Nano Banana 2 Tutorial: Using the Prompt Library to Prevent Identity Drift

Creating a series of images featuring the same character can be challenging. Without careful planning, AI models often introduce subtle changes to a subject's face, hair, or clothing style over time. This phenomenon is known as identity drift. While some workflows rely on complex multi-turn editing to correct these shifts, you can achieve significant stability by leveraging the built-in prompt library within Nano Banana 2. This tutorial explains how to use pre-written instructions to lock in your character's core features from the start.

Understanding the Role of Prompt Instructions

The prompt library in Nano Banana 2 serves as a repository of example prompts that users can copy directly into the generator. These instructions are designed to describe desired outcomes clearly. However, it is crucial to understand that prompt instructions do not guarantee identity, label, object, or typography preservation. They act as strong directional signals rather than absolute constraints.

When working on character consistency, the goal is to provide the model with a dense, descriptive foundation that leaves little room for interpretation regarding the subject's appearance. By selecting high-quality examples from the library, you establish a baseline description that emphasizes specific physical traits. This approach is particularly effective when you want to avoid the pitfalls of sequential editing, where errors can compound over multiple steps.

Selecting the Right Model for Consistency

Before diving into the prompt library, ensure you are using the correct version of the tool. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image). For more advanced capabilities, Nano Banana Pro utilizes Gemini 3 Pro Image (gemini-3-pro-image). It is important to note that Nano Banana 2 Lite is focused on speed and cost efficiency. It is not optimized for multiple reference inputs or multi-turn sequential editing. Therefore, if your primary goal is preventing identity drift through robust prompting, Nano Banana 2 Lite may not be the ideal choice without understanding its limitations.

For this tutorial, we assume you are using the standard Nano Banana 2 interface found at Try Nano Banana. The prompt library is accessible within this environment, offering a variety of templates that you can adapt. Remember that the website supports both text-to-image and image-to-image workflows, but this guide focuses on using text-based prompts to set the stage for consistency.

Step-by-Step Guide to Building Stable Character Prompts

To prevent identity drift, follow these numbered steps to construct your generation strategy using the prompt library:

1. Access the Prompt Library: Navigate to the Nano Banana 2 interface and locate the prompt library section. Browse the available examples to find those that focus on detailed character descriptions.
2. Copy a Base Template: Select an example prompt that closely matches the general style of your character. Copy this text into your input field. Label any untested prompt examples as examples before modifying them.
3. Inject Specific Traits: Identify the unique features of your character, such as eye color, hairstyle, or distinct accessories. Add these details explicitly into the copied prompt. Be precise; vague terms like "nice eyes" are less effective than "piercing blue eyes with long lashes."
4. Reinforce Context: Include environmental context that does not distract from the subject. If the character is always seen in a specific setting, mention it briefly to ground the image, but keep the focus on the person.
5. Generate and Review: Run the generation. Compare the output against your mental image of the character. If the identity holds, save this modified prompt as your new template for future generations.
6. Iterate Without Editing: Instead of generating a new image and then trying to edit it to fix a drifted feature, regenerate the image using your refined prompt. This maintains the integrity of the original design intent.

Judging Results and Fixing Common Issues

How do you know if your strategy is working? Judge results by comparing multiple generations side-by-side. Look for consistency in the shape of the jawline, the position of the eyes, and the texture of the hair. If you notice the character's nose changing shape or their hair color shifting, your prompt likely lacked sufficient specificity or relied too heavily on generic descriptors.

If identity drift occurs despite your efforts, try the following fixes:

• Increase Descriptive Density: Add more adjectives related to the specific features that are drifting.
• Simplify the Scene: Remove unnecessary background elements that might confuse the model about what constitutes the main subject.
• Verify Model Selection: Ensure you are not accidentally using Nano Banana 2 Lite for tasks requiring high fidelity, as it lacks optimization for complex reference handling.

By treating the prompt library as a foundational tool rather than a magic solution, you can significantly reduce identity drift. This method allows you to maintain a cohesive visual narrative across multiple generations without relying on error-prone multi-turn editing processes.

Remember, while these strategies improve consistency, they do not guarantee perfect identity preservation in every single output. Always test your prompts thoroughly before committing to a large batch of generations.