Veo 2 (Image-to-Video)

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

veo2/image-to-video

Overview

An advanced multimodal (image + text) AI model that transforms static images into high-quality, dynamic video content. It builds upon the success of Google's Veo2 text-to-video model, offering unprecedented control and realism in video generation from still images.

Key Features:

Faithful content preservation from source images.
Intuitive motion generation with physics-aware movement.

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 examples for both endpoint calls.

API Schemas

Create a video generation task and send it to the server

You can generate a video using this API.

post

Authorizations

Body

modelundefined · 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.

tail_image_urlstring · uriOptional

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

promptstringRequired

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

aspect_ratiostring · enumOptional

The aspect ratio of the generated video.

Possible values:

durationinteger · enumOptional

The length of the output video in seconds.

Possible values:

negative_promptstringOptional

The description of elements to avoid in the generated video.

seedintegerOptional

Varying the seed integer is a way to get different results for the same other request parameters. Using the same value for an identical request will produce similar results. If unspecified, a random number is chosen.

enhance_promptbooleanOptional

Whether to enhance the video generation.

Default: true

Responses

201Success

post

POST /v2/generate/video/google/generation HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 211

{
  "model": "veo2/image-to-video",
  "image_url": "https://example.com",
  "tail_image_url": "https://example.com",
  "prompt": "text",
  "aspect_ratio": "16:9",
  "duration": 5,
  "negative_prompt": "text",
  "seed": 1,
  "enhance_prompt": true
}

201Success

No content

Fetch the video

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 complete, the response will include the final result — with the generated video URL and additional metadata.

get

Authorizations

Query parameters

generation_idstringRequired

Responses

200Success

get

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

200Success

No content

Full Example: Generating and Retrieving the Video From the Server

We have a classic reproduction of the famous da Vinci painting. Let's ask the model to generate a video where the Mona Lisa puts on glasses.

Generation may take around 40-50 seconds for a 5-second video.

import requests
import time

# replace <YOUR_AIMLAPI_KEY> with your actual AI/ML API 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/google/generation"
    headers = {
        "Authorization": f"Bearer {api_key}", 
    }

    data = {
        "model": "veo2/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/google/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 = 600
        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()

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: "veo2/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",
  });

  const url = new URL(`${baseUrl}/generate/video/google/generation`);
  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}/generate/video/google/generation`);
  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 10 s until completion or timeout
function main() {
  generateVideo((genResponse) => {
    if (!genResponse || !genResponse.id) {
      console.error("Failed to start generation");
      return;
    }

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

    const startTime = Date.now();
    const timeout = 600000;

    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;
        console.log("Status:", status);

        if (["waiting", "active", "queued", "generating"].includes(status)) {
          console.log("Still waiting... Checking again in 10 seconds.");
          setTimeout(checkStatus, 10000);
        } else {
          console.log("Processing complete:\n", responseData);
        }
      });
    };

    checkStatus();
  });
}

main();

Response

Gen_ID:   9bf4e6f6-dae7-41c7-94aa-354443b300c6:veo2/image-to-video
Status: queued
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': '812dbc37-f15a-46a4-a058-8477bd243f5a:veo2/image-to-video', 'status': 'completed', 'video': {'url': 'https://cdn.aimlapi.com/eagle/files/rabbit/D7LD6oJwKMF9gk9KjqwDV_output.mp4', 'content_type': 'video/mp4', 'file_name': 'output.mp4', 'file_size': 6827705}}

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