video-o1-video-to-video-reference

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

klingai/video-o1-video-to-video-reference

Model Overview

The model that performs video-to-video editing by applying a reference style or identity to source footage, enabling appearance transfer across clips while preserving motion and structure from the original video. The model is well-suited for maintaining consistent characters, branding elements, or artistic style over multiple outputs derived from related source videos.

How to Make a Call

Step-by-Step Instructions

1️ Setup You Can’t Skip

▪️ Create an Account: Visit the AI/ML API website and create an account (if you don’t have one yet). ▪️ Generate an API Key: After logging in, navigate to your account dashboard and generate your API key. Ensure the key is enabled on the UI.

2️ Copy the code example

At the bottom of this page, you'll find a code example that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

3️ Modify the code example

▪️ Replace <YOUR_AIMLAPI_KEY> with your actual AI/ML API key. ▪️ Adjust the input field used by this model (for example, prompt, input text, instructions, media source, or other model-specific input) to match your request.

4️ ^_(Optional) Adjust other optional parameters if needed

Only the required parameters shown in the example are needed to run the request, but you can include optional parameters to fine-tune behavior. Below, you can find the corresponding API schema, which lists all available parameters and usage notes.

5️ Run your modified code

Run your modified code inside your development environment. Response time depends on many factors, but for simple requests it rarely exceeds a few seconds.

If you need a more detailed walkthrough for setting up your development environment and making a request step-by-step, feel free to use our Quickstart guide.

API Schema

post

Body

modelstring · enumRequiredPossible values:

promptstring · max: 2500Required

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

video_urlstring · uriRequired

A HTTPS URL pointing to a video or a data URI containing a video. This video will be used as a reference during generation.

image_liststring · uri[] · min: 1 · max: 4Optional

Array of image URLs for multi-image-to-video generation.

aspect_ratiostring · enumOptional

The aspect ratio of the generated video.

Default: 16:9Possible values:

durationinteger · enumOptional

The length of the output video in seconds.

Default: 5Possible values:

keep_audiobooleanOptional

Whether to keep the original audio from the video.

Default: false

Responses

200Success

application/json

post

/v2/video/generations

async function main() {
  const response = await fetch('https://api.aimlapi.com/v2/video/generations', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      "model": "klingai/video-o1-video-to-video-reference",
      "video_url": "https://zovi0.github.io/public_misc/kling-v2-master-t2v-racoon.mp4",
      "prompt": "Add a small fairy as a rider on the raccoon’s back. She must have a black-and-golden face and a cloak in the colors of a dark emerald tropical butterfly with bright blue shimmering spots."
    }),
  });

  const data = await response.json();
  console.log(JSON.stringify(data, null, 2));
}

main();

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 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

Query parameters

generation_idstringRequired

Responses

200

Successfully generated video

application/json

get

/v2/video/generations

GET /v2/video/generations?generation_id=text HTTP/1.1
Host: api.aimlapi.com
Accept: */*

200

Successfully generated video

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

Code Example

The code below creates a video generation task, then automatically polls the server every 15 seconds until it finally receives the video URL.

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}/video/generations"
    headers = {
        "Authorization": f"Bearer {api_key}", 
    }

    data = {
        "model": "klingai/video-o1-video-to-video-reference",
        "prompt":'''
            Add a small fairy as a rider on the raccoon’s back. She must have a black-and-golden face and a cloak in the colors of a dark emerald tropical butterfly with bright blue shimmering spots.
        ''',
        "video_url": "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/racoon-in-the-forest.mp4"      
    }
 
    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}/video/generations"
    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)

    # Try to retrieve the video from the server every 15 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")
            
            if status in ["waiting", "queued", "generating"]:
                print(f"Status: {status}. Checking again in 15 seconds.")
                time.sleep(15)
            else:
                print("Processing complete:\n", response_data)
                return response_data

        print("Timeout reached. Stopping.")
        return None


if __name__ == "__main__":
    main()

const https = require("https");
const { URL } = require("url");

// Replace <YOUR_AIMLAPI_KEY> with your actual AI/ML API key
const apiKey = "<YOUR_AIMLAPI_KEY>";
const baseUrl = "https://api.aimlapi.com/v2";

// Creating and sending a video generation task to the server
function generateVideo(callback) {
  const data = JSON.stringify({
    model: "klingai/video-o1-video-to-video-reference",
    prompt: `
    Add a small fairy as a rider on the raccoon’s back. She must have a black-and-golden face and a cloak in the colors of a dark emerald tropical butterfly with bright blue shimmering spots.`,
    video_url: "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/racoon-in-the-forest.mp4",
  });

  const url = new URL(`${baseUrl}/video/generations`);
  const options = {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${apiKey}`,
      "Content-Type": "application/json",
      "Content-Length": Buffer.byteLength(data),
    },
  };

  const req = https.request(url, options, (res) => {
    let body = "";
    res.on("data", (chunk) => body += chunk);
    res.on("end", () => {
      if (res.statusCode >= 400) {
        console.error(`Error: ${res.statusCode} - ${body}`);
        callback(null);
      } else {
        const parsed = JSON.parse(body);
        callback(parsed);
      }
    });
  });

  req.on("error", (err) => console.error("Request error:", err));
  req.write(data);
  req.end();
}

// Requesting the result of the task from the server using the generation_id
function getVideo(genId, callback) {
  const url = new URL(`${baseUrl}/video/generations`);
  url.searchParams.append("generation_id", genId);

  const options = {
    method: "GET",
    headers: {
      "Authorization": `Bearer ${apiKey}`,
      "Content-Type": "application/json",
    },
  };

  const req = https.request(url, options, (res) => {
    let body = "";
    res.on("data", (chunk) => body += chunk);
    res.on("end", () => {
      const parsed = JSON.parse(body);
      callback(parsed);
    });
  });

  req.on("error", (err) => console.error("Request error:", err));
  req.end();
}

// Initiates video generation and checks the status every 15 seconds until completion or timeout
function main() {
    generateVideo((genResponse) => {
        if (!genResponse || !genResponse.id) {
            console.error("No generation ID received.");
            return;
        }

        const genId = genResponse.id;
        console.log("Generation ID:", genId);

        const timeout = 1000 * 1000; // 1000 sec
        const interval = 15 * 1000; // 15 sec
        const startTime = Date.now();

        const checkStatus = () => {
            if (Date.now() - startTime >= timeout) {
                console.log("Timeout reached. Stopping.");
                return;
            }

            getVideo(genId, (responseData) => {
                if (!responseData) {
                    console.error("Error: No response from API");
                    return;
                }

                const status = responseData.status;
        
                if (["waiting", "queued", "generating"].includes(status)) {
                    console.log(`Status: ${status}. Checking again in 15 seconds.`);
                    setTimeout(checkStatus, interval);
                } else {
                    console.log("Processing complete:\n", responseData);
                }
            });
        };
        checkStatus();
    })
}

main();

Response

Generation ID:   6a3ada23-8eb3-4e29-948a-59e5bc2074c5:klingai/video-o1-video-to-video-reference
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Status: generating. Checking again in 15 seconds.
Processing complete:
 {'id': '6a3ada23-8eb3-4e29-948a-59e5bc2074c5:klingai/video-o1-video-to-video-reference', 'status': 'completed', 'video': {'url': 'https://cdn.aimlapi.com/flamingo/files/b/0a878546/2Wt0iDavlysQHFuP3_8I5_output.mp4'}}

Processing time: ~ 3 min 23 sec.

Generated video (1920x1080, without sound):

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