How to Review Generated Image Artifacts for Lighting Inconsistencies in Nano Banana 2

When generating images with AI tools, achieving visual coherence is often as important as the subject matter itself. One of the most common areas where generated content can falter is lighting consistency. Even a well-crafted prompt may result in an image where shadows fall in impossible directions, highlights clash with the stated time of day, or multiple light sources create confusing depth. This guide focuses on how to effectively review these artifacts specifically within the context of Nano Banana 2.

Nano Banana refers to the AI image generation and editing tool used in this workflow. It is not a skincare brand, bottle, jar, or physical subject. The platform supports both text-to-image and image-to-image workflows, allowing users to iterate on concepts quickly. However, because the underlying models interpret prompts based on statistical patterns rather than physical laws, manual review remains essential for professional results. By understanding what to look for, you can refine your inputs and achieve more believable imagery.

Identifying Contradictory Light Sources

The first step in reviewing your output is to establish the intended lighting scenario defined in your prompt. Did you request a "sunset scene," "overcast studio lighting," or "neon-lit cyberpunk street"? Once you have this mental baseline, examine the generated image for any elements that violate that specific environment.

Look for conflicting directional cues. If your prompt specifies a single sun source from the left, check every object in the frame. Do the shadows cast by trees, people, or furniture all point consistently to the right? A common artifact occurs when the model attempts to satisfy complex composition requests but fails to unify the illumination, resulting in some objects lit from the front while others are lit from behind. This creates a disjointed visual experience that breaks immersion.

Additionally, pay attention to color temperature mismatches. A prompt describing a warm firelight should not produce cool blue shadows unless explicitly requested. If the highlights are orange but the shadows are stark white or deep blue without a logical reason, this indicates a lighting inconsistency. These artifacts often happen when the model blends multiple training data concepts that do not naturally coexist. Use the prompt library available on the site to compare example prompts against your own to see how similar scenarios were handled successfully.

Analyzing Shadow Logic and Depth Cues

Shadows are critical for establishing depth and grounding objects in a scene. When reviewing Nano Banana 2 outputs, scrutinize the relationship between objects and their shadows. Are the shadows soft or hard? Do they match the intensity of the light source described? For instance, a harsh midday sun should produce sharp, high-contrast shadows, whereas a cloudy day should yield soft, diffuse transitions.

A frequent issue involves shadow placement relative to the ground plane. Objects may appear to float if their shadows do not align with the surface they are standing on, or if the shadow direction contradicts the perspective of the camera. Another artifact is the "ghost shadow," where an object casts a shadow that seems to belong to a different object nearby. This often happens when the model struggles to parse complex spatial relationships in dense scenes.

To mitigate these issues, consider simplifying your prompt to focus on clear lighting conditions before adding complex details. If you are working with image-to-image workflows, ensure the reference image has consistent lighting that matches your desired outcome. Note that while Nano Banana 2 supports various workflows, specific limitations apply to other versions like Nano Banana 2 Lite, which is focused on speed and cost and is not optimized for multiple reference inputs or multi-turn sequential editing. Therefore, for complex lighting reviews, relying on the standard Nano Banana 2 capabilities is advisable.

Practical Steps for Iterative Refinement

Once you have identified inconsistencies, the next phase is refinement. Start by isolating the specific element causing the problem. If the shadows are wrong, try adjusting the prompt to be more explicit about the light direction, such as adding "shadows cast strictly to the right" or "single overhead light source." Be aware that prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. The model will attempt to follow your guidance, but results vary.

Here is a usable prompt strategy to test lighting consistency:

1. Define the primary light source clearly (e.g., "golden hour sunlight from the west").
2. Specify the shadow characteristics (e.g., "long, soft shadows stretching eastward").
3. Describe the ambient fill (e.g., "cool blue ambient light filling the shadows").
4. Request a specific mood that relies on correct lighting (e.g., "atmospheric and moody").

After generating the new image, repeat the review process. Compare the new output side-by-side with the previous one. Look for improvements in shadow alignment and color harmony. If the artifacts persist, consider breaking the task into smaller steps or using the image-to-image feature to refine specific areas rather than regenerating the entire scene.

For those interested in exploring the full capabilities of the tool, you can Try Nano Banana. Remember that Google documents Nano Banana 2 as Gemini 3.1 Flash Image, distinct from Nano Banana Pro or Lite. Understanding these model distinctions helps set realistic expectations for performance and feature availability.

By systematically scanning for mismatched shadows and contradictory light sources, you transform the AI generation process from a game of chance into a controlled creative workflow. While no tool can guarantee perfect outcomes every time, a disciplined review process significantly increases the likelihood of producing high-quality, visually coherent images.