Nano Banana 2 Iterative Panel Size Refinement Workflow

Architectural design often begins with a conceptual sketch that captures the essence of a building but lacks the precise dimensional data required for construction. When working with modular facades, the challenge lies in maintaining the original design intent while systematically adjusting panel sizes to meet specific structural or aesthetic requirements. This is where the iterative workflow within Nano Banana 2 becomes an essential tool for designers. By leveraging multi-turn capabilities, users can engage in a dialogue with the AI to refine dimensions without losing the core visual identity of the initial concept.

This guide outlines a step-by-step approach to transforming rough architectural drawings into detailed specifications. The process focuses on sequential editing, allowing you to make incremental adjustments to panel scale and proportion. It is important to note that while this workflow is powerful, it relies on the specific capabilities of the model used. For tasks requiring multiple reference inputs or complex sequential editing, standard Lite versions may not be sufficient due to their focus on speed rather than deep iterative refinement.

Starting Your Workflow: Inputs and Initial Setup

The foundation of a successful refinement workflow is the quality of your initial input. To begin, you need a clear starting point that represents your rough sketch. This could be a hand-drawn diagram, a basic digital rendering, or a low-fidelity image showing the general layout of your modular panels.

Required Inputs:

1. Base Image: A clear image of your initial facade concept. Ensure the lines defining the panels are visible, even if they are rough.
2. Design Intent Description: A brief text summary explaining the style (e.g., "modern minimalist," "industrial brick") and the primary goal (e.g., "increase panel width by 20%" or "align grid to 3-meter modules").
3. Target Dimensions: Specific measurements you aim to achieve, such as height-to-width ratios or total facade coverage.

Before generating any images, ensure you are accessing the correct version of the tool. While Google documents various models like Gemini 3.1 Flash Image and Gemini 3 Pro Image, the iterative nature of this task requires a model capable of handling multi-turn conversations effectively. Always verify that the platform supports these advanced editing features before proceeding. If you are unsure about the specific model capabilities available to you, consult the official product documentation at Try Nano Banana.

Step-by-Step Iterative Refinement Process

Once your inputs are ready, the workflow moves into the iterative phase. This involves a series of prompts where each turn builds upon the previous result. The goal is to adjust the scale of the panels gradually rather than attempting a massive change in a single generation.

Step 1: Establish the Baseline Upload your rough sketch and provide a prompt that asks the AI to interpret the current panel sizes. Do not ask for final precision yet. Instead, request a clean interpretation of the existing proportions. Example Prompt: "Interpret this sketch as a modern facade. Maintain the current rough proportions of the panels but clean up the lines."

Step 2: First Adjustment Review the output. If the panels are too small relative to the windows, use the new image as the base for the next turn. Provide a specific instruction to alter the scale. Example Prompt: "Using the previous image, increase the vertical height of the glass panels by 15% while keeping the frame thickness consistent."

Step 3: Precision Tuning Continue this loop. In each subsequent turn, introduce more specific constraints. You might ask to align the top edge of one row with the bottom edge of another, or to ensure all panels fit a specific module size. Example Prompt: "Adjust the horizontal spacing between panels so they form a perfect 3-meter grid. Ensure the aspect ratio remains consistent with the previous iteration."

It is crucial to remember that prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. Therefore, always review the generated image to ensure the design intent has not drifted. If the style changes unexpectedly, revert to the previous step or restate the original stylistic constraints in your next prompt.

Checkpoints and Finalizing Specifications

Throughout the process, you should pause at specific checkpoints to evaluate progress. These checkpoints prevent the accumulation of errors and ensure the design remains viable.

Checkpoint 1: Visual Consistency After every two or three iterations, compare the latest image against your original sketch. Does the overall character of the building remain the same? If the texture or material look has shifted significantly, you may need to reinforce the material description in your next prompt.

Checkpoint 2: Dimensional Accuracy Verify that the requested changes have been applied correctly. Are the panels actually larger? Is the grid aligned? Since the AI generates visual representations, it cannot provide exact CAD coordinates. However, you can visually estimate if the proportions match your target specifications.

Export and Use Steps Once the iterative process yields an image that meets your visual and proportional goals, you can export the result. This final image serves as a high-fidelity reference for your technical team. They can use it to extract precise measurements or to create detailed blueprints based on the refined visual data.

Remember that Nano Banana refers to the AI image generation/editing tool and is not a physical product or skincare brand. The results generated are visual aids intended to support the design process, not guaranteed construction documents. By following this structured workflow, you can efficiently move from a rough idea to a precisely scaled facade design, leveraging the power of sequential editing to maintain control over your project's evolution.