Can Sunburst generate images directly from text?
Yes. Submit model=gpt-image-2.5-sunburst and prompt to /openai/images/generations to begin text-to-image generation. Describe the subject, environment, lighting, and composition, and clearly state whether text is needed; if an existing image needs to be modified, use the editing endpoint instead.
How can I make reference image edits more closely match the original design?
Submit the image and editing instructions to /openai/images/edits, and explicitly specify the Sunburst ID. The prompt should separately describe the modification target and content to preserve, and multiple reference images should also indicate their respective purposes. Focus on a clear target in each round, and compare with the original image to check subject and brand details.
How should Sunburst masks be prepared?
The mask workflow uses gpt-image-2.5-sunburst:official, with both the original image and mask uploaded via multipart. The mask must be a PNG with an Alpha channel, match the dimensions of the first original image, and not exceed 4MB; transparent areas may be modified, while images containing only black-and-white colors without a transparency channel cannot be used as a substitute.
Can I generate multiple images at once and return Base64?
The platform allows n to be set from 1–10, which is suitable for obtaining multiple candidate images; however, response_format=b64_json supports only 1 image. Use URL responses when multiple results are needed, and make a single-image request when directly processing Base64 image data; do not mix the two settings.
Will multi-round editing automatically remember previous images?
You should use the selected image as the input for the next edit and specify the changes and preservation requirements for the current round in the new instructions; do not rely only on previous requests. Long tasks can include callback_url to first obtain task_id and receive results when completed; save images and instructions from each round for comparison and rollback.
