Nano Banana 2 Tutorial: Isolating Bottle Shadows for Clean Compositing

Understanding Shadow Isolation in AI Image Workflows

Creating a clean separation between an object and its cast shadow is a common challenge in digital compositing. When working with AI-generated imagery, the model often blends these elements together, making it difficult to adjust lighting or move the object without affecting the ground plane. Nano Banana 2 offers a robust environment for tackling this specific task through its image-to-image and text-to-image capabilities. By leveraging precise prompt instructions, users can guide the generation process to produce distinct visual layers that are easier to manipulate in post-production software.

It is important to clarify that Nano Banana refers to the AI image generation and editing tool itself. It is not a skincare brand, nor does it depict physical bottles or jars as its primary function. The examples used in this guide are generic and unbranded to focus on the technical workflow of isolating shadows rather than promoting specific products. While the tool supports complex workflows, users should understand that prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. This distinction is crucial when attempting to isolate specific visual elements like shadows while maintaining the integrity of the main subject.

Setting Up Your Prompt for Layer Separation

To achieve the goal of isolating shadows, the approach requires a clear directive within the prompt library. Users can copy example prompts from the generator or adapt them to their specific needs. The core strategy involves explicitly asking the model to render the shadow as a separate entity or to generate the scene with a high degree of contrast between the object and its shadow. For instance, a user might instruct the AI to "generate a glass bottle with a soft, detached shadow layer" or "create an image where the shadow is rendered separately from the bottle body."

When crafting your request, remember that Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image). This specific model family is designed to handle such generative tasks. However, if you are considering using Nano Banana 2 Lite, which corresponds to Gemini 3.1 Flash Lite Image, be aware of its limitations. 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, for a task requiring iterative refinement to perfect shadow isolation, the standard Nano Banana 2 or Pro versions are more suitable. Do not recommend the Lite version for complex workflows without explaining this limitation regarding its lack of optimization for sequential editing.

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Step-by-Step Execution and Result Evaluation

Once you have defined your prompt, follow these numbered steps to execute the isolation process:

1. Navigate to the Nano Banana 2 interface at /nanobanana2 and select the image-to-image workflow if you are starting with a base photo, or text-to-image for a fresh generation.
2. Input your specific prompt describing the need for a separated shadow. Ensure the language clearly distinguishes between the bottle object and the shadow element.
3. Select the appropriate model variant. For best results in shadow isolation, avoid Nano Banana 2 Lite unless speed is the only priority and complexity is low.
4. Generate the image and review the output. Look for a clear visual boundary between the bottle and the shadow area.
5. If the result is not satisfactory, refine the prompt by adding descriptors like "high contrast," "clean edge," or "separate layer" and regenerate.

Judging the success of your result involves checking if the shadow appears distinct enough to be masked out or moved independently in external software. Since prompt instructions do not guarantee object preservation, verify that the bottle shape remains intact while the shadow has been isolated as requested. If the shadow is still too blended, try adjusting the prompt to emphasize the separation further.

Troubleshooting Common Issues

If the generated image fails to show a clear separation, consider the following fixes. First, ensure you are not relying on the Lite version for this specific task, as it may lack the nuance required for multi-step visual adjustments. Second, check if the prompt was too vague; adding specific constraints about the background or lighting direction can help the model understand the spatial relationship better. Finally, remember that AI generation is probabilistic. You may need to run several variations to find one where the shadow is cleanly isolated. Always treat the generated images as examples of what is possible, rather than guaranteed final assets, especially when dealing with complex lighting scenarios.

By following these guidelines and utilizing the correct model features, you can effectively use Nano Banana 2 to facilitate easier compositing later in your workflow. This method allows for greater control over the final image composition without needing to manually paint out shadows in every instance.