Fixing Inconsistent Lighting Direction in Nano Banana Collages

When generating a collage of event photos using Nano Banana, the most common visual flaw is inconsistent lighting direction. You might notice that one subject casts a shadow to the left while another in the same frame has highlights coming from the right. This creates a disjointed, surreal appearance that breaks the immersion of the scene. The symptom is not merely a difference in brightness but a fundamental conflict in how the AI interprets the light source for each individual image within the composition.

This issue often arises because the tool processes multiple reference inputs or generates new elements based on separate prompts that do not explicitly define a single, cohesive environment. Without a unified directive, the model may default to the lighting conditions present in each original photo or invent random sources for new elements. To fix this, you must move beyond simply combining images and instead instruct the AI to treat the entire collage as a single scene with one dominant light source.

Separating Plausible Causes from Known Facts

It is crucial to distinguish between what users observe and the technical realities of the generation process. A frequent assumption is that the inconsistency stems from a bug in the rendering engine or a failure to merge layers correctly. However, known facts indicate that prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. This means the AI prioritizes the semantic meaning of your text over strict adherence to the original pixel data of your input images.\n Another plausible cause users suspect is the specific model version being used. While Google documents Nano Banana 2 Lite as focused on speed and cost, it is explicitly noted that it is not optimized for multiple reference inputs or multi-turn sequential editing. If you are attempting to build a complex collage with varying lighting requirements using Nano Banana 2 Lite, the model may struggle to maintain consistency across different parts of the image due to these architectural limitations. Conversely, Nano Banana Pro (Gemini 3 Pro Image) and Nano Banana 2 (Gemini 3.1 Flash Image) offer more robust capabilities for handling complex scenes, though they still require clear guidance to align lighting.

The root cause is rarely a lack of processing power but rather a lack of explicit environmental constraints in the prompt. When the prompt fails to specify "uniform lighting" or "single light source," the model fills the gaps with its own probabilistic guesses, leading to the conflicting shadows seen in the final output.

Step-by-Step Guide to Standardizing Light Sources

To resolve the inconsistency, you need to rewrite your prompt to enforce a singular lighting environment. Start by identifying the primary light source you want to emulate, such as "golden hour sunlight from the upper left" or "soft studio lighting from the front." Do not rely on the AI to infer this from the input images alone; state it clearly.

When constructing your request, combine the description of the scene with the specific lighting constraint. For example, instead of asking for "a collage of people at a party," try "a unified collage of people at a party under consistent golden hour sunlight streaming from the top left corner, ensuring all shadows align to the bottom right." This approach forces the model to re-render the lighting across all elements to match your description.

If you are working with existing images that have conflicting lights, you may need to use an image-to-image workflow where the prompt overrides the original lighting cues. Be aware that prompt instructions do not guarantee perfect preservation of every detail, so some adjustment in the original subjects' appearance may occur to accommodate the new lighting rules. For best results, avoid using Nano Banana 2 Lite for this specific task if your collage requires high fidelity across multiple reference inputs, as it lacks optimization for such complex workflows.

Verifying Your Results and Next Steps

After generating the image, inspect the collage closely for continuity. Check the edges where different subjects meet to ensure no harsh transitions in shadow length or direction exist. Verify that the highlight intensity matches the angle of the light source across the entire frame. If inconsistencies remain, refine your prompt by adding more descriptive adjectives about the atmosphere, such as "diffused light" or "hard directional shadows," and regenerate.

Remember that the goal is a unified look, not necessarily a perfect replica of reality, as the AI is synthesizing a new image based on your instructions. If you find that the current model struggles to maintain the lighting direction after several attempts, consider switching to a more capable model like Nano Banana Pro, which handles complex scene synthesis better than the Lite version.

For those ready to experiment with these advanced prompting techniques to create seamless event collages, Try Nano Banana. By taking control of the lighting parameters in your prompt, you can transform disjointed photo collections into cohesive, professional-grade visual stories.