Integration and Use of Flux Tasks API

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

Application Process

To use the Flux Images Generation API, first obtain your API Token from the Ace Data Cloud Console for future reference.

If you are not logged in or registered, you will be automatically redirected to the login page to invite you to register and log in, and will return to the current page upon completion. 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: Flux Images Generation API →

Request Example

The Flux Tasks API can be used to query the results of the Flux Images Generation API. For information on how to use the Flux Images Generation API, please refer to the documentation Flux Images Generation API. We will demonstrate how to use this API with an example task ID returned by the Flux Images Generation API. Suppose we have a task ID: 2db0168c-2373-4367-8d9a-9dc778802e8a, we will show how to pass in a task ID.

Task Example Image

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.
action: The operation method for the task.

Set as shown in the image below:

Code Example

You can see 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:

CURL

curl -X POST 'https://api.acedata.cloud/flux/tasks' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
  "id": "2c454ff3-4f8f-47f0-8147-acb29a84d1c2",
  "action": "retrieve"
}'

Python

import requests

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

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

payload = {
    "id": "2c454ff3-4f8f-47f0-8147-acb29a84d1c2",
    "action": "retrieve"
}

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

Response Example

Upon successful request, the API will return the detailed information of the image task as shown in the following example:

{
  "_id": "677de81d550a4144a5f4cf62",
  "id": "2db0168c-2373-4367-8d9a-9dc778802e8a",
  "api_id": "deefc5d7-7f22-43e9-929e-f2b6afee60b7",
  "application_id": "001c2f84-2a4a-4c4d-ba3f-8a89f43b5be2",
  "created_at": 1736304669.779,
  "started_at": 1736304669.839,
  "finished_at": 1736304679.439,
  "elapsed": 9.6,
  "credential_id": "b00bddd3-140f-4343-a9a2-affb312b60de",
  "request": {
    "action": "generate",
    "size": "1024x1024",
    "prompt": "a white siamese cat"
  },
  "trace_id": "6624929c-bb80-40c0-81e8-d96af8405d19",
  "user_id": "ad7afe47-cea9-4cda-980f-2ad8810e51cf",
  "response": {
    "success": true,
    "task_id": "2db0168c-2373-4367-8d9a-9dc778802e8a",
    "trace_id": "6624929c-bb80-40c0-81e8-d96af8405d19",
    "data": [
      {
        "prompt": "a white siamese cat",
        "image_url": "https://cdn.acedata.cloud/e724d7f13d.png",
        "seed": 281520112,
        "timings": {
          "inference": 3.193
        }
      }
    ]
  }
}

The returned result contains multiple fields, with the request field being the request body when the task was initiated, while the response field is the response body returned after the task is completed. The field descriptions are as follows.

id: The ID of the image task generated, used to uniquely identify this image generation task.
request: The request information in the image task.
response: The return information in the image task.
created_at: The task creation time, Unix timestamp (seconds, float).
started_at: The task start execution time, Unix timestamp (seconds, float).
finished_at: The task completion time, Unix timestamp (seconds, float). This field is not returned if the task is not completed.
elapsed: The time taken for task execution, in seconds (float, rounded to 3 decimal places). This field is not returned if the task is not completed.

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:

Code Example

You can see 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:

Response Example

Upon successful request, the API will return the specific details of all batch image tasks. For example:

{
  "items": [
    {
      "_id": "677de81d550a4144a5f4cf62",
      "id": "2db0168c-2373-4367-8d9a-9dc778802e8a",
      "api_id": "deefc5d7-7f22-43e9-929e-f2b6afee60b7",
      "application_id": "001c2f84-2a4a-4c4d-ba3f-8a89f43b5be2",
      "created_at": 1736304669.779,
      "started_at": 1736304669.839,
      "finished_at": 1736304679.439,
      "elapsed": 9.6,
      "credential_id": "b00bddd3-140f-4343-a9a2-affb312b60de",
      "request": {
        "action": "generate",
        "size": "1024x1024",
        "prompt": "a white siamese cat"
      },
      "trace_id": "6624929c-bb80-40c0-81e8-d96af8405d19",
      "user_id": "ad7afe47-cea9-4cda-980f-2ad8810e51cf",
      "response": {
        "success": true,
        "task_id": "2db0168c-2373-4367-8d9a-9dc778802e8a",
        "trace_id": "6624929c-bb80-40c0-81e8-d96af8405d19",
        "data": [
          {
            "prompt": "a white siamese cat",
            "image_url": "https://cdn.acedata.cloud/e724d7f13d.png",
            "seed": 281520112,
            "timings": {
              "inference": 3.193
            }
          }
        ]
      }
    },
    {
      "_id": "677de950550a4144a5f52963",
      "id": "72bdd69d-290d-4710-a6d4-60c78968865a",
      "api_id": "deefc5d7-7f22-43e9-929e-f2b6afee60b7",
      "application_id": "001c2f84-2a4a-4c4d-ba3f-8a89f43b5be2",
      "created_at": 1736304976.278,
      "started_at": 1736304976.338,
      "finished_at": 1736304985.938,
      "elapsed": 9.6,
      "credential_id": "b00bddd3-140f-4343-a9a2-affb312b60de",
      "request": {
        "action": "generate",
        "size": "1024x1024",
        "prompt": "a white siamese cat"
      },
      "trace_id": "1dca4b49-d31d-42e6-83d9-7f0c56f62d31",
      "user_id": "ad7afe47-cea9-4cda-980f-2ad8810e51cf",
      "response": {
        "success": true,
        "task_id": "72bdd69d-290d-4710-a6d4-60c78968865a",
        "trace_id": "1dca4b49-d31d-42e6-83d9-7f0c56f62d31",
        "data": [
          {
            "prompt": "a white siamese cat",
            "image_url": "https://cdn.acedata.cloud/e724d7f13d.png",
            "seed": 1437672535,
            "timings": {
              "inference": 3.175
            }
          }
        ]
      }
    }
  ],
  "count": 2
}

The return result contains multiple fields, among which items includes the specific details of batch image tasks, and each image task’s specific information is the same as the fields mentioned above.

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 image tasks in this batch query.

CURL

curl -X POST 'https://api.acedata.cloud/flux/tasks' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
  "ids": ["2db0168c-2373-4367-8d9a-9dc778802e8a","72bdd69d-290d-4710-a6d4-60c78968865a"],
  "action": "retrieve_batch"
}'

Python

import requests

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

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

payload = {
    "ids": ["2db0168c-2373-4367-8d9a-9dc778802e8a","72bdd69d-290d-4710-a6d4-60c78968865a"],
    "action": "retrieve_batch"
}

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

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 use the FLux Tasks API to query all specific details of single or batch image tasks. 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.

Getting Started

Core Concepts

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AI Image

AI Video

AI Audio

Web & Data

CAPTCHA

Identity

Proxy

Advanced

Coding

Integration and Use of Flux Tasks API

Application Process

Request Example

Task Example Image

Setting Request Headers and Request Body

Code Example

CURL

Python

Response Example

Batch Query Operation

Code Example

Response Example

CURL

Python

Error Handling

Error Response Example

Conclusion

​Application Process

​Request Example

​Task Example Image

​Setting Request Headers and Request Body

​Code Example

​CURL

​Python

​Response Example

​Batch Query Operation

​Code Example

​Response Example

​CURL

​Python

​Error Handling

​Error Response Example

​Conclusion

Application Process

Request Example

Task Example Image

Setting Request Headers and Request Body

Code Example

CURL

Python

Response Example

Batch Query Operation

Code Example

Response Example

CURL

Python

Error Handling

Error Response Example

Conclusion