Nano Banana 2 Image-to-Image: Repairing Torn Document Corners with Natural Curvature

Restoring physical documents often involves more than just filling in blank spaces; it requires replicating the texture, lighting, and structural integrity of the original paper. When corners are torn or edges are missing, a simple fill-in approach can result in a flat, artificial look that clashes with the rest of the page. Nano Banana 2 offers an image-to-image workflow designed to handle these complex reconstruction tasks by understanding the context of the surrounding area.

The primary challenge in repairing torn corners is maintaining the illusion of continuity. A repaired corner must not only contain the correct text but also follow the subtle curve of the paper as it sits on a surface or within a binding. This tutorial outlines a structured method for using Nano Banana 2 to achieve these results without inventing new facts or guaranteeing specific outcomes.

Understanding the Prompt Structure for Document Repair

Effective restoration begins with a precise prompt structure. Unlike general image generation, document repair requires specific instructions regarding geometry and content preservation. The prompt should explicitly describe the desired outcome, such as "reconstruct the top-right corner of a scanned document," while acknowledging that the tool does not guarantee the exact preservation of labels or typography.

When crafting your input, focus on the visual attributes of the damage. Describe the type of tear (e.g., jagged, smooth) and the condition of the remaining paper (e.g., yellowed, creased). Crucially, you must instruct the model to align the new content with the document's natural curvature. This ensures the repaired section blends seamlessly rather than appearing pasted on top. Remember that prompt instructions describe desired outcomes but do not guarantee identity or object preservation. Therefore, the language used should be descriptive rather than prescriptive about specific characters or words that might be lost.

Step-by-Step Workflow for Reconstructing Edges

To successfully repair a damaged scan using Nano Banana 2, follow this logical sequence. First, upload the image of the damaged document into the Nano Banana 2 interface via the image-to-image workflow. Ensure the image clearly shows the intact portions of the document to provide sufficient context for the AI.

1. Upload the Source Image: Select the damaged document scan as your base image. Verify that the lighting and contrast are visible enough for the model to understand the paper texture.
2. Define the Inpainting Area: Use the masking tools to highlight the torn or missing sections. Be precise; including too much of the intact text can confuse the reconstruction process.
3. Enter the Descriptive Prompt: Input a prompt that details the need for edge reconstruction and curvature alignment. For example, "Reconstruct the missing top-left corner of a vintage document, matching the paper grain and following the natural curve of the page." Label any untested examples as examples if you are experimenting with variations.
4. Generate and Review: Initiate the generation process. Review the output to see if the text flows logically and the paper edge appears realistic.
5. Iterate if Necessary: If the first result lacks the correct curvature or texture, refine the prompt to emphasize the bending of the paper or adjust the mask size slightly.

This workflow leverages the capabilities of the underlying models, which are distinct from other versions like Nano Banana Pro or Nano Banana 2 Lite. While Nano Banana 2 supports these complex editing tasks, note that Nano Banana 2 Lite is focused on speed and cost and is not optimized for multiple reference inputs or multi-turn sequential editing. Do not recommend using the Lite version for this specific type of detailed repair work without explaining its limitations.

Judging Results and Fixing Common Issues

Evaluating the success of a repair involves checking both the visual blend and the textual coherence. A successful result will show a continuous paper texture where the torn edge meets the new content, with no harsh lines or sudden shifts in lighting. The text within the repaired area should appear consistent with the font style and ink density of the original document, though exact character preservation is not guaranteed.

If the result looks flat or disconnected, the issue often lies in the prompt's lack of emphasis on depth. Try adding keywords related to perspective, such as "slight shadow" or "curved surface." Another common issue is the introduction of gibberish text. Since the model does not guarantee typography preservation, you may need to generate the image multiple times or accept that some text may require manual correction after the AI pass.

For users looking to explore these capabilities further, Try Nano Banana provides access to the necessary tools and documentation. By following these structured steps and refining your prompts based on the visual feedback, you can effectively use Nano Banana 2 to restore the integrity of damaged physical documents.

Remember that while Google documents Nano Banana 2 as Gemini 3.1 Flash Image, the features available on this website are specific to the product path provided. Always refer to the official documentation for the most current model capabilities and limitations.