An Amazon Listing-Image Playbook

Amazon enforces specific technical requirements for listing images, and violations result in suppressed listings. Before generating anything, understand the non-negotiable rules:

• Main image: pure white background (RGB 255, 255, 255), product occupies at least 85% of the frame, no logos or watermarks, no props, no text.
• Supplemental images: up to 6 additional images that can include lifestyle scenes, infographics, dimension diagrams, and feature call-outs.
• Resolution: minimum 1000px on the longest side to enable zoom functionality; 2000px or higher is recommended.
• File format: JPEG, PNG, GIF, or TIFF — JPEG is most reliable for colour accuracy.

Floniks gives you the tools to produce all of these programmatically. The main image requires careful background removal and isolation; the supplemental images are where creative storytelling drives conversion.

The main image is your most important asset — it is what appears in search results and determines whether a shopper clicks your listing at all. Options for generating a clean white-background main image in Floniks:

Option A — Direct generation: Use /ai-image with a prompt like "[product name] isolated on pure white background, studio product photography, soft shadow beneath product, sharp focus, no props, no text, high-resolution". This works well for products with clear geometric forms (electronics, bottles, boxes).

Option B — Background replacement workflow: If you have an existing product photo with a distracting background, use the background replacement workflow in /editor to strip the original background and replace it with pure white. This approach preserves the exact product appearance from your physical sample rather than relying on AI reconstruction of the product's details.

Always verify the final output: zoom in to the product edges and confirm there are no grey halos, missing fine details (like a thin handle or logo text), or colour shifts from the original product.

Infographic images combine a clean product visual with text call-outs highlighting key features. Floniks handles the visual generation; you add text in Canva, Adobe Express, or Figma:

1. Generate a 3/4-angle product shot using /ai-image with soft neutral background (light grey or pale gradient works well for infographics — pure white can make the text hard to layer over).
2. Export at 2000px width and import into your design tool.
3. Add 3–5 feature call-outs with connecting lines pointing to specific parts of the product. Use a clean sans-serif font in your brand colour.
4. Add a hero headline at the top or bottom ("200W Fast Charge" / "Leak-Proof Seal" / "BPA-Free Materials").

The most effective infographic images answer the top 3 objections that appear in your product's negative reviews. If reviews complain about durability, make a call-out that directly addresses material quality. If they ask about compatibility, show the connection port with a label. Turn your review analytics into your infographic brief.

Lifestyle images show the product in use by a real-looking person in a relatable setting. They convert hesitant browsers by answering "what would my life look like with this product?" Use /ai-image to generate lifestyle scenes:

"A woman in her early 30s sitting at a modern kitchen counter, holding a stainless steel water bottle, casual morning sunlight from a window on her left, warm and cosy atmosphere, shallow depth of field, lifestyle photography"

Key principles:

• Match your target demographic in the lifestyle prompt — age, setting, and context should mirror the buyer your listing is aimed at.
• Show the product in active use, not just held passively. A fitness tracker should be on someone's wrist during a workout; a desk lamp should be illuminating an actual work surface.
• Vary the setting across your lifestyle images — home, office, outdoors — to demonstrate versatility without making separate product claims.

For composite lifestyle images (AI-generated scene + your actual product inserted), generate the scene without the product first, then composite your real product in post. This ensures exact product accuracy while benefiting from AI's scene generation capability.

Shoppers frequently complain about receiving products that were larger or smaller than expected. A clear dimension diagram dramatically reduces this complaint and reduces returns. Floniks generates the base visual; you add measurements in your design tool:

• Flat-lay top-down product shot in /ai-image: pure white background, product perfectly centred, perfectly level camera angle (no perspective distortion). This becomes the base for overlaying dimension lines.
• Scale comparison visual: generate the product alongside a recognisable everyday item (a coin, a standard pen, a hand) to give shoppers an intuitive size reference even before they read the dimensions.
• Multiple units or pack sizes: if you sell a 3-pack or bundle, show all units in frame arranged cleanly. Generate this as a flat-lay composition prompt with "three identical [product name] arranged in a row, overhead view, white background".

Dimension and comparison images reduce returns and increase confidence in the purchase decision — both of which improve your seller metrics over time.

For sellers with multiple SKUs or product variants (different colours, sizes, or materials), producing individual listing image sets manually is impractical. The /editor workflow solves this:

1. Build a product listing image workflow that accepts a product image as input and outputs three standard formats: white-background main, lifestyle scene, and 3/4-angle infographic base.
2. Create a batch input list with one product image per SKU.
3. Run the workflow — each product flows through the same visual treatment and emerges with a consistent, professional set of listing images.
4. Review and approve outputs, then upload to Seller Central.

For colour variants of the same product, generate all colour versions in a single session using a colour-swap prompt strategy (generate the product once, then use the style transfer tool to re-colour it for each variant without re-generating the full scene). This produces visual consistency across variant listings while minimising generation time.