WaveSpeed AI Logo
imggen ai image upscalerupscale imageai

ImgGen AI Image Upscaler vs WaveSpeed API

Compare ImgGen AI image upscaler request fields with WaveSpeed's target-resolution API. Map inputs, formats, result handling, and review before migrating.

ImgGen AI Image Upscaler vs WaveSpeed API
02

Upscale an image in 3 steps

Choose the delivery target first, enlarge one representative source, and inspect the exported file before repeating the workflow.

IMAGE WORKFLOW
1

Upload the source

Use the clearest authorized image and record the details that must not change.

2

Choose the target size

Select the smallest resolution and format that satisfy the final placement.

3

Inspect and export

Compare text, faces, edges, and texture before approving the high-resolution file.

Section 01

Translate the request, not just the field names

ImgGen's documented `factor` is a magnification instruction. WaveSpeed's `target_resolution` offers published 2K, 4K, and 8K choices. These are not interchangeable units. For each source size, calculate the intended output and choose the nearest appropriate WaveSpeed target. Do not copy `factor=4` into a field that expects `4k`. ImgGen also documents `enhanceFace` and `sharpen` switches. The standard WaveSpeed upscaler does not list those controls. If the old workflow depends on either, test the resulting pixels or retain a separate editing stage. No migration plan should imply hidden one-to-one controls.

Section 02

Change response handling deliberately

The ImgGen example returns encoded image content in its result. WaveSpeed uses a prediction submission and result retrieval path. Adjust client code for asynchronous state, returned output location, error handling, and storage before changing production traffic. Keep a correlation ID that ties the source asset to the output and its review decision. Test with one product package bearing small typography and a reflective edge. Save both outputs from the same original; comparing files processed from different source sizes can create a false performance story.

Section 03

Define pass criteria before a pilot

For a migration pilot, record output dimensions, file format, edge changes, labeling accuracy, and reviewer time. A technically valid response can still be unsuitable for regulated copy or catalog markings. Have a fallback route when an image fails approval. Do not publish invented benchmark numbers from an unrun comparison. The image API collection is useful for exploring adjacent options, but each model has its own schema. Map only fields documented for the standard endpoint in the current integration.

Section 04

Roll out by source category

If the pilot passes, migrate one class of assets at a time and keep the old output path until reviewers accept the new one. A queue can track individual predictions, as described in the bulk workflow guide. That queue belongs to your application. Log unexpected output sizes and inspect them before increasing traffic.

Related Pages

Continue the workflow

FAQ

Is ImgGen's scale factor the same as a 4K target?+

No. A multiplier acts on source dimensions; a target-resolution option names an output class. Map expected dimensions before migration.

Can I carry over ImgGen's sharpen switch?+

Not into the standard WaveSpeed request as a like-named control. Its published fields are image, target resolution, and output format.

Will the response parser stay the same?+

No. Plan for WaveSpeed's prediction and result flow rather than assuming the ImgGen example's encoded image response.

What should the pilot include?+

Include varied source types, output-size checks, visual approval, error handling, and a rollback path.

Ready to Experience Lightning-Fast AI Generation?