v2.5-turbo/pro/image-to-video

This documentation is valid for the following list of our models:

• klingai/v2.5-turbo/pro/image-to-video

Try in Playground

KlingAI’s most advanced image-to-video model as of September 2025.

Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our Quickstart guide.

How to Make a Call

Step-by-Step Instructions

Generating a video using this model involves sequentially calling two endpoints:

• The first one is for creating and sending a video generation task to the server (returns a generation ID).

• The second one is for requesting the generated video from the server using the generation ID received from the first endpoint.

Below, you can find both corresponding API schemas.

API Schemas

Now, all of our API schemas for video models use our new universal short URL — https://api.aimlapi.com/v2/video/generations. However, you can still call this model using the legacy URL that includes the vendor name.

Create a video generation task and send it to the server

post

Body

application/json

modelstring · enumRequiredPossible values:

image_urlstring · uriRequired

A direct link to an online image or a Base64-encoded local image that will serve as the visual base or the first frame for the video.

promptstringRequired

The text description of the scene, subject, or action to generate in the video.

durationinteger · enumOptional

The length of the output video in seconds.

Default: 5Possible values:

negative_promptstringOptional

The description of elements to avoid in the generated video.

cfg_scalenumber · max: 1Optional

The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt.

Responses

200Success

application/json

post

/v2/video/generations

cURLJavaScriptPython

curl -L \
  --request POST \
  --url 'https://api.aimlapi.com/v2/video/generations' \
  --header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
      "model": "klingai/v2.5-turbo/pro/image-to-video",
      "prompt": "Mona Lisa puts on glasses with her hands.",
      "image_url": "https://s2-111386.kwimgs.com/bs2/mmu-aiplatform-temp/kling/20240620/1.jpeg"
    }'

200Success

{
  "id": "60ac7c34-3224-4b14-8e7d-0aa0db708325",
  "status": "completed",
  "video": {
    "url": "https://cdn.aimlapi.com/generations/hedgehog/1759866285599-0cdfb138-c03a-49d4-a601-4f6413e27b15.mp4"
  },
  "error": {
    "name": "text",
    "message": "text"
  },
  "meta": {
    "usage": {
      "credits_used": 120000
    }
  }
}

Retrieve the generated video from the server

After sending a request for video generation, this task is added to the queue. This endpoint lets you check the status of a video generation task using its id, obtained from the endpoint described above. If the video generation task status is completed, the response will include the final result — with the generated video URL and additional metadata.

get

Authorizations

access-token

AuthorizationstringRequired

Bearer key

Query parameters

generation_idstringRequiredExample: <REPLACE_WITH_YOUR_GENERATION_ID>

Responses

200Success

application/json

get

/v2/video/generations

cURLJavaScriptPython

curl -L \
  --request GET \
  --url 'https://api.aimlapi.com/v2/video/generations?generation_id=<REPLACE_WITH_YOUR_GENERATION_ID>' \
  --header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>'

200Success

{
  "id": "60ac7c34-3224-4b14-8e7d-0aa0db708325",
  "status": "completed",
  "video": {
    "url": "https://cdn.aimlapi.com/generations/hedgehog/1759866285599-0cdfb138-c03a-49d4-a601-4f6413e27b15.mp4"
  },
  "error": {
    "name": "text",
    "message": "text"
  },
  "meta": {
    "usage": {
      "credits_used": 120000
    }
  }
}

Code Example

The code below creates a video generation task, then automatically polls the server every 10 seconds until it finally receives the video URL.

Python

import requests
import time

# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
api_key = "<YOUR_AIMLAPI_KEY>"
base_url = "https://api.aimlapi.com/v2"

# Creating and sending a video generation task to the server
def generate_video():
    url = f"{base_url}/generate/video/kling/generation"
    headers = {
        "Authorization": f"Bearer {api_key}", 
    }

    data = {
        "model": "klingai/v2.5-turbo/pro/image-to-video",
        "prompt": "Mona Lisa puts on glasses with her hands.",
        "image_url": "https://s2-111386.kwimgs.com/bs2/mmu-aiplatform-temp/kling/20240620/1.jpeg",
        "duration": "5",       
    }
 
    response = requests.post(url, json=data, headers=headers)
    if response.status_code >= 400:
        print(f"Error: {response.status_code} - {response.text}")
    else:
        response_data = response.json()
        return response_data
    

# Requesting the result of the task from the server using the generation_id
def get_video(gen_id):
    url = f"{base_url}/generate/video/kling/generation"
    params = {
        "generation_id": gen_id,
    }
    
    headers = {
        "Authorization": f"Bearer {api_key}", 
        "Content-Type": "application/json"
        }

    response = requests.get(url, params=params, headers=headers)
    return response.json()


def main():
     # Running video generation and getting a task id
    gen_response = generate_video()
    gen_id = gen_response.get("id")
    print("Generation ID:  ", gen_id)

    # Trying to retrieve the video from the server every 10 sec
    if gen_id:
        start_time = time.time()

        timeout = 1000
        while time.time() - start_time < timeout:
            response_data = get_video(gen_id)

            if response_data is None:
                print("Error: No response from API")
                break
        
            status = response_data.get("status")
            print("Status:", status)

            if status == "waiting" or status == "active" or  status == "queued" or status == "generating":
                print("Still waiting... Checking again in 10 seconds.")
                time.sleep(10)
            else:
                print("Processing complete:\n", response_data)
                return response_data
   
        print("Timeout reached. Stopping.")
        return None     


if __name__ == "__main__":
    main()

Response

Generation ID:   935ed026-34da-4357-bb8d-d7f444a2393b:klingai/v2.5-turbo/pro/image-to-video
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: generating
Still waiting... Checking again in 10 seconds.
Status: completed
Processing complete:\n {'id': '935ed026-34da-4357-bb8d-d7f444a2393b:klingai/v2.5-turbo/pro/image-to-video', 'status': 'completed', 'video': {'url': 'https://cdn.aimlapi.com/eagle/files/koala/Wr1zMMgriXmIeQh0GGjZV_output.mp4', 'content_type': 'video/mp4', 'file_name': 'output.mp4', 'file_size': 17744261}}

Generated Video

Original: 1180x1756

Low-res GIF preview:

"prompt": "Mona Lisa puts on glasses with her hands."