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Design Tools

Image Upscaling

Enlarge low-resolution images while adding plausible detail, with before/after preview and target scale. Honest comparison before export for print, display, and crop workflows.

Interactive demo

Image Upscaling
Original (256x256)
→
Upscaled (1024x1024)

Overview

How might we design image upscaling so people can trust and act on AI output?

When to use

  • Perfect for designers, photographers, and content creators who need to enhance image quality and resolution for print, displays, or professional use.

When to skip

  • Source is already larger than the delivery target.
  • Faces or text need forensic accuracy, not plausible detail.
  • Batch cost is unchecked on huge source files.

Rules

  • Upscale with no side-by-side or slider comparison.

  • Inventing faces or text that were not in the original.

  • No max dimension guard on upload size.

  • Calling upscale “enhance” with no scale factor shown.

Evidence

ProductImplementation
Topaz GigapixelDrag slider before/after with scale and model presets.
UpscaylLocal batch upscale with model picker and preview.
Real-ESRGANOpen models used in pipelines with 2x/4x targets.
Adobe FireflyEnhance detail in generative and edit flows.

FAQ

What is AI upscaling in product UX?

The user picks a target size or scale; the model predicts detail to make the enlargement look sharp instead of blurry.

Should users pick a model?

Power users yes, with presets for photos vs anime vs text-heavy images. Default one good general model for everyone else.

How honest should previews be?

Show true output resolution and warn when detail is synthesized, especially on faces and logos.

Upscale vs inpainting?

Upscale changes resolution globally. Inpainting edits a masked region at any resolution.