Generating product images with AI takes four steps: upload a clear product photo, define visual style, generate variants, and export at the resolution your platform requires.
Input quality is the biggest factor in output quality. Spend a few minutes preparing your product photo.
This is where catalog consistency is won or lost. Lock lighting, scene, framing, and color grading before scaling. This enables product image consistency.
Generate variants, review outputs, and regenerate selectively. For catalog-scale work, batch generation is faster than one-by-one production.
CatalogLab creates SKU-based square exports ready for PIM or storefront upload. Review catalog workflows in the case studies.
Avoid over-styled defaults, inconsistent batches, weak input quality, skipping review, and mixing multiple tools in one catalog.
Single shots are useful for hero tests and ads. Bulk generation is best for catalog refreshes, marketplace expansion, seasonal drops, and PIM-driven workflows. Compare limits on pricing.
Upload a clean product photo, define visual style, generate variants, then export at the required resolution.
Use a sharp, well-lit product photo on a plain background with a small margin around the product.
For single products, 3-5 variants is a good start. For catalogs, generate the batch first and selectively regenerate misses.
Yes, if the tool supports locked parameters across a batch. CatalogLab is built for this.
Shopify commonly uses 2048 square, Amazon recommends 2000 square, and Etsy wants 2000px on the longest side.
A single product can take minutes. A 100-product CatalogLab batch is designed to complete in roughly 10 minutes.
Check logos, text, reflections, shadows, missing details, and color shifts before publishing.
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