Model Limitations

To effectively troubleshoot this issue, one must separate plausible causes rooted in prompt ambiguity from the inherent limitations of the underlying technology. A common misconception is that the model automatically calculates correct perspective based on keywords like "background" or "foreground." However, without explicit definitions of relative distances and sizes, the AI generates content based on general training data patterns. For instance, if a prompt asks for a "large mountain behind a small house," the model might interpret "large" and "small" as independent descriptors rather than relative measurements, resulting in a mountain that is physically impossible in relation to the house.

Another factor to consider is the distinction between different model versions available through the platform. Google describes Nano Banana 2 Lite as focused on speed and cost. It is explicitly noted that this version is not optimized for multiple reference inputs or multi-turn sequential editing. Users attempting complex compositional tasks with Nano Banana 2 Lite may face more frequent scaling inconsistencies because the model prioritizes rapid generation over nuanced spatial reasoning. Therefore, while the issue can occur across versions, it is often more pronounced in the Lite variant when handling detailed scene compositions.

It is also important to note that the website hosts a Nano Banana Pro page at /nanobananapro, which corresponds to Gemini 3 Pro Image (gemini-3-pro-image). While Pro models generally offer higher fidelity, they still operate under the same fundamental rule: prompt instructions do not guarantee object preservation or perfect adherence to physical scale. Users should not assume that switching to a Pro model will automatically resolve all scaling issues without adjusting the input text.

Crafting Prompts for Accurate Relative Scaling

The most effective method to fix inconsistent object scale is to refine the prompt to explicitly define relative distances and sizes. Instead of relying on vague adjectives, users should employ comparative language that anchors the size of background elements to the foreground subject. For example, rather than saying "a giant castle in the back," a more precise instruction would be "a castle in the far distance that appears tiny compared to the knight in the foreground." This approach forces the model to establish a spatial hierarchy before rendering the details.

Users can utilize the prompt library provided on the website to find example prompts that demonstrate these techniques. These examples serve as templates for structuring requests that emphasize depth. When modifying an existing prompt, add clauses that specify the focal length or camera angle, such as "wide-angle shot" or "telephoto lens," as these terms influence how the AI compresses or expands space. Remember that these are untested prompt examples intended to illustrate the concept; they are not guaranteed to produce identical results in every generation.

For complex scenes involving multiple layers, break the description down into sequential steps. First, define the main subject and its immediate surroundings. Then, describe the mid-ground elements with specific size references relative to the subject. Finally, detail the background with clear indicators of distance, such as "fading into the horizon" or "appearing significantly smaller due to perspective." By layering these instructions, you provide the model with a clearer roadmap for constructing the scene logically.

Verifying Results and Iterating on Composition

After applying these refined prompts, verify the output by checking the relative proportions of key elements. Look specifically for the relationship between the foreground subject and the background objects. If the background still appears too dominant, increase the emphasis on distance in the next iteration. Conversely, if the background is too faint or small, adjust the wording to suggest proximity or prominence without losing the sense of depth.

If the issue persists after multiple attempts, consider whether the workflow requires features beyond the current model's optimization. As noted, Nano Banana 2 Lite is not designed for multi-turn sequential editing or complex reference inputs. In such cases, users might need to explore other options or accept the limitations of the specific model being used. For high-stakes projects requiring precise control, the Nano Banana Pro model may offer better stability, though it still relies heavily on the clarity of the prompt.

Ultimately, achieving consistent object scale is a collaborative process between the user's descriptive skills and the AI's interpretation capabilities. By treating the prompt as a set of rigorous spatial constraints rather than a casual description, users can significantly reduce scaling errors. For those ready to experiment with these techniques, Try Nano Banana to apply these strategies directly in the generator.

Remember that the goal is to guide the AI toward a realistic representation, acknowledging that while the tool is powerful, it does not possess an innate understanding of physics. Continuous refinement of language and structure remains the most reliable path to resolving inconsistencies in generated imagery.