Nano Banana not applying color from a reference image

Understanding the Challenges of Color Transfer in Nano Banana: A Case Study

In the realm of AI-assisted image editing, users often seek seamless ways to transfer specific colors or materials from one image to another. A common scenario involves repainting surfaces in a photograph to match a particular hue provided separately. However, recent experiences indicate that achieving perfect color replication using Nano Banana’s tools can sometimes present unforeseen challenges.

The Scenario

Consider a straightforward task: recoloring the walls of a bedroom image with a precise shade of light blue. The process involved:

1. Uploading a Color Reference: An image containing a solid block of the target hue was uploaded, with clear instructions to the AI to remember this color for subsequent steps.

2. Uploading the Target Image: A photograph of a bedroom with green walls was provided.

3. Color Application Request: The user instructed the AI to create a new image where the bedroom’s walls are painted in the previously supplied color, explicitly stating, “The final image should be a bedroom picture with the walls painted in the first color I provided.”

Despite clear directives and multiple attempts, the AI consistently retained the original green walls, disregarding the color reference. This indicates a recurring difficulty in Nano Banana’s ability to sample and apply colors from an external reference image.

Potential Workarounds and Limitations

One considered workaround involves asking the AI to identify the color in the reference image by name (e.g., “Periwinkle Blue”) and then requesting the AI to recolor the walls accordingly. While this method can produce acceptable results, it relies on the AI’s internal color knowledge rather than an exact shade match, leading to potential discrepancies.

The core challenge lies in the AI’s interpretation: rather than sampling the precise color from the reference image, the model attempts to apply a general understanding of named colors or visual features, which may not match the exact shade desired.

Expert Insights and Recommendations

This experience underscores the importance of precise prompting and understanding the limitations of current AI tools in color sampling tasks. To improve results, consider the following strategies:

• Use Detailed Descriptions: Instead of solely referencing images, provide detailed color descriptions (e.g., RGB or HEX codes) in prompts to guide the AI more accurately.

• Layered Editing: Employ image editing software post-AI generation to fine-tune colors, ensuring exact matches.

• Material and Texture Transfer: When working with textures or patterns, AI models often excel, highlighting that color-specific tasks may require supplementary steps