Bulk Image Upscaler Workflow with an Application Queue
Plan a bulk image upscaler workflow around individual API predictions, source categories, review, and storage without assuming native batch upload.
Upscale an image in 3 steps
Choose the delivery target first, enlarge one representative source, and inspect the exported file before repeating the workflow.
Upload the source
Use the clearest authorized image and record the details that must not change.
Choose the target size
Select the smallest resolution and format that satisfy the final placement.
Inspect and export
Compare text, faces, edges, and texture before approving the high-resolution file.
Segment the folder before processing
Do not apply one acceptance rule to unrelated sources. Use one pilot batch of product packages with readable labels as the running case. Include an easy source, a typical source, and the poorest file still intended for use within that same batch. Record original dimensions and the destination's size requirement. The upscaler endpoint accepts image, target resolution, and output format. Its 2K, 4K, and 8K choices do not imply every file should receive the same target. Assign settings by destination or asset category, then reject a source that cannot meet the brief without altered content.
Own the queue and its recovery path
Create a job record containing asset ID, source version, requested target, format, and reviewer state. Submit one prediction per image, retain the returned ID, and query its result. Handle timeouts, failed predictions, and duplicate submissions without losing the source-to-output link. Move completed files into your own storage rather than relying on a temporary model result as an archive. The WaveSpeed FAQ should be checked for current throughput and submission guidance. Do not label an application-built queue as a native multi-file upload. If a single request fails, retry that item within a controlled policy instead of replaying an entire collection blindly.
Review by risk, not only by count
Automated checks can verify dimensions, file type, missing outputs, and duplicate assets. Human review still matters for small text, identity, product shape, and generated texture. A sample may pass visually but a different category may fail. Keep rejection reasons in a structured field so the team can adjust its source rules or choose a manual edit. Estimate workload using successful jobs, retries, and correction time. A run with many rejected exports is not efficient merely because submission is automated. The single-image guide is a useful approval baseline.
Release only approved files downstream
Create an explicit boundary between an API result and a publishable asset. The CMS or product catalog should pull only approved versions; a failed or unreviewed result remains in staging. Store the original and final export together, and record who approved sensitive changes. This makes a large run traceable even when several teams touch the same folder.
Continue the workflow
FAQ
Is there a native multi-image upload in the standard endpoint?+
The published standard reference describes individual image predictions. Build and operate your own queue for a collection.
What belongs in each job record?+
At least the source asset ID, version, target resolution, output format, prediction ID, result location, and review state.
Can all images share one visual check?+
No. Labels, portraits, illustrations, and reflective objects have different failure points.
When should the queue publish an output?+
After the file passes technical checks and any required visual approval, not simply when a prediction completes.