Why does image drift happen in AI editing?

Understand why edits sometimes change more than requested, and how to reduce it.

“Image drift” is when an editing model changes parts of the image you did not ask to change (faces shift, backgrounds mutate, details move).

Common causes

  • The prompt contains too many edits at once.
  • The target object is ambiguous (the model guesses).
  • The instruction is “high-level semantic” (style/scene changes) while you expected a surgical edit.
  • Missing constraints about what must remain unchanged.

How to reduce drift

1) Make one clear edit per run. 2) Add explicit preservation constraints (“keep X unchanged”). 3) Name the target region precisely (left/right, foreground/background). 4) Avoid mixing identity edits with global style changes. 5) If needed, split the task into steps.

A good drift-resistant template

  • [Do the edit]. Keep the rest of the image unchanged. Do not change [protected elements].
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