Nano Banana Tutorial: Eliminating Lens Distortion with Negative Prompts

When generating images of people using AI tools, the camera perspective plays a critical role in the final output. Wide-angle lenses are popular for their ability to capture expansive scenes, but they often introduce unwanted optical side effects when used too close to a subject. The most common issue is lens distortion, where facial features appear stretched, pinched, or unnaturally warped. This tutorial explains how to leverage the specific capabilities of Nano Banana to counteract these artifacts using negative prompts.

Nano Banana is an AI image generation and editing tool designed to help users create high-quality visuals through text-to-image and image-to-image workflows. While the tool offers a robust prompt library with examples that users can copy, understanding how to refine these inputs is essential for achieving professional results. By explicitly telling the model what not to include, you can guide the generation process away from optical flaws without needing complex post-processing.

Understanding the Prerequisites for Clean Generation

Before attempting to correct distortion, it is important to understand the environment in which you are working. Nano Banana supports both text-to-image and image-to-image modes, allowing flexibility in how you start your project. However, the effectiveness of negative prompts relies heavily on the clarity of your input instructions.

The primary prerequisite is a clear understanding of the desired outcome. If you intend to generate a portrait that looks like it was taken with a standard or telephoto lens, you must avoid terms that imply extreme wide-angle perspectives unless you plan to actively negate them. It is also crucial to remember that prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. Therefore, while negative prompts can remove distortions, they cannot force the AI to maintain specific details if the core concept conflicts with the requested style.

Additionally, users should be aware that Nano Banana refers to the AI image generation/editing tool in these articles. It is not a skincare brand, bottle, jar, or physical subject. Example products mentioned in the system are generic and unbranded. When crafting your workflow, focus on the digital nature of the generation rather than physical camera hardware, as the AI interprets concepts rather than simulating real-world optics directly.

Step-by-Step Guide to Removing Artifacts

To eliminate lens distortion artifacts effectively, follow this structured approach within the Nano Banana interface. These steps utilize the negative prompt feature to exclude specific visual characteristics associated with wide-angle warping.

1. Define Your Subject: Start by writing a positive prompt that clearly describes the person or object you want to generate. Be specific about the lighting, mood, and general composition. For example, "A close-up portrait of a woman with soft natural lighting."
2. Identify Distortion Keywords: Analyze the types of distortion you wish to avoid. Common terms associated with wide-angle warping include "fisheye," "barrel distortion," "stretched face," "bulging eyes," "wide angle lens," and "perspective distortion."
3. Construct the Negative Prompt: Enter these identified keywords into the negative prompt field. This tells the AI to actively suppress these features during the generation process. A sample negative prompt might look like: "fisheye, barrel distortion, stretched face, bulging eyes, wide angle lens, perspective distortion, warped proportions."
4. Generate and Review: Run the generation. Observe the results closely. Since prompt instructions do not guarantee specific outcomes, you may need to iterate. If the distortion persists, try adding more descriptive terms related to the flaw you see.
5. Refine Iteratively: If the first attempt still shows minor warping, adjust the weight or specificity of your negative terms. You might add "straight lines" or "natural proportions" to reinforce the desired geometry.

It is important to note that the prompt library offers example prompts that users can copy or take into the generator. However, these examples are illustrative. You should treat any specific negative prompt structure provided here as an example rather than a guaranteed solution for every unique scenario.

Judging Results and Troubleshooting Fixes

Evaluating whether your negative prompts have worked requires a critical eye. Look specifically at the edges of the frame and the relationship between facial features. In a successful generation, the nose should not appear disproportionately large, and the ears should not seem pushed back unnaturally. The background lines should remain relatively straight if the scene includes architectural elements.

If the results still show distortion, consider the following fixes:

• Strengthen the Negative Input: Sometimes, a single term is not enough. Combine multiple descriptors like "no fisheye effect" and "correct perspective" to ensure the AI understands the constraint.
• Adjust the Positive Prompt: Ensure your positive prompt does not inadvertently encourage distortion. Avoid phrases like "extreme close-up" or "ultra-wide view" unless you are certain the negative prompt will override them.
• Check Image-to-Image Settings: If you are starting from an existing image, the source image itself might contain distortion that the AI tries to preserve. In such cases, increasing the denoising strength or providing a cleaner base image can help.

Remember that AI generation involves probabilistic outcomes. While these techniques significantly reduce the likelihood of artifacts, they do not offer absolute guarantees. Always test different combinations to find the best balance for your specific needs.

For those ready to experiment with these techniques, Try Nano Banana to access the full suite of generation tools and begin creating distortion-free portraits today.