Nano Banana 2 Image-to-Image Workflow for Correcting Color Palette Mismatches in Generated Landscapes

When generating game assets or digital art, it is common to create different sections of a scene—such as a foreground path and a distant mountain range—in isolation. While each section might look stunning individually, combining them often reveals jarring inconsistencies. One area may appear too warm while another feels unnaturally cool, breaking the immersion of the final composition. This guide outlines a practical Nano Banana 2 image-to-image workflow designed specifically to correct these color palette mismatches and ensure a unified visual tone across your entire project.

Preparing Your Input Assets and Strategy

Before launching into the editing phase, you must gather your raw materials and select the appropriate tool within the Nano Banana ecosystem. The core of this workflow relies on the image-to-image capabilities found at /nanobanana2. You will need two primary inputs: the base image containing the mismatched sections and a reference image that displays the desired color tone. If you do not have a specific reference, you can generate one using a text prompt describing the target atmosphere, such as "golden hour lighting with soft blue shadows," to serve as a stylistic guide.

It is crucial to choose the right model for this task. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image), which is well-suited for complex edits requiring multiple reference inputs or sequential adjustments. In contrast, Nano Banana 2 Lite is focused on speed and cost. It is explicitly not optimized for multiple reference inputs or multi-turn sequential editing. Therefore, for a workflow involving detailed color correction across separate sections, you should avoid Nano Banana 2 Lite unless you are willing to accept significant limitations in handling complex prompts. Do not assume that the existence of a Nano Banana Pro page or a Nano Banana Lite page on this website establishes support for all Google model names; always verify the specific capabilities required for your edit.

Constructing the Harmonization Prompt

The success of your color correction depends heavily on how you instruct the AI. Nano Banana refers to the AI image generation/editing tool in these articles, and its prompt library offers example prompts that users can copy or take into the generator. However, remember that prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. When correcting colors, you want to preserve the geometry and structure of your landscape while shifting the hue and saturation.

Below is an example prompt structure you can adapt. Label any untested prompt examples as examples, as results may vary based on the specific input images.

Example Prompt: "Recolor the landscape sections to match the warm golden-hour tones of the reference image. Maintain the original shapes of the mountains, trees, and paths. Blend the sky and ground colors seamlessly to remove harsh boundaries. Ensure the lighting direction remains consistent across the entire image."

This prompt focuses on the outcome (matching tones) rather than forcing specific pixel changes. By emphasizing the blending of sky and ground colors, you address the root cause of the mismatch. If the initial result is still slightly off, you can engage in a multi-turn conversation to refine the instruction, a process better supported by the standard Nano Banana 2 model rather than the Lite version.

Checkpoints and Exporting Your Unified Scene

As you iterate through the generation process, use specific checkpoints to evaluate progress. First, check if the structural integrity of the landscape has been preserved. Did the AI accidentally alter the shape of a mountain or the layout of a road? Second, assess the color harmony. Does the transition between the previously mismatched sections now feel natural? Third, verify the overall mood. Does the new palette convey the intended atmosphere without looking artificial?

Once you are satisfied with the output, proceed to the export steps. Navigate to the download options provided on the interface to save your corrected image. Be aware that while the tool supports text-to-image and image-to-image workflows, specific download functionality details depend on the current interface state. After saving, integrate the image into your game board or project. If further adjustments are needed, you can upload the exported image back into Nano Banana 2 for additional refinement cycles.

For those ready to begin this workflow immediately, Try Nano Banana. By following this structured approach, you can effectively resolve color palette mismatches and create cohesive, professional-grade landscapes without needing advanced manual editing skills.