Nano Banana 2 Shoe Catalog: Batch Naming Convention Workflow for File Organization
Managing a large volume of generated product images can quickly become chaotic without a structured approach. When creating a shoe catalog using AI tools, the sheer number of files often leads to confusion during the final assembly phase. Implementing a systematic naming protocol is the most effective way to solve this. By embedding specific SKU data directly into filenames, you can drastically reduce the time spent on manual sorting and ensure that every image corresponds accurately to its product listing.
This guide outlines a start-to-finish workflow designed specifically for organizing shoe catalog assets. It focuses on how to prepare your inputs, construct a usable prompt strategy, and manage the output files efficiently. The goal is to create a predictable file structure that scales as your inventory grows.
Defining Inputs and Data Structure
Before generating any images, you must establish a clear data structure. The foundation of this workflow lies in the input variables you provide to the system. For a shoe catalog, the essential data points include the Stock Keeping Unit (SKU), the colorway, the style name, and the generation batch identifier.
Start by compiling a spreadsheet or list containing these details. Each row should represent a unique shoe variant. For example, a single entry might look like this: SKU: NB-SHOE-001, Color: Midnight Blue, Style: Runner X. This structured data will serve as the blueprint for your filename construction.
It is crucial to remember that Nano Banana refers to the AI image generation and editing tool used here; it is not a skincare brand, bottle, jar, or physical subject. The products depicted are generic and unbranded unless specified otherwise in your prompt. Ensure your input data reflects this distinction to avoid confusion between the tool and the content being generated. Having your data ready in a clean format allows you to focus on the generation process rather than scrambling to find missing information later.
Constructing the Prompt and Naming Logic
The core of this workflow involves translating your data inputs into a consistent naming convention within the prompt instructions. While prompt instructions describe desired outcomes, they do not guarantee identity, label, object, or typography preservation. Therefore, the naming logic should be applied externally or as a strict instruction for the output metadata if supported, rather than relying solely on the visual text within the generated image.
For this workflow, we recommend a standard format: {BatchID}_{SKU}_{Color}_{Style}.png. You can use the prompt library available on the platform to copy example prompts that align with your visual needs. These examples help users visualize the outcome but should be adapted to fit your specific catalog requirements.
Here is an example of how to structure your request. If you are generating a batch of shoes, your prompt might read: "Generate a high-quality studio shot of a sneaker. Style: Runner X. Color: Midnight Blue. Output filename reference: BatchA_NB-SHOE-001_MidnightBlue_RunnerX."
Note: The following is an example of how a prompt might be constructed for this workflow. Actual results may vary based on the model's interpretation. Do not assume the AI will automatically rename the file on your local machine without external scripting or manual intervention after download. The prompt guides the visual generation, while the naming convention ensures organizational clarity.
If you need to generate multiple variations, consider the capabilities of the specific model you are using. Google documents Nano Banana 2 as Gemini 3.1 Flash Image, Nano Banana Pro as Gemini 3 Pro Image, and Nano Banana 2 Lite as Gemini 3.1 Flash Lite Image. These are distinct models with different strengths. For instance, Nano Banana 2 Lite is focused on speed and cost. It is not optimized for multiple reference inputs or multi-turn sequential editing. Do not recommend it for complex workflows requiring multiple reference inputs without explaining this limitation. For a robust catalog workflow involving detailed consistency, Nano Banana 2 or Pro may be more suitable depending on your budget and quality needs.
Checkpoints and Export Steps
Once your prompts are defined and the generation process begins, you must implement checkpoints to ensure data integrity. After each batch of images is generated, perform a verification step. Compare the generated filenames against your original input spreadsheet. Look for discrepancies in the SKU numbers or color names. If a file is named incorrectly, correct it immediately before moving to the next batch. This prevents the accumulation of errors that are difficult to fix later.
When exporting your files, maintain the folder structure you established during the planning phase. Create a main directory for the catalog, then subdirectories for each batch or category. Move the renamed files into their respective folders. This step is critical for streamlining the catalog assembly process. With files organized by SKU and color, assembling the final catalog becomes a matter of drag-and-drop rather than searching through a disorganized pile of images.
Remember that the website has a Nano Banana 2 product page at /nanobanana2 which supports text-to-image and image-to-image workflows. However, the page named Nano Banana Lite at /nanobananalite does not by itself establish support for Google Nano Banana 2 Lite. Google model names and capabilities must not be presented as proof of availability or identical features on this website. Always verify the specific features available in your current session before committing to a large-scale generation task.
By following this systematic approach, you transform a potentially messy creative process into a streamlined production line. The combination of clear input data, logical naming conventions, and rigorous checkpoints ensures that your shoe catalog is professional, organized, and ready for publication. This method reduces manual sorting time significantly and allows you to focus on the visual quality of your products rather than administrative overhead.