How to Review Generated Image Artifacts in Nano Banana 2

When working with AI image generation tools like Nano Banana 2, the output is rarely perfect on the first try. Identifying and understanding image artifacts—unwanted visual errors such as distorted limbs, strange textures, or inconsistent lighting—is a critical skill for any user. This tutorial provides a structured approach to reviewing these artifacts, ensuring you can refine your results effectively without relying on unverified claims about specific model performance.

Understanding What Constitutes an Artifact

Before diving into the review process, it is essential to define what constitutes an artifact in this context. In the realm of AI image generation, artifacts are deviations from the intended prompt or logical reality. These might include morphological errors where fingers merge into hands, text that appears as gibberish, or background elements that do not align with the foreground perspective. It is important to note that while Google documents models like Gemini 3.1 Flash Image under the name Nano Banana 2, the features available on this specific website may differ. You must evaluate the tool based on what you see here, rather than assuming all documented Google capabilities are active.

The Nano Banana 2 product page at /nanobanana2 supports both text-to-image and image-to-image workflows. However, users should be aware that prompt instructions describe desired outcomes but do not guarantee the preservation of identity, labels, objects, or typography. Therefore, when reviewing images, look specifically for inconsistencies in these areas. For instance, if your prompt requests a specific logo, check if the text is legible or if it has degraded into abstract shapes, which is a common artifact.

Concrete Inputs and Labeled Example Prompts

To effectively test for artifacts, you need concrete inputs that stress-test the model's logic. A good strategy is to use prompts that require complex spatial relationships or fine details. Below is a labeled example prompt designed to reveal common structural and textural issues. Please treat this as an example; the tool does not guarantee specific outputs.

Example Prompt: "A close-up photograph of a futuristic robot holding a red apple. The robot has intricate metallic gears visible on its chest. There is a small, handwritten sign in the background that reads 'Fresh Fruit'. The lighting is soft and natural, coming from the left side."

This prompt contains several potential failure points: the number of fingers on the hand holding the apple, the clarity of the gears, the legibility of the handwritten sign, and the consistency of the lighting direction. By generating this specific input, you create a controlled environment to spot artifacts. If the robot has six fingers, the sign is unreadable, or the shadows fall to the right despite the prompt specifying the left, you have identified specific artifacts to address.

Result Checks and User-Run Evaluation Methods

Once you have generated an image, a systematic review process helps isolate issues. Do not rely on general impressions; instead, perform a targeted check. First, zoom in to inspect high-frequency details like edges, textures, and text. Look for blurring where sharp lines should exist or smearing where distinct objects meet. Second, verify logical consistency. Does the object being held actually connect to the hand? Is the shadow cast by the apple consistent with the light source described?

It is crucial to distinguish between the capabilities of the underlying Google models and the features available on this website. For example, Google describes Nano Banana 2 Lite as focused on speed and cost, noting it is not optimized for multiple reference inputs or multi-turn sequential editing. If you are using a version of the tool that resembles Lite, you might encounter more artifacts in complex scenes because the model prioritizes speed over detailed refinement. Conversely, if you are accessing the standard Nano Banana 2 workflow, expect higher fidelity but still remain vigilant for errors.

If you notice persistent artifacts, consider adjusting your prompt to be more explicit about the problematic area. Instead of just saying "robot," try "robot with five distinct fingers." However, remember that prompt instructions do not guarantee preservation. If the artifact persists, it may be a limitation of the current model iteration rather than a prompt issue.

For those looking to explore different capabilities, you can visit the Try Nano Banana page to start your own evaluation. Remember that the Nano Banana Pro page at /nanobananapro offers a different set of options, and the existence of a Nano Banana Lite page at /nanobananalite does not automatically confirm support for the specific Google Nano Banana 2 Lite model features. Always verify the actual behavior within the interface.

Fixes and Iterative Refinement

Fixing artifacts often requires an iterative approach. If an image shows distorted hands, try regenerating with a modified prompt that emphasizes anatomy. If text is garbled, acknowledge that the tool does not guarantee typography preservation and consider adding the text via post-processing software rather than expecting the AI to render it perfectly. When dealing with lighting inconsistencies, explicitly state the light source position again in your next attempt.

Avoid assuming that a single model update will solve all issues. Since this website supports various workflows, the best method is to run your own evaluation. Generate the same prompt multiple times and compare the results. Note which variations produce fewer artifacts and analyze why. This user-run evaluation method provides real data on what works for your specific needs, far more reliable than generic benchmark results found elsewhere.

By following these steps, you can master the art of reviewing and refining generated images, ensuring your final output meets your creative standards while navigating the unique constraints of the Nano Banana 2 platform.