What is AI upscaling?

Traditional enlargement interpolates: to double an image's size it invents each new pixel by averaging its neighbours. The result is bigger but no sharper, because no new information was added. AI upscaling instead uses a model trained on pairs of small and large images, which has learned what fine detail typically looks like at higher resolution, and generates it.

That distinction matters because the added detail is invented, not recovered. An upscaler looking at a blurry face produces a sharp, plausible face, but not necessarily the face that was there. For generated art this is usually fine and often desirable. For anything evidentiary, a document, a licence plate, a photograph of a real person, it is actively misleading.

The most common reason to upscale generated images is print. Screen use needs very little: 1024 by 1024 is more than enough for a phone wallpaper or a social post. Print at 300 DPI needs roughly 2500 by 3300 pixels for A4 and around 5400 by 7200 for A2, well beyond what any generator outputs directly, so upscaling is a required step rather than an optional one.

Dreamify does not include a built-in upscaler; SDXL outputs at 1024 by 1024, which covers screen use comfortably. For print, run the finished image through a dedicated upscaler afterwards. Generating at the highest resolution available first always beats upscaling a small image aggressively, because the upscaler has more real information to work from.