GPT Image 2.5: When a Better Image Still Makes a Worse Workflow

GPT Image 2.5 is worth testing for precise edits, but an attractive first image does not establish that it can finish your particular editing workflow. The useful comparison is the approved image against its edited version, including the parts that were supposed to remain unchanged.

A room-building workflow found repeated edits less destructive than before while still getting excessive, repetitive micro-detail across the walls and floor of a post-apocalyptic brick basement. That separates two problems that are easy to confuse: improved edit stability can coexist with an unsuitable surface texture.

The texture problem also has conflicting triggers: some attempts became worse through successive edits, while fresh portraits and paintings showed swirly lines without any preceding edit. Treat restarting a conversation as an experiment, not a dependable cure; another set of attempts still failed after both repeated retries and new chats.

One recovery method converted the damaged picture into line art, then used that line drawing in a new conversation to rebuild a photorealistic image. Its important limitation was explicit: this workaround was used where the result did not need to preserve a specific person’s identity.

Direct cleanup instructions asking for fewer repeating patterns and preserved natural detail worked intermittently, rather than consistently. Longer prompts were not a universal answer either: carefully constrained examples looked convincing, while other attempts retained the same unwanted default style despite further prompting.

A supposedly simple outdoor park with a small kiosk repeatedly became a polished restaurant scene with lamps, wine and rain. Cold color grading was another isolated sticking point, even in a workflow whose operator otherwise liked the model’s anime output.

A template meant to ask clarifying questions instead printed those questions and answer lines on the poster itself. Making those words more legible would not solve that failure: the wrong conversational material had become the design.

Requests for a larger image produced another mismatch: in one attempt the picture became sharper while the actual dimensions stayed unchanged. A follow-up intended as image processing could also trigger another generation, prompting the explicit instruction “DO NOT CREATE AN IMAGE”.

A satisfactory result from Sunburst on high in a different interface cannot establish that the same prompt would improve inside the default chat experience. Likewise, apparent speed improvements and stronger text rendering should be assessed separately from texture defects, anatomy and repeated-edit reliability.

For a deliverable, keep the last approved image and reject an edit that improves the requested detail while damaging something else. When another cleanup pass only trades one defect for another, returning to the source or repairing a small area outside the generator is a more useful next test than extending the same edit chain indefinitely.