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v2-master/image-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

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 two corresponding API schemas and an example with both endpoint calls.

API Schemas

Create a video generation task and send it to the server

post
Authorizations
Body
modelundefined · enumRequiredPossible values:
typestring · enumOptionalPossible values:
image_urlstringRequired

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.

tail_image_urlstringOptional

URL of the image to be used as the last frame of the video.

static_maskstringOptional

URL of the image for Static Brush Application Area (Mask image created by users using the motion brush).

negative_promptstringOptional

The description of elements to avoid in the generated video.

durationinteger · enumOptional

The length of the output video in seconds.

Possible values:
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.

Default: 0.5
external_task_idstringOptional

Customized Task ID

promptstringOptional

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

Responses
201Success
201Success
No content

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 generation_id, obtained from the endpoint described above. If the video generation task status is complete, the response will include the final result — with the generated video URL and additional metadata.

get
Authorizations
Query parameters
generation_idstringRequired
Responses
200Success
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.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-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()
        # 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:   88a64d31-cce4-41e8-b9d8-5392e8c2a6d4:kling-video/v2/master/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: 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': '88a64d31-cce4-41e8-b9d8-5392e8c2a6d4:kling-video/v2/master/image-to-video', 'status': 'completed', 'video': {'url': 'https://cdn.aimlapi.com/eagle/files/kangaroo/iKWC3BJOdav7Cy8cWSO9k_output.mp4', 'content_type': 'video/mp4', 'file_name': 'output.mp4', 'file_size': 5150376}}
Generated Video

Original: 784x1172

Low-res GIF preview:

"prompt": "Mona Lisa puts on glasses with her hands."
Previousv1.6-pro/effectsNextv2-master/text-to-video

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