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The main function of the Midjourney Tasks API is to query the execution status of tasks by inputting the task ID generated by the Midjourney Imagine API or Midjourney Describe API. This document will provide detailed integration instructions for the Midjourney Tasks API, helping you easily integrate and fully utilize the powerful features of this API. With the Midjourney Tasks API, you can easily query the execution status of tasks from the Midjourney Imagine API or Midjourney Describe API.

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Application Process

To use the Midjourney Tasks API, you first need to apply for the corresponding service on the application page Midjourney Imagine API, and then copy the task ID from the Imagine API, as shown in the image:

Finally, go to the Tasks API page Midjourney Tasks API to apply for the corresponding service. After entering the page, click the “Acquire” button, as shown in the image: Application Page If you are not logged in or registered, you will be automatically redirected to the login page inviting you to register and log in. After logging in or registering, you will be automatically returned to the current page. There is a free quota available for first-time applicants, allowing you to use the API for free.

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Request Example

The Midjourney Tasks API can be used to query the results of both the Midjourney Imagine API and the Midjourney Describe API. For information on how to use the Midjourney Imagine API, please refer to the documentation Midjourney Imagine API. For information on how to use the Midjourney Describe API, please refer to Midjourney Describe API. We will take a task ID returned by the Midjourney Imagine API as an example to demonstrate how to use this API. Suppose we have a task ID: 7489df4c-ef03-4de0-b598-e9a590793434, and we will demonstrate how to pass in a task ID.

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Task Example Image

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Setting Request Headers and Request Body

Request Headers include:
  • accept: Specifies that the response should be in JSON format, set to application/json.
  • authorization: The key to call the API, which can be selected directly after application.
Request Body includes:
  • id: The uploaded task ID.
  • ids: An array of task IDs for batch queries.
  • action: The operation method for the task, supporting retrieve (single query) and retrieve_batch (batch query).
Set as shown in the image below:

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Code Example

It can be seen that various language codes have been automatically generated on the right side of the page, as shown in the image:

Some code examples are as follows:

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CURL

curl -X POST 'https://api.acedata.cloud/midjourney/tasks' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
  "id": "7489df4c-ef03-4de0-b598-e9a590793434",
  "action": "retrieve"
}'

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Python

import requests

url = "https://api.acedata.cloud/midjourney/tasks"

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

payload = {
    "id": "7489df4c-ef03-4de0-b598-e9a590793434",
    "action": "retrieve"
}

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

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Response Example

Upon successful request, the API will return the detailed information of the image task. For example: 
{
  "_id": "668aae3f550a4144a540803b",
  "id": "7489df4c-ef03-4de0-b598-e9a590793434",
  "application_id": "9dec7b2a-1cad-41ff-8536-d4ddaf2525d4",
  "created_at": 1720364607.967,
  "credential_id": "68253cc8-505d-47f4-97ad-0050a62e4975",
  "request": {
    "mode": "fast",
    "prompt": "A cat sitting on a table",
    "action": "generate"
  },
  "type": "imagine",
  "hold": false,
  "image_id": "1259525319472185344",
  "job_id": "da317da6-f500-48e6-bf32-dd48b3e6f84f",
  "response": {
    "image_url": "https://platform.cdn.acedata.cloud/midjourney/7489df4c-ef03-4de0-b598-e9a590793434.png?imageMogr2/thumbnail/!50p",
    "image_width": 1024,
    "image_height": 1024,
    "actions": [
      "upscale1",
      "upscale2",
      "upscale3",
      "upscale4",
      "reroll",
      "variation1",
      "variation2",
      "variation3",
      "variation4"
    ],
    "raw_image_url": "https://platform.cdn.acedata.cloud/midjourney/7489df4c-ef03-4de0-b598-e9a590793434.png",
    "raw_image_width": 2048,
    "raw_image_height": 2048,
    "progress": 100,
    "image_id": "1259525319472185344",
    "task_id": "7489df4c-ef03-4de0-b598-e9a590793434",
    "success": true,
    "job_id": "da317da6-f500-48e6-bf32-dd48b3e6f84f",
    "hold": false
  },
  "duration": 29.437000036239624,
  "finished_at": 1720364637.404
}
The returned result contains multiple fields, with the request field being the request body when the task was initiated, and the response field being the response body returned after the task is completed. If type = imagine, the result is consistent with the request and return of the Midjourney Imagine API; if type = describe, the result is consistent with the request and return of the Midjourney Describe API. The field descriptions are as follows.
  • id: The ID of the image generation task, used to uniquely identify this image generation task.
  • type: If type = imagine, it represents the result of the Midjourney Imagine API; if type = describe, it represents the result of the Midjourney Describe API.
  • job_id: The ID of the image query task generated this time, used to uniquely identify this image query task.
  • image_id: The unique identifier of the image task being queried, which needs to be passed when performing transformation operations on the image next time.
  • request: The request information in the image query task.
  • response: The return information in the image query task.

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Batch Query Operation

This is for querying the details of multiple task IDs, and unlike the above, the action needs to be selected as retrieve_batch. Request Body includes:
  • ids: An array of uploaded task IDs.
  • action: The operation method for the task.
Set as shown in the image below:

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Code Example

It can be seen that various language codes have been automatically generated on the right side of the page, as shown in the figure:

Some code examples are as follows:

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Response Example

After a successful request, the API will return the specific details of all batch image tasks. For example: 
{
  "items": [
    {
      "_id": "668aae3f550a4144a540803b",
      "id": "7489df4c-ef03-4de0-b598-e9a590793434",
      "application_id": "9dec7b2a-1cad-41ff-8536-d4ddaf2525d4",
      "created_at": 1720364607.967,
      "credential_id": "68253cc8-505d-47f4-97ad-0050a62e4975",
      "request": {
        "mode": "fast",
        "prompt": "A cat sitting on a table",
        "action": "generate"
      },
      "type": "imagine",
      "hold": false,
      "image_id": "1259525319472185344",
      "job_id": "da317da6-f500-48e6-bf32-dd48b3e6f84f",
      "response": {
        "image_url": "https://platform.cdn.acedata.cloud/midjourney/7489df4c-ef03-4de0-b598-e9a590793434.png?imageMogr2/thumbnail/!50p",
        "image_width": 1024,
        "image_height": 1024,
        "actions": [
          "upscale1",
          "upscale2",
          "upscale3",
          "upscale4",
          "reroll",
          "variation1",
          "variation2",
          "variation3",
          "variation4"
        ],
        "raw_image_url": "https://platform.cdn.acedata.cloud/midjourney/7489df4c-ef03-4de0-b598-e9a590793434.png",
        "raw_image_width": 2048,
        "raw_image_height": 2048,
        "progress": 100,
        "image_id": "1259525319472185344",
        "task_id": "7489df4c-ef03-4de0-b598-e9a590793434",
        "success": true,
        "job_id": "da317da6-f500-48e6-bf32-dd48b3e6f84f",
        "hold": false
      },
      "duration": 29.437000036239624,
      "finished_at": 1720364637.404
    },
    {
      "_id": "668b41d6550a4144a551d996",
      "id": "807f62de-c63e-4add-8345-7f0ae6dd18e7",
      "application_id": "9dec7b2a-1cad-41ff-8536-d4ddaf2525d4",
      "created_at": 1720402390.341,
      "credential_id": "6fd3e1d5-4bd6-47e8-8872-fab89a183b53",
      "request": {
        "mode": "fast",
        "prompt": "A beautiful girl",
        "action": "generate"
      },
      "type": "imagine",
      "hold": false,
      "image_id": "1259683790612070400",
      "job_id": "ede5c805-e231-498c-8f74-3aa76d5d6d12",
      "response": {
        "image_url": "https://platform.cdn.acedata.cloud/midjourney/807f62de-c63e-4add-8345-7f0ae6dd18e7.png?imageMogr2/thumbnail/!50p",
        "image_width": 1024,
        "image_height": 1024,
        "actions": [
          "upscale1",
          "upscale2",
          "upscale3",
          "upscale4",
          "reroll",
          "variation1",
          "variation2",
          "variation3",
          "variation4"
        ],
        "raw_image_url": "https://platform.cdn.acedata.cloud/midjourney/807f62de-c63e-4add-8345-7f0ae6dd18e7.png",
        "raw_image_width": 2048,
        "raw_image_height": 2048,
        "progress": 100,
        "image_id": "1259683790612070400",
        "task_id": "807f62de-c63e-4add-8345-7f0ae6dd18e7",
        "success": true,
        "job_id": "ede5c805-e231-498c-8f74-3aa76d5d6d12",
        "hold": false
      },
      "duration": 29.471999883651733,
      "finished_at": 1720402419.813
    }
  ],
  "count": 2
}
The returned result contains multiple fields, among which items include the specific details of batch image tasks. The specific information of each image task is the same as the fields mentioned above, and the field information is as follows.
  • items, all specific details of batch image tasks. It is an array, and each element of the array has the same format as the return result of querying a single task above.
  • count, the number of batch image tasks queried here.

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CURL

curl -X POST 'https://api.acedata.cloud/midjourney/tasks' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
  "action": "retrieve_batch",
  "id": "",
  "ids": ["7489df4c-ef03-4de0-b598-e9a590793434","807f62de-c63e-4add-8345-7f0ae6dd18e7"]
}'

​
Python

import requests

url = "https://api.acedata.cloud/midjourney/tasks"

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

payload = {
    "action": "retrieve_batch",
    "id": "",
    "ids": ["7489df4c-ef03-4de0-b598-e9a590793434","807f62de-c63e-4add-8345-7f0ae6dd18e7"]
}

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

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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.

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Error Response Example

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

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Conclusion

Through this document, you have learned how to use the Midjourney Tasks API to query the specific details of single or batch image tasks. We hope this document can help you better integrate and use the API. If you have any questions, please feel free to contact our technical support team.