Image Editing Workflow
Distinguish the legacy DALL·E generation and editing interfaces. The source describes DALL·E 3 as generation-only, DALL·E 2 as supporting edits/masks, and GPT Image as the newer image workflow. Confirm current model availability before maintaining an integration.
#Edit or regenerate?
Editing suits a background replacement, a local element change or a task that must preserve composition and brand imagery. If most of the image should change, regeneration may be simpler.
#Editing sequence
- Identify elements that must remain.
- Specify the regions to change.
- Describe the intended change concretely.
- Review edges, materials and subject consistency.
#Prepare masks correctly
The source notes matching image/mask size and format, an alpha channel in the mask, and the fact that a GPT Image mask guides an edit rather than guaranteeing a perfectly hard boundary. Check the selected endpoint's precise requirements.
#Common failures
An unspecified preservation requirement can change the subject. Too many simultaneous edits can shift the whole style. Inspect seams, faces, logos and product details for distortion.
#Migration approach
For detail-sensitive work, evaluate supported fidelity controls such as input_fidelity in the relevant GPT Image model. Keep an existing legacy flow available for its remaining tasks, evaluate new work on the newer supported route, and test high-fidelity edits before migrating them. Do not assume a model change preserves results automatically.
