Nano Banana 2 Lite Troubleshooting: Fixing Inconsistent Object Scale Across Batches

When generating multiple assets in a single batch using Nano Banana 2 Lite, users may encounter a specific symptom where objects appear disproportionately sized compared to previous or subsequent outputs. One image might feature a character that looks like a giant relative to the background, while the next generation renders the same subject as tiny or miniature. This inconsistency is particularly noticeable when attempting to create a series of assets with uniform visual weight. It is important to clarify that Nano Banana refers to the AI image generation tool and not a cosmetic brand or physical product. The issue stems from the underlying architecture of the model rather than a user error in file management.

Distinguishing Symptoms from Model Limitations

To effectively troubleshoot this issue, it is crucial to separate the observed symptoms from the known technical facts of the platform. The symptom is clear: sequential outputs within a batch lack consistent scaling, resulting in objects that vary wildly in size relative to their environment. However, the cause is not a bug in the rendering engine but a fundamental design choice of the specific model version being used.

Google documents Nano Banana 2 Lite as Gemini 3.1 Flash Lite Image (gemini-3.1-flash-lite-image). Unlike its counterparts, this model is explicitly focused on speed and cost efficiency. A verified fact regarding this workflow is that Nano Banana 2 Lite is not optimized for multiple reference inputs or multi-turn sequential editing. Consequently, the model does not possess an auto-correct mechanism to maintain scale consistency across sequential outputs. While the prompt library offers example prompts that users can copy, these instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. Therefore, relying on the model to infer consistent scale without explicit guidance will lead to the inconsistencies described.

Diagnosing the Root Cause

The diagnosis for inconsistent object scale lies in the trade-off between performance and precision. Because Nano Banana 2 Lite prioritizes rapid generation and lower costs, it sacrifices the contextual memory required to hold a strict size parameter constant across a batch. When you generate a batch, the model treats each request somewhat independently, recalculating spatial relationships based on the immediate prompt text rather than maintaining a global scene geometry from the first image to the last.

This behavior is distinct from other models in the family. For instance, Google describes Nano Banana 2 as Gemini 3.1 Flash Image and Nano Banana Pro as Gemini 3 Pro Image. These are distinct Google image models with different capabilities. While the website has a Nano Banana 2 product page at /nanobanana2 supporting text-to-image workflows, the Lite version operates under different constraints. Users must recognize that the lack of optimization for sequential editing means the system cannot automatically correct scaling drift. If you require high-fidelity consistency across a large set of images, this limitation suggests that the Lite version may not be the optimal tool for that specific workflow without manual intervention.

Strategies for Consistent Scaling

Since the model does not auto-correct scaling, the solution requires users to adjust prompt descriptors for size explicitly in every generation. To achieve consistent results, you must treat each image in the batch as a standalone task that requires detailed spatial instruction. Instead of assuming the model remembers the size of an object from the first prompt, restate the scale requirements in every single prompt variation.

For example, if you are generating a series of icons, do not simply say "a cat." Instead, use descriptive phrases such as "a large, foreground cat filling 50% of the frame" or "a small, distant cat in the background." By anchoring the object's size to specific visual metrics in the text, you provide the necessary context for the model to approximate the desired scale. Remember that prompt instructions describe desired outcomes; they do not guarantee identity or preservation. Therefore, testing different phrasings is essential.

If you find that even with explicit descriptors the results remain inconsistent, consider whether your workflow truly requires the Lite version. The documentation notes that Nano Banana 2 Lite is not optimized for multi-turn sequential editing. In such cases, exploring the capabilities of the standard Nano Banana 2 or Nano Banana Pro might yield better stability, though pricing and speed profiles will differ. For those needing to test specific scaling scenarios quickly, you can Try Nano Banana to experiment with prompt variations in real-time.

Verifying Your Adjustments

After implementing explicit size descriptors, verification involves comparing the generated batch visually. Check if the relative proportions of the main subjects remain stable against the background elements. Since there are no automated statistics or download functionality provided to measure pixel-perfect consistency, the verification process relies on human observation. Generate a small test batch of three to five images using your refined prompts. If the objects still appear disproportionately sized, refine the language further by adding comparative terms like "twice as tall as the tree" or "smaller than the car."

It is vital to avoid claims of guaranteed outcomes, as AI generation remains probabilistic. Even with perfect prompting, slight variations are inherent to the technology. However, by acknowledging the model's focus on speed and cost over sequential consistency, and by manually enforcing scale through detailed prompts, you can significantly reduce the frequency of inconsistent object sizes. Always remember that Nano Banana names the image tool, never the depicted cosmetic brand or physical product, ensuring your focus remains on the digital creation process.