v2-master/text-to-video

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

• klingai/v2-master-image-to-video

Model Overview

Compared to v1.6, this Kling model better aligns with the prompt and delivers more dynamic and visually appealing results.

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

Generating a video using this model involves making two sequential API calls:

• 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 or extended video from the server using the generation ID received from the first endpoint.

Below, you can find both corresponding API schemas.

API Schema

Create a video generation task and send it to the server

post

Authorizations

Body

modelundefined · enumRequiredPossible values:

ratiostring · enumOptionalDeprecatedPossible values:

aspect_ratiostring · enumOptional

The aspect ratio of the generated video frame

Possible values:

promptstringRequired

The text prompt to guide video generation

durationinteger · enumOptional

The duration of the generated video in seconds. Possible values: 5, 10

Possible 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

201Success

post

HTTPcURLJavaScriptPython

POST /v2/generate/video/kling/generation HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 133

{
  "model": "klingai/v2-master-text-to-video",
  "aspect_ratio": "16:9",
  "prompt": "text",
  "duration": 5,
  "negative_prompt": "text",
  "cfg_scale": 1
}

201Success

No content

Retrieve the generated video from the server

After sending a request for video generation, this task is added to the queue. Based on the service's load, the generation can be completed in seconds or take a bit more.

get

Authorizations

Query parameters

generation_idstringRequired

Responses

200Success

get

HTTPcURLJavaScriptPython

GET /v2/generate/video/kling/generation HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Accept: */*

200Success

No content

Code Example (Python)

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

This model produces highly detailed and natural-looking videos, so generation may take around 5–6 minutes for a 5-second video and 11-14 minutes for a 10-second video.

Full code example

import requests
import time

base_url = "https://api-staging.aimlapi.com/v2"
api_key = "<YOUR_AIMLAPI_KEY>"

# 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-master-text-to-video",
        "prompt": "A cheerful white raccoon running through a sequoia forest",
        "aspect_ratio": "16:9",
        "duration": "5",
        "cfg_scale": 0.9  
    }
 
    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()
        # print(response_data)
        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,
    }
    
    # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
    headers = {
        "Authorization": f"Bearer {api_key}", 
        "Content-Type": "application/json"
        }

    response = requests.get(url, params=params, headers=headers)
    # print("Generation:", response.json())
    return response.json()



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

Gen_ID:   10c09c56-2e00-4a64-89ec-358ff71f8144:kling-video/v2/master/text-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: 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: 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': '10c09c56-2e00-4a64-89ec-358ff71f8144:kling-video/v2/master/text-to-video', 'status': 'completed', 'video': {'url': 'https://cdn.aimlapi.com/eagle/files/lion/0jOQ9V3lSX-nz16Xu6BMV_output.mp4', 'content_type': 'video/mp4', 'file_name': 'output.mp4', 'file_size': 11664920}}

Generated Video

Original: 1280x720

Low-res GIF preview:

"prompt": "A cheerful white raccoon running through a sequoia forest"