How to Generate Production-Ready Images with the GPT Image 2 API

When you need images inside a product workflow, the hard part is usually not writing the prompt. It is making image generation predictable enough that a backend job, content tool, or internal dashboard can call it repeatedly without guesswork.

This guide walks through the GPT Image 2 / 2.5 image generation API on Ace Data Cloud from a builder’s point of view: how to call the endpoint, which parameters matter, how to choose canvas sizes, and when to move from a synchronous request to a callback-based workflow.

What you can do

The API exposes a straightforward image generation route:

POST https://api.acedata.cloud/openai/images/generations

You send a JSON body with an image model, a prompt, and optional generation controls. A successful synchronous response returns image URLs in data[].url. That makes the endpoint easy to plug into CMS tools, product mockup generators, design review bots, and automated editorial pipelines.

The documented model choices include gpt-image-2, gpt-image-2.5-flare, and gpt-image-2.5-sunburst. The same family also supports corresponding :official variants, and gpt-image-2 supports :reverse. If you are starting from scratch, the documentation recommends gpt-image-2 as the default.

How it works

At minimum, the request needs authorization, JSON content, a model, a prompt, and usually a size. The response shape depends on whether you run synchronously or provide a callback_url.

For synchronous requests, the API returns a response containing created and data. For callback-based requests, it first returns a task_id, then posts the final result to your callback URL when the long-running task completes.

Send your first request

Here is a minimal call adapted from the documented pattern. Replace YOUR_API_KEY with the API key copied from your Ace Data Cloud application.

curl https://api.acedata.cloud/openai/images/generations   -H "Authorization: Bearer YOUR_API_KEY"   -H "Content-Type: application/json"   -d '{
    "model": "gpt-image-2",
    "prompt": "Minimal editorial product illustration: one plain white ceramic coffee mug centered on a solid cobalt blue tabletop, pale cream background, soft shadow, clean geometric shapes, no text, no logo, portrait composition",
    "size": "1024x1536"
  }'

In your application code, treat data[].url as the generated asset output. Store the URL alongside your own job ID, prompt, model, and timestamp so that you can reproduce or audit the generation later.

Choose the right canvas size

The size field can be auto or an explicit WIDTHxHEIGHT. Explicit sizes are useful when the downstream surface is fixed: a blog cover, mobile story, product card, thumbnail, or slide.

The documented constraints are worth enforcing before you send the request:

• Width and height must be multiples of 16.
• The longer side must not exceed 3840.
• Total pixels must be between 655,360 and 8,294,400.
• The aspect ratio must not exceed 3:1.

Common documented examples include 1024x1024 for square images, 1536x1024 for 4:3, 1024x1536 for 3:4, 1792x1024 for 16:9, and 1024x1792 for 9:16. If layout precision matters, pass the exact size. If you want the model to infer a suitable canvas from the prompt, use auto.

Generate multiple variations

The n parameter lets you request between 1 and 10 images. This is helpful when a human will choose the best result, or when your product wants to show several drafts.

curl https://api.acedata.cloud/openai/images/generations   -H "Authorization: Bearer YOUR_API_KEY"   -H "Content-Type: application/json"   -d '{
    "model": "gpt-image-2",
    "prompt": "A small fox reading under a glowing mushroom, watercolor illustration",
    "size": "1536x1024",
    "n": 2
  }'

One important edge case: when response_format is b64_json, only n: 1 is supported. If you want multiple results, use URL responses and handle each item in data.

Use callbacks for long-running jobs

Image generation can take longer than a typical HTTP request budget. If your synchronous request times out, the troubleshooting table recommends moving to callback_url. With a callback, the first response returns a task_id, and your server receives the final result later.

{
  "model": "gpt-image-2",
  "prompt": "A product poster with clear typography",
  "size": "1024x1024",
  "callback_url": "https://example.com/webhooks/images"
}

In the callback receiver, use task_id for deduplication. The documentation also recommends verifying the request source and data format before marking the job complete.

Handle failures like a production system

Most integration bugs show up around validation and authentication. A 400 means you should inspect the combination of model, prompt, size, n, and response_format. A 401 points to the API key or the Authorization: Bearer ... header. A 429 means requests are too frequent, and a 504 suggests switching to callback_url.

Error responses include error.code, error.message, and trace_id. Log the trace_id with your internal job record, but never log or share the API key.

Where Ace Data Cloud fits

The useful part of this API is its boring shape: one HTTP endpoint, predictable JSON fields, explicit canvas constraints, and a callback path when the job should outlive the request. That is exactly what you want when image generation becomes part of a real workflow instead of a one-off experiment.

Read the full reference in the GPT Image 2 / 2.5 Image Generation API guide.