To reduce unwanted changes in GPT Image, describe the one thing you want changed, list the specific details that must stay the same, and edit in small steps. For faces, name identity and appearance details; for text, quote the exact wording and specify its placement and style. These instructions improve control, but they cannot guarantee that every unrequested detail will remain unchanged.
Give GPT Image a narrow, explicit edit
State the target and the change separately from everything that must be preserved. A broad request can invite changes across the image; a specific one makes your intent clearer.
- Name the person, object, wording, or region you want changed.
- Say “change only” that target, then list the details that should remain unchanged.
- Make one change at a time and inspect the result before asking for another edit.
Replace “keep everything else” with details suited to the image. For a portrait, that might mean identity, facial features, expression, hairstyle, skin tone, body shape, pose, and proportions. For a product image, it might mean the product’s shape, labels, layout, lighting, camera angle, and surrounding objects. Do not include a preservation instruction that conflicts with the change you want—for example, do not ask to change the lighting while also requiring the lighting to stay fixed.
For a face or portrait, list the features to preserve
When changing clothing or another part of a portrait, explicitly say what should remain consistent. For example:
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Change only [specific clothing item or area]. Preserve the person’s identity, exact facial features, expression, skin tone, hairstyle, body shape, pose, and proportions. Keep the background, framing, camera angle, lighting, and all other objects unchanged. Do not add text, logos, or other elements.
Adapt the list to the requested edit: remove any constraint that would prevent the intended change. Treat this as a prompt pattern, not a guarantee of pixel-for-pixel preservation.
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For lettering, specify the exact wording and location
Put required text in quotation marks and describe where it belongs and how it should look. Ask for no additional text. For example:
Change only the wording in [specific location] to “EXACT WORDING”. Keep the label’s position, size, color, typography, surrounding layout, and all other image details unchanged. Add no other text.
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For an unusual spelling or brand name, spell it out letter by letter. Then check both spelling and legibility in the final image. OpenAI recommends medium or high quality for small text, dense information, or designs with multiple fonts; inspect the result at the size at which it will actually be used.
Use area selection as guidance, not a hard boundary
In ChatGPT Images, you can select an area and describe the edit in your prompt. Mention the selected target in words as well as selecting it. OpenAI cautions that a highlight may not precisely contain an edit, and changes can extend beyond the selected area. Do not assume that pixels outside the selection are locked.
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Iterate and check for drift
Review each result against the preservation details you listed. If a face, label, layout, or other element has drifted, request a targeted correction and repeat the critical preservation instructions in that follow-up. Small, single-purpose follow-ups make it easier to identify what changed, but may still alter other details.
- For portraits, check identity, facial features, expression, hair, skin tone, pose, and proportions.
- For text, check exact spelling, placement, and legibility.
- For diagrams and products, check labels, relationships between elements, and product shape.
- For any image, check whether anything beyond the requested edit changed.
Choose model settings for the workflow you are using
OpenAI’s current image prompting guide describes GPT Image 2.5 Flare as optimized for speed and GPT Image 2.5 Sunburst as optimized for quality, and says both improve precise editing and subject preservation. This is vendor guidance, not an independent benchmark showing that one model preserves faces or text better in every case. Compare results on images representative of your own task and inspect the output.
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Fidelity controls are model-specific. For the older GPT Image 1 workflow, OpenAI documents input_fidelity="high" as useful for distinctive details such as faces and logos. The current guide says not to provide this parameter for GPT Image 2 because those inputs are processed at high fidelity. Do not treat the setting as a universal control; consult the current instructions for the model and workflow you are using.
If you are editing through the API, the image edit API reference documents prompt-based editing or extending of supplied images, including supported input formats and limits. Those technical details can change, so check the reference for the current model requirements before building an integration.
