Nano Banana 2 Image-to-Image: Keeping Market Baskets Recognizable

Understanding Object Identity in AI Editing

When working with generative AI, one of the most common challenges is preserving the core identity of an object while altering its environment. In this tutorial, we focus specifically on the task of maintaining object identity for a market basket. Whether you are a photographer looking to simulate different times of day or a designer testing new material finishes, the goal is to ensure the basket remains recognizable as the same item despite significant changes to texture or lighting conditions.

It is crucial to understand that Nano Banana refers to the AI image generation and editing tool used here. It is not a skincare brand, bottle, jar, or physical subject. The examples provided in this guide involve generic, unbranded market baskets to illustrate the capabilities of the system without implying endorsement of specific commercial products. When using the image-to-image workflow, prompt instructions describe desired outcomes; however, they do not guarantee identity, label, object, or typography preservation. Users should approach these edits as creative experiments rather than guaranteed reproductions.

Selecting the Right Model for Complex Edits

Choosing the correct model within the Nano Banana ecosystem is vital for tasks requiring high fidelity and structural consistency. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image), Nano Banana Pro as Gemini 3 Pro Image (gemini-3-pro-image), and Nano Banana 2 Lite as Gemini 3.1 Flash Lite Image (gemini-3.1-flash-lite-image). These are distinct Google image models with different strengths.

For a task like maintaining the intricate weave of a market basket while changing its lighting, speed alone may not be sufficient. Google describes Nano Banana 2 Lite as focused on speed and cost. It is not optimized for multiple reference inputs or multi-turn sequential editing. Therefore, it is generally not recommended for complex workflows where maintaining strict object identity is the primary concern without explaining this limitation first. For better results in preserving the specific structure of the basket, users should consider the standard Nano Banana 2 or Nano Banana Pro options available on their respective product pages.

You can explore the features of the main tool by visiting Try Nano Banana. This page supports text-to-image and image-to-image workflows, providing the necessary interface for uploading your source images and refining your prompts.

Step-by-Step Workflow for Texture and Lighting Changes

To successfully edit a market basket while keeping it recognizable, follow this structured approach using the Nano Banana 2 image-to-image capabilities.

1. Prepare Your Source Image: Upload a clear photo of your market basket. Ensure the lighting in the original image is decent, as the AI will use this as the baseline for structure.
2. Access the Image-to-Image Interface: Navigate to the generator section on the Nano Banana 2 product page. Select the image-to-image mode rather than text-to-image.
3. Craft a Specific Prompt: Write a prompt that explicitly states what you want to keep and what you want to change. For example, "Keep the woven market basket structure identical, but change the lighting to golden hour sunset and apply a wet wood texture." Remember that prompt instructions describe desired outcomes; they do not guarantee identity preservation.
4. Adjust Strength Parameters: If the interface allows, adjust the image strength or denoising strength. A lower strength value typically retains more of the original image's structure, which helps maintain the basket's identity. Higher values allow for more creative freedom but risk altering the object's shape too much.
5. Generate and Review: Run the generation process. Review the output to see if the basket still looks like the original item. If the weave pattern has disappeared or the shape has warped, try lowering the strength or refining the prompt to emphasize "identical structure" or "same object."

Evaluating Results and Troubleshooting Common Issues

Judging the success of your edit requires a critical eye. Ask yourself: Does the edited image still look like the specific market basket from the input? Has the handle moved? Is the weave pattern consistent? Since there are no guarantees of identity preservation, you may need to iterate several times.

If the basket loses its identity, consider the following fixes:

• Refine the Prompt: Add negative prompts if available, such as "no new objects," "do not change shape," or "preserve original geometry."
• Lower Denoising Strength: Reducing the amount of noise added during generation forces the AI to stick closer to the original pixel data.
• Switch Models: If you were using a faster, lighter model, try switching to Nano Banana 2 or Nano Banana Pro for better structural adherence.

Remember that these are example workflows. While the tools offer powerful capabilities, the outcome depends heavily on the interplay between your prompt, the source image, and the model selected. By understanding the limitations of each model and carefully crafting your instructions, you can achieve impressive results that transform the atmosphere of your image without losing the soul of the object.

For more information on how these models function, refer to the official documentation at https://ai.google.dev/gemini-api/docs/image-generation.