Mastering Nano Banana 2 Image-to-Image Prompt Structure

Creating high-quality variations of existing images requires more than just a simple description. When using Nano Banana for image-to-image tasks, the structure of your text prompt is the primary lever you have to control the output. It is crucial to understand that while Google documents models like Gemini 3.1 Flash Image under the name Nano Banana 2, this website offers specific features that may differ from the raw model capabilities. This guide focuses on how to construct effective prompts within the Nano Banana 2 interface at /nanobanana2 to achieve desired visual changes without relying on unverified assumptions.

Understanding the Core Components of Your Prompt

In an image-to-image workflow, your prompt serves as a set of instructions that tells the AI how to interpret the input image and what modifications to apply. Unlike text-to-image generation where the prompt builds a scene from scratch, here the prompt acts as a filter or a transformation layer. The system reads your instructions alongside the visual data of the uploaded image.

A robust prompt structure generally consists of three distinct parts: the base instruction, the style modifier, and the constraint list. The base instruction should clearly state the primary action, such as "change the background" or "update the clothing." The style modifier defines the aesthetic outcome, for example, "in a cyberpunk style" or "photorealistic lighting." Finally, constraints are essential for maintaining fidelity; they tell the model what not to change, such as "keep the facial features identical" or "preserve the original logo text."

It is important to note that prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. If your goal is to keep a specific brand logo or text exactly as it appears in the source image, you must explicitly state this in the constraints, though success is not guaranteed by the system alone.

Constructing a Labeled Example Prompt

To illustrate the correct structure, consider a scenario where you want to take a photo of a generic coffee cup and transform it into a stylized illustration while keeping the shape of the cup intact. Below is a labeled example prompt designed for this specific task. You can use this structure as a template for your own projects.

Base Instruction: Transform the subject into a watercolor illustration.

Style Modifier: Use soft pastel colors with visible brush strokes and a white background.

Constraints: Maintain the exact silhouette of the cup and handle. Do not alter the position of the steam rising from the top.

Combined Prompt: Transform the subject into a watercolor illustration. Use soft pastel colors with visible brush strokes and a white background. Maintain the exact silhouette of the cup and handle. Do not alter the position of the steam rising from the top.

This labeled approach ensures that every part of your request is clear to the generator. Remember that these are examples of prompt structures; actual results will vary based on the specific input image and current model behavior. For users looking to experiment with different styles, the prompt library on the site offers additional examples that users can copy or take into the generator to see how different phrasings affect the output.

Evaluating Results and Troubleshooting Common Issues

After generating an image, you must evaluate the result against your initial intent. Since the system does not promise perfect adherence to all constraints, a manual check is necessary. Look specifically at the areas you tried to preserve. Did the silhouette remain unchanged? Was the style applied correctly? If the image deviates significantly, analyze which part of your prompt might have been too vague or contradictory.

If the output fails to match your expectations, try refining the constraints section. Often, adding negative constraints (what you do not want) helps steer the model away from unwanted artifacts. For instance, if the background changes when you only wanted to change the foreground, add Keep the background exactly as is to your prompt.

Be aware of the limitations regarding different versions of the tool. While 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. If you are attempting complex edits that require several steps or multiple reference images, avoid assuming the Lite version will handle them seamlessly. Always verify if the specific feature you need is supported on the current page before starting a complex workflow.

For those needing advanced capabilities, the website also hosts a Nano Banana Pro page at /nanobananapro, which may offer different performance characteristics compared to the standard Nano Banana 2 interface. However, the presence of a Nano Banana Lite page at /nanobananalite does not automatically establish support for the specific Google Nano Banana 2 Lite model features. Comparisons between versions should be done through user-run evaluations rather than relying on external benchmark claims.

By structuring your prompts with clear instructions, modifiers, and constraints, you can significantly improve the reliability of your image-to-image results. Start with simple transformations and gradually increase complexity as you learn how the Nano Banana 2 engine responds to specific wording. Try Nano Banana to begin experimenting with these structures today.

Remember, the goal is to guide the AI, not command it. Clear, descriptive language yields better results than short, ambiguous commands. If you encounter issues, revisit your prompt structure to ensure your constraints are explicit and your style modifiers are precise.