Nano Banana: Avoiding Hallucinated Details in Character Design

When generating characters using Nano Banana, users often encounter a frustrating phenomenon where the AI introduces unintended elements or omits critical features. This symptom manifests as extra limbs, mismatched clothing textures, distorted facial structures, or accessories that simply do not exist in the reference concept. These hallucinations are particularly problematic in character design, where consistency and anatomical accuracy are paramount for storytelling and visual identity.

It is important to distinguish between these generation artifacts and actual model limitations regarding brand recognition. Nano Banana refers strictly to the AI image generation and editing tool described in this documentation. It is not a skincare brand, bottle, jar, or physical subject. The confusion often arises when users expect the model to preserve specific real-world product labels or typography exactly as they appear in a source image. Prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. Therefore, expecting perfect replication of complex text or branded logos on a character's clothing is a common source of perceived failure, even when the underlying anatomy is correct.

Separating Plausible Causes from Known Facts

To effectively troubleshoot these issues, one must separate plausible user-side causes from the known technical facts of the system. A frequent cause of missing features is insufficient prompt specificity. If a description relies on vague terms like "cool outfit" rather than defining specific garments, colors, and cuts, the model fills the gaps with its own probabilistic guesses, leading to inconsistencies.

Another factor involves the selection of the specific model variant. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image), Nano Banana Pro as Gemini 3 Pro Image (gemini-3-pro-image), and Nano Banana 2 Lite as Gemini 3.1 Flash Lite Image (gemini-3.1-flash-lite-image). These are distinct Google image models with different optimization goals. While Nano Banana 2 and Pro are designed for high-fidelity generation, Nano Banana 2 Lite is focused on speed and cost. Crucially, it is not optimized for multiple reference inputs or multi-turn sequential editing. Using the Lite version for complex character iterations without understanding this limitation can result in degraded detail accuracy and increased hallucination rates.

Furthermore, the availability of specific website pages does not automatically confirm feature parity across all model names. This website has a Nano Banana Pro page at /nanobananapro. Its page named Nano Banana Lite at /nanobananalite does not by itself establish support for Google Nano Banana 2 Lite. Google model names and capabilities must not be presented as proof of identical features on this website. Users must verify that their chosen workflow aligns with the capabilities of the specific model they have selected.

Diagnosing and Fixing Prompt Specificity

Diagnosing the root cause usually begins with analyzing the prompt structure. If the output shows missing eyes or floating hands, the prompt likely lacked negative constraints or precise anatomical descriptors. To fix this, users should move from abstract concepts to concrete descriptions. Instead of saying "a warrior," specify "a female warrior wearing chainmail armor with a silver helmet featuring a red plume."

The prompt library offers example prompts that users can copy or take into the generator. These examples serve as a baseline for structure but should be adapted to the specific needs of the character. When refining prompts, explicitly state what you want to avoid. For instance, adding "no extra fingers, no blurred faces, consistent lighting" can guide the model away from common artifacts. However, remember that prompt instructions describe desired outcomes; they do not guarantee identity, label, object or typography preservation. If the goal is to keep a specific logo intact, relying solely on text prompts may fail, and the model might hallucinate a generic symbol instead.

For complex character designs requiring multiple reference inputs or sequential edits, ensure you are not using Nano Banana 2 Lite. Since it is not optimized for those workflows, switching to Nano Banana 2 or Nano Banana Pro is necessary to maintain detail integrity. You can explore the available options at Try Nano Banana to access the appropriate tools for your project.

Verifying Accuracy Through Iterative Refinement

Verification is an iterative process. After generating an initial draft, compare the output against the original intent. Check for anatomical correctness, such as the number of limbs and the alignment of joints. Inspect clothing details for texture consistency and logical layering. If the character looks correct but the text on a shirt is gibberish, this confirms the known fact that the model does not guarantee typography preservation.

If artifacts persist, try adjusting the balance between positive and negative constraints in the prompt. Use the example prompts from the library as a starting point, but modify them to include more granular details about the character's pose and environment. Label untested prompt examples as examples to manage expectations; they demonstrate syntax rather than guaranteed results. By systematically refining the prompt and selecting the correct model variant based on the complexity of the task, users can significantly reduce hallucinated details and achieve higher fidelity in their character designs.