For a healthtech catalog, use an image generation API to create the base product image at native resolution, then use upscaling to enhance resolution only when a large display or print output needs it. Keep the original and the enlarged derivative separate.

Short answer: use /v1/images/generations for the generated base and /v1/ai/image/upscale for a 2x or 4x derivative. This is a good production path when clean enlargement is enough. It is the wrong boundary for AI-quality super-resolution, because the built-in upscaler currently offers Lanczos interpolation only.

The constraint matters more than the endpoint names. A messy catalog description can produce a useful visual asset, but a larger canvas does not restore detail that was never present. In a healthtech workflow, I would also keep the source description, generation request, base image, and derivative linked by one catalog-item ID so an editor can replace one stage without regenerating everything.…