Why Restore Photo matters in real workflows
AI face restoration is the engine; your QA discipline is the steering wheel. The trap with Restore Photo is overconfidence: over-smoothing skin texture and inventing facial features that were not in the source, and reviewers must catch it before publication. Newsrooms and policy researchers must be careful: Restore Photo is illustrative, not evidentiary. Where the channel allows it, attribute the photo as 'AI-restored' so audiences interpret the result correctly. Always compare the input and output side by side at zoom; reject any case where over-smoothing skin texture and inventing facial features that were not in the source drifts the output away from documented reality. The honest sales pitch: Restore Photo saves hours, but only when you trust your reviewer process more than the model.
How to use Restore Photo: a 3-step playbook
- Open Restore Photo and decide your spec up front: target output (format/size/quality), naming convention, and which destination this run feeds.
- Run the conversion or edit, then sample-review the first 5 outputs at native resolution before committing the rest of the batch.
- Validate on the actual destination surface (CDN, reader, channel) and archive both source and output with version metadata for rollback.