Create Product Ad Images With GPT Image 2.5

Create product ad images with GPT Image 2.5 by giving the model a clear product reference, a specific scene, and fixed product details. Review the generated package before approving the overall image.

In ReelsFarm, GPT Image 2.5 uses Sunburst. This guide explains a model-based image workflow. Product Studio is a separate compositing workflow and can use different controls or models.

1. Define the Ad Image's Job

Choose one purpose: show product scale, illustrate a use case, support a launch, or provide space for an offer headline.

For example, a desk scene can show how a bottle fits beside a laptop. Do not ask the image to prove durability or a health benefit unless the content includes actual supporting evidence.

2. Prepare the References

Use a clear product image with the complete package visible. If a person must remain consistent, include a suitable character reference through a supported workflow.

Name what each reference controls. The product source controls the package. The person source controls the character. A style reference controls general visual direction rather than product facts.

3. Write the Scene and Preservation Instructions

Try this brief: “Create a vertical ad image with one supplied bottle on a desk beside the supplied character. Preserve the bottle shape, cap color, label layout, and product count. Keep the character recognizable. Use soft window light, natural proportions, and a clear desk surface. Leave uncluttered space above the bottle for copy. Do not add a testimonial or benefit claim.”

Treat this as an initial instruction. It cannot guarantee exact packaging reproduction. Use a real product image or controlled correction when the generated label is not usable.

4. Choose the Supported Settings

Select GPT Image 2.5 in the supported image workflow and use the controls shown in the app. The provider's full API options do not all appear in every application.

Start with an exploratory image. Check the scene before producing a larger batch. [OpenAI's model documentation](https://developers.openai.com/api/docs/models/gpt-image-2.5-sunburst) describes Sunburst's generation and editing role; it does not establish your campaign's output quality.

5. Change One Campaign Variable

Compare a desk scene and a travel scene, or compare two crops. Keep the product and core message stable so the variation has a clear purpose.

Save the prompt and source references with each output. A large batch without that record is difficult to evaluate or reproduce.

6. Inspect the Product at Full Size

Check label text, cap, shape, color, scale, and object contact. Also inspect hands and any reflections. A good face and attractive background cannot compensate for a wrong package.

Reject serious inaccuracies before adding copy. Our editing mistakes checklist covers common preservation failures.

7. Add Exact Copy and Review the Final Layout

Use a controlled text layer when exact wording matters. Preview the finished image on a phone. Check whether the headline is readable and the product remains visible in the intended crop.

Keep clean versions with and without copy so the asset can be adapted without another unnecessary generation.

Save a Production Record

Store the brief, source files, model choice, settings, attempts, and approved output. Use the model comparison method if you need evidence for switching models.

FAQ

Can GPT Image 2.5 preserve a product perfectly?

Do not assume perfect preservation. Inspect the generated package against the source and correct or reject inaccurate details.

Is this the same as Product Studio?

No. This article describes a GPT Image workflow. Product Studio has its own composition route and available controls.