Product Catalog Image Generation: Resolution, Upscaling, and Trust Boundaries

Description and use

Separating product image generation and upscaling enables caching of base images, reduces repeat generation costs, and clearly defines trust boundaries. Infrai is used for generation and scaling, with OpenAI as an example.

Problem

Generate high‑quality product images for an e‑commerce catalog while separating creative generation from display‑size enlargement to avoid repeated expensive generation and to maintain clear trust boundaries.

Outcome

Separating generation and upscaling reduces repeated generation costs, clarifies trust boundaries, and allows selective use of AI super‑resolution when needed.

Workflow

• Generate base image from product description using an image‑generation endpoint (e.g., Infrai).
• Evaluate the base image against catalog requirements and approve it.
• Cache the approved base image with metadata (provider, operation, request ID).
• When a larger display is needed, request a 2x or 4x upscale of the approved base image via the upscale endpoint.
• Store the derived image with its own metadata (factor, provider).
• Review and retain or delete artifacts according to their ownership and retention rules.

Evidence

Short answer: generate the product image at its native resolution first, then upscale the approved result to 2x or 4x when a hero slot, print export, or high‑DPI display needs it.

Source

The built‑in upscaler uses Lanczos interpolation only. It produces a clean, sharp enlargement, but it is not an AI super‑resolution model.

Source

I would make those boundaries explicit in the data model: source_description, base_image, and derived_image are separate records, each with an owner, retention rule, and provider identifier.

Source