GPT Image 2: product visuals, poster drafts, and output verification
Prepare a GPT Image 2 trial using current mode requirements, verify text and reference details, and revise one factor at a time without speed or accuracy guarantees.
This guide uses the on-site GPT Image 2 product entry to explain preparation, output checks, and iteration. Its current display name is GPT Image 2 while the legacy URL remains. It does not promise fixed speed, batch size, text accuracy, reference fidelity, or commercial suitability.
Prepare the scene and acceptance criteria
Specify purpose, subject, composition, lighting, and exact copy. List shapes, colors, and marks that must remain separately. References must meet the current form requirements. Inspect text and logos directly rather than assuming accuracy from the model name.
Choose a mode by input
The current catalog lists text-to-image, image-to-image, and reference-to-image with prompt, image_urls, size, quality, and resolution fields. Required inputs, choices, and cost follow the actual mode form. A field does not prove arbitrary inputs are accepted or a separate local editing tool exists.
Original prompt examples
These express creative intent, not guaranteed text accuracy, count, dimensions, or reference consistency. Adjust them to the current form and inspect the output.
White-background ecommerce hero, glass serum bottle centered, clear bottle highlights, soft studio light, product fills about 60% of frame, no clutter props, no watermark, commercial photography look.
Vertical promo poster, large readable headline Limited 70% off at lower left, skincare bottle on the right, premium navy and gold accents, sharp typography, leave a safe zone at the top for a logo.
Front packaging close-up, Chinese and English product names printed clearly on the box, soft light, macro texture, clean gradient background, no blurry text.
Clean mobile checkout UI illustration, price and button labels readable, flat modern style, light background, no real brand logos.
Product bottle at 3/4 angle, large subject, sharp edges, simple background, suitable for a gentle rotate showcase later; no motion blur.
Three reusable workflows
Recipe A: product views and backgrounds
Keep subject, composition, and light fixed while changing one background or angle factor. Check contours and details without promising multiple versions in a fixed time.
Recipe B: posters and headlines
Confirm the subject and empty space, then inspect copy, punctuation, and safe zones. Revise or lay out incorrect text separately rather than treating readability as a model guarantee.
Recipe C: candidate stills to video
Select stills that pass detail checks and retain the brief and settings. Then verify video-mode image requirements. A good still does not guarantee video fidelity; check Suno audio separately in the edit.
Output checklist
| Check | Inspection |
|---|---|
| Subject | Shape, count, edges, and proportions against the brief |
| Composition | Empty space, safe zones, and subject position |
| Text | Every character, punctuation mark, and readability against the original |
| Reference consistency | Compare required details with the input image |
| Effort | Actual cost, waiting time, retries, and manual editing |
Compare alternatives
Use the same brief, exact copy, and necessary references when comparing Midjourney Image, GPT Image, or Nano Banana. Align settings where possible and record differences. Do not assume a model is always faster, better at Chinese text, or more consistent with characters. Judge your material against the checklist. The fixed legacy Claude entry currently maps to Sonnet 5; consult current product names for Veo and Kling links.
Common issues
- Incorrect text: retain exact copy and inspect every character; use separate layout tools when necessary.
- Reference drift: identify details to preserve, check the mode and input requirements, then change one factor.
- Missing count or parameter: follow the form instead of inferring support from a prompt or older tutorial.
- Unclear comparison after retries: record prompts, modes, settings, and failures rather than changing many factors together.
