Generate Images With GPT Image 2: Six Common Mistakes and Their Fixes

Generating images with GPT Image 2 rarely fails because the model is weak. It fails because the prompt leaves the model a decision you did not mean to hand over: the surface a product sits on, the colour of a jacket between two edits, the size of the words on a poster. OpenAI's own guidance names where GPT Image 2 still needs help — long complex prompts, precise text placement, recurring characters and brand elements that drift between separate generations, and layouts where elements must land in an exact spot. Every one of those is a decision point, and every one is settled by a prompt habit rather than by the model. This guide names the mistakes that turn a clear request into a round of redos, and pairs each one with the corrected pattern you can paste into the GPT Image 2 tool on KOOX AI.

Mistake 1: Briefing a Mood Instead of a Deliverable

The most common failure happens before the model draws anything: the prompt describes a feeling, not an asset. "A cool futuristic speaker, cinematic, high detail" tells GPT Image 2 what you like, not what it is building. PixVerse's prompt guide calls the fix "name the job before the style" — start with the output type, such as a product ad, a poster, a character sheet, a UI screen, or a first frame for video, because a prompt assembled from style adjectives alone leaves the success standard undefined.

Compare the two briefs:

A cool futuristic speaker, cinematic, high detail.

Create a premium product ad for a matte black wireless speaker. It must work as a 16:9 campaign banner with the product on the right, a short headline area on the left, clean negative space, and sharp product edges.

The second prompt tells the model how the result will be judged — by layout and usability, not only by beauty. PrompTessor's guide makes the same point from the writing side: begin with a definition of an accepted asset, because "premium cinematic image" is a mood while "4:5 paid-social product photograph with a copy-safe region" is a specification.

Mistake 2: Naming the Change but Not the Preservation List

Editing prompts fail when they describe only what should move. A preservation list can matter as much as the requested change, because without one the model is free to redraw anything it decides is related. Felo's prompting guide packages this as the change-only formula — state the change, then list what must stay.

Replace the parked car with a vintage bicycle. Preserve the house, fence, driveway, landscaping, lighting direction, camera angle, and time of day exactly. Match the bicycle's scale, contact shadow, and perspective to the existing scene.

Three sentences, three jobs: the edit, the lock, and the physical realism. The same structure travels to KOOX's image-to-image tool, where a scene-and-preserve instruction keeps the subject fixed while the surroundings change.

Mistake 3: Uploading Reference Images Without Assigning Roles

When several images go in, the model needs to know what each one is for. "Use the references" gives GPT Image 2 room to blend everything together; assigning a role to each image removes that ambiguity. This is the reference-handling rule that Felo's guide sums up as roles, not vibes, and PrompTessor shows it in practice:

Image 1: primary product identity. Preserve geometry, label, cap shape, materials, and proportions.
Image 2: lighting reference only. Use its soft source and controlled shadow contrast. Do not copy its subject.
Image 3: composition reference only. Use its left-weighted placement and negative-space ratio. Do not copy its colours or text.

A quieter mistake rides along with this one: building a reference set one image at a time. Flick's GPT Image 2 guide warns that separate prompts drift, and that a coherent batch — generated together from the same invariant block — produces a sheet that agrees with itself instead of eight near-misses.

Mistake 4: Trusting the Prose to Render Exact Text

Text is where a prompt stops being descriptive and becomes a locked asset. If words must appear, quote them, say how many times they should appear, and say what must not be added. "A slogan about speed" invites the model to invent copy; a quoted line with a placement instruction does not. PixVerse's guide recommends naming the headline verbatim and adding constraints such as "exact text only", "no extra words", and "no duplicate text", and it flags the cases to treat as review-gated rather than prompt-gated: exact brand-logo reproduction, tiny compliance copy, and proprietary typefaces. Morphic's model notes confirm why — OpenAI still lists text placement and clarity as a known limitation, so a dense block of small type should be checked at final display size rather than assumed from a thumbnail.

Mistake 5: Changing Several Things at Once

Iteration is where drift compounds. If one attempt changes the lighting, the background and the copy together, you cannot tell which instruction worked, and the next round starts from an image whose differences you did not choose. Felo's editing rules are blunt: change one thing per turn, and feed the previously approved output back in rather than re-describing the scene from scratch. Flick's guide adds the second half of the habit — repeat the invariants every time, because a prompt that says "navy bomber" once and "blue jacket" later invites the model to treat them as different objects. Models drift when the constraints stop being restated.

Mistake 6: Leaving Size, Quality, and Background in the Prompt

Asking for "4K" or "high quality" in prose is a suggestion; setting the parameter is a guarantee. Felo's guide keeps a separate section for the settings that should not live in the sentence, and PrompTessor's advice is to keep model, quality, size, background, and output-format choices apart from the prose. A practical rule follows: describe what the image should contain in the prompt, and express how large, how sharp, and with what background through the controls. The same discipline prevents the reverse problem — inflating the prose with parameter language that the model reads as an aesthetic request instead of a technical one.

Why Pixel-Identical Regions Need Compositing, Not a Prompt

One limit deserves stating plainly, because it explains a whole class of disappointing edits. Repeated edits can change details you meant to preserve, and where a region has to stay pixel-identical, the documented guidance is to composite the approved edit back into the original rather than relying on prompting. That is not a flaw in your wording; it is the boundary of what a generative edit can promise. If a logo, a legal line, or a product label has to be exact, place it with a deterministic editor after generation instead of asking the model to hold it.

A Fix Checklist Before You Generate

Run these six questions against any GPT Image 2 prompt before you spend a generation:

1. Deliverable — does the first line name the output type and where it will be used?
2. Preserve list — for an edit, is every element that must not change written down?
3. Reference roles — does each uploaded image have a job, and are non-transferable traits excluded?
4. Text lock — is required copy quoted, placed, counted, and fenced with exclusions?
5. One variable — if this is an iteration, does it change exactly one thing from the last approved image?
6. Settings — are size, quality, and background in the controls rather than the sentence?

Six questions, six chances to avoid a redo. The pattern behind all of them is the same: every ambiguous phrase is a decision the model will make for you, and a decision you did not specify is one you will probably reject.

Put the Fixes to Work

Start with one asset you actually need — a product still, a poster, a character sheet — and write the brief in the order above. Generate a draft, audit it against the six checks, change one slot, then compare. Once the loop feels routine, the same structure carries to the neighbouring tools: build a still with the text-to-image generator, refine it with image-to-image, and keep each accepted step as the input to the next. A prompt that reads like a brief rather than a wish is the difference between one generation and six.