Nano Banana 2 Workflow: Integrating External References for Plant Morphology

Creating accurate botanical illustrations or scientific visualizations requires more than just a descriptive text prompt. When the goal is to replicate specific plant morphology, relying solely on textual descriptions often leads to generic results that miss critical structural details. This guide outlines a practical workflow for using Nano Banana 2 to integrate external reference images, ensuring your generated outputs remain faithful to the source material without triggering unsupported features.

It is important to clarify that Nano Banana refers to the AI image generation and editing tool discussed here. It is not a skincare brand, bottle, jar, or physical subject. The following steps are designed for end users looking to leverage the tool's capabilities for scientific or artistic accuracy.

Selecting the Right Model for Reference Integration

The first critical decision in this workflow is selecting the appropriate model variant within the Nano Banana ecosystem. 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 models with different optimization goals.

For workflows involving external references and detailed morphological accuracy, Nano Banana 2 Lite should be approached with caution. 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, recommending it for complex reference integration without explaining these limitations would be inaccurate. Instead, users aiming for high-fidelity plant structures should prioritize Nano Banana 2 or Nano Banana Pro, which offer better alignment with the requirements of integrating external visual data.

Step-by-Step Workflow for Single-Reference Integration

To achieve accurate plant morphology, you must adhere to a strict single-reference protocol. While the desire to upload multiple photos of a plant from different angles might seem logical, the current system does not support unsupported multi-reference features. Attempting to force multiple uploads can lead to unpredictable results or errors. The following workflow ensures the output remains faithful to the source material by focusing on one primary reference image at a time.

Inputs Required

Before opening the generator, gather the following assets:

• Primary Reference Image: A clear, high-resolution photograph of the target plant species showing the specific morphology you wish to replicate (e.g., leaf venation, stem texture, flower structure).
• Descriptive Text Prompt: A concise description of the desired scene, lighting, and composition, explicitly referencing the visual traits seen in the reference image.
• Model Selection: Ensure you have selected either Nano Banana 2 or Nano Banana Pro based on the complexity of the task.

The Process

1. Navigate to the Generator: Access the Nano Banana 2 interface via the product page at /nanobanana2. This platform supports both text-to-image and image-to-image workflows.
2. Upload the Reference: In the image-to-image section, upload your single primary reference image. Do not attempt to add secondary images. The system will use this single anchor to interpret the morphological constraints.
3. Craft the Prompt: Enter your descriptive text. Remember that prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. Be specific about the biological features you want preserved, such as "five-lobed leaves" or "spiral phyllotaxy," rather than vague terms like "looks real."
4. Generate and Review: Initiate the generation process. Observe how the model interprets the reference image combined with your text.

Checkpoints and Validation Strategies

Once the image is generated, you must validate the result against your original reference. This validation phase is crucial for maintaining scientific or artistic integrity.

• Morphological Consistency: Compare the leaf shape, stem thickness, and overall growth habit of the generated image against the uploaded reference. Does the new image capture the specific curvature or texture? If not, refine your text prompt to emphasize those specific traits before regenerating.
• Single Source Fidelity: Verify that the output has not drifted into a generic representation of the plant family. If the image looks too similar to other plants, your prompt may have been too broad, diluting the influence of the single reference image.
• Limitation Awareness: If you find yourself needing to combine features from two different reference images, acknowledge that the current workflow does not support this natively. You may need to create separate generations for each feature set or manually composite them in post-production software outside of Nano Banana.

Export and Use Steps

After achieving a satisfactory result, the final step is exporting the asset for your intended use. Since Nano Banana 2 supports standard image generation workflows, you can download the resulting file directly from the interface. Ensure you save the file in a format compatible with your downstream applications, whether that be a presentation, a publication, or a design project.

Remember that the prompt library offers example prompts that users can copy or take into the generator. However, these examples are untested for your specific plant species and should be treated as starting points rather than guaranteed solutions. Always adapt the language to match the unique characteristics of your subject.

By following this structured approach, you can effectively utilize Nano Banana 2 to generate images with accurate plant morphology. This method respects the model's architectural limits while maximizing its potential for detailed visual synthesis. For more information on the underlying technology, you can visit the Try Nano Banana link to explore the tool's full capabilities.

This workflow ensures that your creative or scientific projects benefit from AI assistance without compromising the accuracy required by your specific subject matter.