OpenAI Images Generations API Application and Usage

The OpenAI Images Generations API currently supports various image generation models, including the classic dall-e-3, the text rendering-capable gpt-image-1, the latest generation gpt-image-2, and the series of models nano-banana / nano-banana-2-lite / nano-banana-2 / nano-banana-pro that can be accessed through the same interface. They can all generate high-quality images based on text descriptions.

This document mainly introduces the usage process of the OpenAI Images Generations API, allowing us to easily utilize the image generation capabilities of the OpenAI series.

¶ Application Process

To use the OpenAI Images Generations API, first go to the Ace Data Cloud Console to obtain your API Token for backup.

If you are not logged in or registered, you will be automatically redirected to the login page inviting you to register and log in, and upon completion, you will be automatically returned to the current page.

One API Token can call all services on the platform without needing to apply separately for each service. The first application will grant a free quota for a trial experience; when the quota is insufficient, you can recharge the general balance in the console.

📘 Complete documentation: OpenAI Images Generations API →

¶ GPT-Image-2 Model

gpt-image-2 is the new generation image generation model launched by OpenAI, which has significant improvements over dall-e-3 and gpt-image-1 in the following aspects:

Stronger instruction-following ability: Can accurately understand complex compositions, counting, positional relationships, and other structured instructions.
Clearer text rendering: English and numbers in scenarios such as posters, menus, infographics, and logos are almost never garbled.
Richer style expression: Natively supports various styles such as cinematic portraits, retro posters, children's illustrations, product photography, and infographics.
Native multi-aspect ratio + high-resolution support: Covers 5 aspect ratios (1:1, 4:3, 3:4, 16:9, 9:16) with a total of 3 resolution tiers (1K / 2K / 4K).

The calling method is completely consistent with other models; just set the model field to gpt-image-2. The url in the returned result is a permanently hosted image link on platform.cdn.acedata.cloud, which can be opened directly in a browser or embedded in a webpage.

¶ Line Variants (`:official` / `:reverse`)

gpt-image-2 defaults to the standard line. You can explicitly choose the line by appending the model name suffix:

gpt-image-2:official: Official channel, stable and compliant. Supports true 2K / 4K resolution, billed per image, with a unit price 2 times that of the default gpt-image-2. If the line is unavailable, it will return an error directly and will not automatically downgrade.
gpt-image-2:reverse: Completely equivalent to the default gpt-image-2, with a better cost-performance ratio, price unchanged.

¶ Supported `size` Values

gpt-image-2 only checks the format of size; as long as it is not auto or an empty string, it needs to match WIDTHxHEIGHT (e.g., 1024x1024, 2048x1152, 800x600); any other form will return 400. All sizes (1K / 2K / 4K / custom) are charged uniformly per image, without additional charges based on size.

Size limitations: Custom sizes must meet the criteria of both width and height being multiples of 16, long side ≤ 3840, total pixel count ≤ 8,294,400; exceeding these limits will return a 4xx error.

Ratio	1K Recommended	2K Recommended	4K Recommended
1:1	`1024x1024`	`2048x2048`	`2880x2880`
4:3	`1536x1024`	`2048x1536`	`3264x2448`
3:4	`1024x1536`	`1536x2048`	`2448x3264`
16:9	`1792x1024`	`2048x1152`	`3840x2160`
9:16	`1024x1792`	`1152x2048`	`2160x3840`

You can also pass size: "auto" or omit the size field, in which case the model will choose the default size itself. The output in the 1K tier does not guarantee strict pixel alignment—if you pass 1024x1024, you might receive 1254x1254, maintaining the aspect ratio. If you pass it back as size, the billing remains unchanged. A single call for 4K usually takes 4–8 minutes; it is recommended to use it with the callback_url for asynchronous callbacks mentioned later.

About the n Parameter gpt-image-2 supports n > 1 (values 1–10): a single request can return and bill for the corresponding number of images. To ensure that multiple results have differences, it is recommended to pass different prompt or seed simultaneously. This also applies to gpt-image-1 / gpt-image-1.5, as well as the nano-banana / nano-banana-2-lite / nano-banana-2 / nano-banana-pro series; dall-e-3 only supports n = 1. Note that response_format=b64_json only supports n=1; for n>1, please use the default URL return. If some images fail to generate, only the successful parts will be returned and billed.

Below are several real examples from different perspectives to intuitively feel the capabilities of gpt-image-2.

¶ Scenario 1: Cinematic Portrait

Film terminology (35mm film, shallow depth of field, neon light, etc.) can be used in the prompt to precisely control the atmosphere and texture.

Python sample call code:

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "gpt-image-2",
    "prompt": "A cinematic portrait of a young woman standing in a convenience store at night, illuminated by soft pink and cyan neon signs through the window. Shot on 35mm film, shallow depth of field, slight grain, melancholic mood.",
    "size": "1024x1536"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

The returned result is as follows:

{
  "success": true,
  "task_id": "ab58a5df-6f46-4874-bff6-93169e2849a3",
  "created": 1777048800,
  "data": [
    {
      "revised_prompt": "A cinematic portrait of a young woman standing in a convenience store at night, illuminated by soft pink and cyan neon signs through the window. Shot on 35mm film, shallow depth of field, slight grain, melancholic mood.",
      "url": "https://platform.cdn.acedata.cloud/gpt-image/ab58a5df-6f46-4874-bff6-93169e2849a3_0.png"
    }
  ]
}

The generated image is as follows:

¶ Scene Two: Vintage Travel Poster (with Text Rendering)

gpt-image-2 performs stably in typesetting and font rendering, making it very suitable for generating designs with text such as posters, menus, and greeting cards.

payload = {
    "model": "gpt-image-2",
    "prompt": "A vintage travel poster of the Amalfi Coast, Italy. Stylized art-deco illustration of cliffside lemon-yellow houses cascading down to a turquoise sea, with a small white sailboat in the harbor. Bold typography at the top reads AMALFI and at the bottom ITALIA 1958. Limited color palette: cream, sea-blue, lemon yellow, terracotta. Slight paper-grain texture.",
    "size": "1024x1536"
}

The image corresponding to the url field in the return result is as follows:

It can be seen that the model not only accurately restored the visual style of the Art Deco poster, but the title text AMALFI and ITALIA 1958 were also rendered clearly and correctly.

¶ Scene Three: Complex Composition and Counting

The following prompt is used to test the model's ability to follow structured instructions regarding "quantity" and "position."

payload = {
    "model": "gpt-image-2",
    "prompt": "A wooden bookshelf consisting of three shelves: On the top shelf, there should be one book. On the second shelf, there should be three books. On the bottom shelf, there should be seven books. Soft warm lighting, photorealistic, cozy library atmosphere.",
    "size": "1024x1024"
}

The generated image is as follows:

It can be seen that the number of books on the three shelves (1 / 3 / 7) is completely consistent with the prompt, which is something that was difficult to achieve stably in the dall-e-3 era.

¶ Scene Four: Illustration Style (Landscape)

By specifying artistic media and emotional keywords, the model can be guided to produce stylized illustrations.

payload = {
    "model": "gpt-image-2",
    "prompt": "A soft, poetic children's book illustration of a small fox reading a book under a glowing mushroom in a moonlit forest. Watercolor and pencil texture, gentle pastel colors, dreamy atmosphere, hand-drawn feel.",
    "size": "1536x1024"
}

The generated landscape illustration is as follows:

¶ Asynchronous and Callback

gpt-image-2 typically requires 60 to 90 seconds for a single call. If you do not wish to maintain a long connection, you can use the callback_url asynchronous callback mechanism introduced later in this article. The calling process is completely consistent with other models.

¶ Nano Banana Series Models

The nano-banana series is an image generation model based on Gemini, which has been integrated through the same /openai/images/generations interface. There is no need to switch endpoints; just change the model to any of those in the table below.

Model	Billing (Credits / Time)	Applicable Scenarios
`nano-banana`	0.14	General image generation, fastest speed, lowest cost
`nano-banana-2-lite`	0.14	Gemini 3.1 lightweight image model, supports only 1K, low latency output
`nano-banana-2`	0.28	Significant improvement in quality and detail
`nano-banana-pro`	0.35	The flagship of the series, best in composition, detail, and text rendering

Important: Parameter Support Range Nano Banana connects to the OpenAI protocol through an adaptation layer and supports only the following parameters compared to gpt-image-*: model, prompt, size, n.

size will be mapped to internal aspect_ratio as per the table below; unlisted sizes will degrade to 1:1:

1024x1024 / 512x512 / 256x256 → 1:1

1792x1024 → 16:9

1024x1792 → 9:16

Does not support parameters such as quality, style, response_format, background, output_format, etc.; any filled will be ignored. n > 1 is supported (1–10), and will return and charge for the corresponding number of images.

The return structure follows the OpenAI format (data[].url), but created is fixed at 0, and b64_json will not be returned; revised_prompt will always equal the original prompt.

¶ Basic Call

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "nano-banana",
    "prompt": "a small red apple on a white table, photoreal",
    "size": "1024x1024"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

The return result is as follows:

{
  "created": 0,
  "data": [
    {
      "url": "https://platform.cdn.acedata.cloud/nanobanana/6870b330-65c4-436c-bb80-819fdae7a7a4.png",
      "revised_prompt": "a small red apple on a white table, photoreal"
    }
  ]
}

The generated image can be accessed directly through the returned url field:

¶ Upgrade to Flagship Model `nano-banana-pro`

Simply change the model to nano-banana-pro, and the other parameters remain completely the same:

payload = {
    "model": "nano-banana-pro",
    "prompt": "abstract painting",
    "size": "1024x1024"
}

Return example:

{
  "created": 0,
  "data": [
    {
      "url": "https://platform.cdn.acedata.cloud/nanobanana/6227fcc9-3442-4aa3-a76c-4a4441a99649.png",
      "revised_prompt": "abstract painting"
    }
  ]
}

¶ Asynchronous Callback

The callback_url asynchronous callback mechanism is equally effective for nano-banana, and the calling process is completely consistent with other models. For details, see the section Asynchronous Callback below.

¶ Basic Usage

Next, you can fill in the corresponding content on the interface, as shown in the figure:

When using this interface for the first time, we need to fill in at least three pieces of content: one is authorization, which can be selected directly from the dropdown list. The other parameter is model, which is the category of the OpenAI DALL-E official model we choose to use. Here we mainly have 1 type of model; details can be found in the models we provide. The last parameter is prompt, which is the prompt we input to generate the image.

You can also notice that there is corresponding code generation on the right side; you can copy the code to run directly or click the "Try" button for testing.

Python sample calling code:

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "dall-e-3",
    "prompt": "A cute baby sea otter"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

After the call, we find the return result as follows:

{
  "created": 1721626477,
  "data": [
    {
      "revised_prompt": "A delightful image showcasing a young sea otter, who is born brown, with wide charming eyes. It is delightfully lying on its back, paddling in the calm sea waters. Its dense, velvety fur appears wet and shimmering, capturing the essence of its habitat. The small creature curiously plays with a sea shell with its small paws, looking absolutely innocent and charming in its natural environment.",
      "url": "https://dalleprodsec.blob.core.windows.net/private/images/5d98aa7c-80c6-4523-b571-fc606ad455b9/generated_00.png?se=2024-07-23T05%3A34%3A48Z&sig=GAz%2Bi3%2BkHOQwAMhxcv22tBM%2FaexrxPgT9V0DbNrL4ik%3D&ske=2024-07-23T08%3A41%3A10Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2024-07-16T08%3A41%3A10Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02"
    }
  ]
}

The return result has multiple fields, described as follows:

created, the ID generated for this image generation, used to uniquely identify this task.
data, contains the result information of the image generation.

Among them, data contains the specific information of the model-generated image, and its url is the detailed link to the generated image, as shown in the figure.

¶ Image Quality Parameter `quality`

Next, we will introduce how to set some detailed parameters for the image generation results, among which the image quality parameter quality includes two types: the first standard indicates generating standard images, and the other hd indicates that the created image has finer details and greater consistency.

Below, set the image quality parameter to standard, with specific settings as shown in the figure:

You can also notice that there is corresponding code generation on the right side; you can copy the code to run directly or click the "Try" button for testing.

Python sample calling code:

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "dall-e-3",
    "prompt": "A cute baby sea otter",
    "quality": "standard"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

After the call, we find the return result as follows:

{
  "created": 1721636023,
  "data": [
    {
      "revised_prompt": "A cute baby sea otter is lying playfully on its back in the water, with its fur looking glossy and soft. One of its tiny paws is reaching out curiously, and it has an expression of pure joy and warmth on its face as it looks up to the sky. Its body is surrounded by bubbles from its playful twirling in the water. A gentle breeze is playing with its fur making it look more charming. The scene portrays the tranquility and charm of marine life.",
      "url": "https://dalleprodsec.blob.core.windows.net/private/images/a93ee5e7-3abd-4923-8d79-dc9ef126da46/generated_00.png?se=2024-07-23T08%3A13%3A55Z&sig=wTXGYvUOwUIkaB2CxjK9ww%2FHjS8OwYUWcYInXYKwcAM%3D&ske=2024-07-23T11%3A32%3A05Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2024-07-16T11%3A32%3A05Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02"
    }
  ]
}

The returned result is consistent with the basic usage content, and you can see the generated image with the image quality parameter set to standard as shown in the figure:

With the same operation as above, simply set the image quality parameter to hd, and you can obtain the image shown in the figure below:

It can be seen that the image generated with hd has finer details and greater consistency than that generated with standard.

¶ Image Size Parameter `size`

We can also set the size of the generated image, and we can make the following settings. The following sets the image size to 1024 * 1024, with specific settings as shown in the image below:

At the same time, you can notice that there is corresponding code generation on the right side, which you can copy and run directly, or you can click the "Try" button to test directly.

Python sample call code:

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "dall-e-3",
    "prompt": "A cute baby sea otter",
    "size": "1024x1024"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

After the call, we found that the returned result is as follows:

{
  "created": 1721636652,
  "data": [
    {
      "revised_prompt": "A delightful depiction of a baby sea otter. The small mammal is captured in its natural habitat in the ocean, floating on its back. It has thick brown fur that is sleek and wet from the sea water. Its eyes are closed as if it is enjoying a moment of deep relaxation. The water around it is calm, reflecting the peacefulness of the scene. The background should hint at a diverse marine ecosystem, with visible strands of kelp floating on the surface, suggesting the baby otter's preferred environment.",
      "url": "https://dalleprodsec.blob.core.windows.net/private/images/9d625ac6-fd2b-42a9-84a6-8c99eb357ccf/generated_00.png?se=2024-07-23T08%3A24%3A24Z&sig=AXtYXowEakGxfRp8LhC2DwqL%2F07LhEDW40oCP%2BdTO8s%3D&ske=2024-07-23T18%3A00%3A45Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2024-07-16T18%3A00%3A45Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02"
    }
  ]
}

The returned result is consistent with the basic usage content, and you can see the generated image with a size of 1024 * 1024 as shown in the image below:

With the same operation as above, simply set the image size to 1792 * 1024, and you can obtain the image shown below:

It can be seen that the image size is obviously different. Additionally, more sizes can be set; for detailed information, please refer to our official documentation.

¶ Image Style Parameter `style`

The image style parameter style includes two parameters. The first one, vivid, indicates that the generated image is more vibrant, while the other, natural, indicates that the generated image is more natural.

The following sets the image style parameter to vivid, with specific settings as shown in the image below:

At the same time, you can notice that there is corresponding code generation on the right side, which you can copy and run directly, or you can click the "Try" button to test directly.

Python sample call code:

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "dall-e-3",
    "prompt": "A cute baby sea otter",
    "style": "vivid"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

After the call, we found that the returned result is as follows:

{
  "created": 1721637086,
  "data": [
    {
      "revised_prompt": "A baby sea otter with soft, shiny fur and sparkling eyes floating playfully on calm ocean waters. This adorable creature is trippingly frolicking amidst small, gentle waves under a bright, clear, sunny sky. The tranquility of the sea contrasts subtly with the delightful energy of this young otter. The critter gamely clings to a tiny piece of driftwood, its small paws adorably enveloping the floating object.",
      "url": "https://dalleprodsec.blob.core.windows.net/private/images/6e48f701-7fd3-4356-839e-a2f6f0fe82d9/generated_00.png?se=2024-07-23T08%3A31%3A37Z&sig=4percxqTbUR1j3BQmkhvj%2FAhHzInKI%2FqiTo1MP69coI%3D&ske=2024-07-27T10%3A39%3A55Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2024-07-20T10%3A39%3A55Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02"
    }
  ]
}

The returned result is consistent with the basic usage content, and you can see the generated image with the style parameter set to vivid as shown in the image below:

With the same operation as above, simply set the image style parameter to natural, and you can obtain the image shown below:

It can be seen that the image generated with vivid is more vibrant and realistic than that with natural.

¶ Image Link Format Parameter `response_format`

The last image link format parameter response_format also has two types. The first type, b64_json, encodes the image link in Base64, while the other type, url, is a regular image link that can be viewed directly.

The following sets the image link format parameter to url, with specific settings as shown in the image below:

At the same time, you can notice that there is corresponding code generation on the right side, which you can copy and run directly, or you can click the "Try" button to test directly.

Python sample call code:

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "dall-e-3",
    "prompt": "A cute baby sea otter",
    "response_format": "url"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

After the call, we found that the returned result is as follows:

{
  "created": 1721637575,
  "data": [
    {
      "revised_prompt": "A charming depiction of a baby sea otter. The otter is seen resting serenely on its back amidst the gentle, blue ocean waves. The baby otter's fur is an endearing mix of soft greyish brown shades, glinting subtly in the muted sunlight. Its small paws are touching, lifted slightly towards the sky as if playing with an unseen object. Its round, expressive eyes are wide in curiosity, sparking with life and innocence. Use a realistic style to evoke the otter's natural habitat and its adorably fluffy exterior.",
      "url": "https://dalleprodsec.blob.core.windows.net/private/images/87792c5f-8b6d-412e-81dd-f1a1baa19bd2/generated_00.png?se=2024-07-23T08%3A39%3A47Z&sig=zzRAn30TqIKHdLVqZPUUuSJdjCYpoJdaGU6BeoA76Jo%3D&ske=2024-07-23T13%3A32%3A13Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2024-07-16T13%3A32%3A13Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02"
    }
  ]
}

The returned result is consistent with the basic usage content, and the image link format parameter for the url of the generated image is Image URL which can be accessed directly, and the image content is shown below:

By performing the same operation as above, simply changing the image link format parameter to b64_json, you can obtain the Base64 encoded image link, with the specific result shown below:

{
  "created": 1721638071,
  "data": [
    {
      "b64_json": "iVBORw0..............v//AQEAAP4AAAD+AAADAQAAAwEEA/4D//8Q/Pbw64mKbVTFoQAAAABJRU5ErkJggg==",
      "revised_prompt": "A charming image of a young baby sea otter. The otter is gently floating on a calm blue sea, basking in the warm, golden rays of sunlight streaming down from a clear sky above. The otter's fur is a rich chocolate brown, and it looks incredibly soft and fluffy. The otter's eyes are bright and expressive, filled with childlike curiosity and joy. It has small, pricked ears and a button-like nose which adds to its overall cuteness. In the sea around it, twinkling droplets of water can be seen, pepped up by the sunlight, the sight is certainly a delightful one."
    }
  ]
}

¶ Asynchronous Callback

Due to the potentially long time taken by the OpenAI Images Generations API to generate images, if the API does not respond for a long time, the HTTP request will keep the connection open, leading to additional system resource consumption. Therefore, this API also provides support for asynchronous callbacks.

The overall process is: when the client initiates a request, an additional callback_url field is specified. After the client initiates the API request, the API will immediately return a result containing a task_id field, representing the current task ID. When the task is completed, the generated image result will be sent to the client-specified callback_url in POST JSON format, which also includes the task_id field, allowing the task result to be associated by ID.

Let’s understand how to operate specifically through an example.

First, the Webhook callback is a service that can receive HTTP requests, and developers should replace it with the URL of their own HTTP server. For demonstration purposes, a public Webhook sample site https://webhook.site/ is used, and opening this site will provide a Webhook URL, as shown in the image:

Copy this URL, and it can be used as a Webhook. The sample here is https://webhook.site/3d32690d-6780-4187-a65c-870061e8c8ab.

Next, we can set the callback_url field to the above Webhook URL, while filling in the corresponding parameters, as shown in the following code:

import requests

url = "https://api.acedata.cloud/openai/images/generations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "dall-e-3",
    "prompt": "A cute baby sea otter",
    "callback_url": "https://webhook.site/3d32690d-6780-4187-a65c-870061e8c8ab"
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

Clicking run, you can find that an immediate result is obtained, as follows:

{
  "task_id": "6a97bf49-df50-4129-9e46-119aa9fca73c"
}

After a moment, we can observe the generated image result at the Webhook URL, with the content as follows:

{
  "success": true,
  "task_id": "6a97bf49-df50-4129-9e46-119aa9fca73c",
  "trace_id": "9b4b1ff3-90f2-470f-b082-1061ec2948cc",
  "data": {
    "created": 1721626477,
    "data": [
      {
        "revised_prompt": "A delightful image showcasing a young sea otter...",
        "url": "https://dalleprodsec.blob.core.windows.net/private/images/..."
      }
    ]
  }
}

You can see that the result contains a task_id field, and the data field includes the same image generation result as the synchronous call, allowing the task to be associated through the task_id field.

¶ Error Handling

When calling the API, if an error occurs, the API will return the corresponding error code and message. For example:

400 token_mismatched: Bad request, possibly due to missing or invalid parameters.
400 api_not_implemented: Bad request, possibly due to missing or invalid parameters.
401 invalid_token: Unauthorized, invalid or missing authorization token.
429 too_many_requests: Too many requests, you have exceeded the rate limit.
500 api_error: Internal server error, something went wrong on the server.

¶ Error Response Example

{
  "success": false,
  "error": {
    "code": "api_error",
    "message": "fetch failed"
  },
  "trace_id": "2cf86e86-22a4-46e1-ac2f-032c0f2a4e89"
}

¶ Conclusion

Through this document, you have learned how to easily use the image generation capabilities of the official OpenAI DALL-E via the OpenAI Images Generations API. We hope this document helps you better integrate and use the API. If you have any questions, please feel free to contact our technical support team.

OpenAI Images Generations API Application and Usage

¶ Application Process

¶ GPT-Image-2 Model

¶ Line Variants (:official / :reverse)

¶ Supported size Values

¶ Scenario 1: Cinematic Portrait

¶ Scene Two: Vintage Travel Poster (with Text Rendering)

¶ Scene Three: Complex Composition and Counting

¶ Scene Four: Illustration Style (Landscape)

¶ Asynchronous and Callback

¶ Nano Banana Series Models

¶ Basic Call

¶ Upgrade to Flagship Model nano-banana-pro

¶ Asynchronous Callback

¶ Basic Usage

¶ Image Quality Parameter quality

¶ Image Size Parameter size

¶ Image Style Parameter style

¶ Image Link Format Parameter response_format

¶ Asynchronous Callback

¶ Error Handling

¶ Error Response Example

¶ Conclusion

¶ Line Variants (`:official` / `:reverse`)

¶ Supported `size` Values

¶ Upgrade to Flagship Model `nano-banana-pro`

¶ Image Quality Parameter `quality`

¶ Image Size Parameter `size`

¶ Image Style Parameter `style`

¶ Image Link Format Parameter `response_format`