A lightweight acceptance checklist for AI photo-editing outputs

Disclosure: We operate Cleanup Pictures and Photo Tea. This is a proposed manual QA workflow, not a benchmark, an independent review, or a claim that these checks are automated in either product. This article was drafted with AI assistance.

When reviewing an AI photo edit, “looks good” is an incomplete acceptance criterion. The changed region may look convincing while an unchanged label or edge drifts. Separate the requested transformation from the invariants.

Define the fixture

Use an image you have permission to modify. Keep the source immutable and record a short case before submitting it:

{
  "case": "product-background-change",
  "requestedChange": "neutral gray background",
  "preserve": ["product silhouette", "label text", "product color"],
  "review": "manual side-by-side"
}

This is illustrative test data, not an API schema for either website. Record the prompt or brush selection, the output, and the actual outcome so a later attempt has a meaningful baseline.

Choose a narrow operation

For a local distraction, Cleanup Pictures provides an upload-and-brush cleanup workflow. Inspect the selection boundary and any line crossing it. Reconstructed pixels are generated estimates rather than recovered ground truth.

For a broader change, Photo Tea provides prompt-based editing and image generation. State the change and preservation constraints separately. The tools are independent; this article does not describe an integration between them.

Separate review outcomes

• Requested change: did the intended edit happen?
• Invariants: did text, subject geometry or identity change?
• Local artifacts: do edges, shadows or repeated textures break?
• Intended display: does the result work at the actual size and crop?

Use pass, fail or uncertain for each check. A pleasing overall image should not override a failed label-text check when the asset is for a product listing. Uncertain is useful when the source itself is ambiguous.

Retry without losing the baseline

If a constraint fails, return to the source and narrow one instruction or selection. Avoid stacking edits on an already-invalid result. Keep the case and output linked in your own notes; do not infer a performance rate from a single successful example.

This process is deliberately manual. It gives a small team explicit review criteria without inventing precision. Check current plans, upload conditions and usage rights on each website before using private or commercial material.