Luma Ray 2

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

luma/ray-2

This model generates up to 9-second clips at 4K, compared to lower resolutions and shorter durations in Ray 1.6. You can specify the first and last frames as images or extend previously generated videos by passing their generation IDs. Looped videos are also supported.

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

Create a video generation task and send it to the server

You can generate a video using this API. In the basic setup, you only need a reference image and a prompt. This endpoint creates and sends a video generation task to the server — and returns a generation ID.

post

Body

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:

loopbooleanOptional

Whether to loop the video

Default: false

modelundefined · enumRequiredPossible values:

resolutionstring · enumOptional

The resolution of the output video, where the number refers to the short side in pixels.

Possible values:

durationinteger · enumOptional

The length of the output video in seconds.

Possible values:

Responses

200

Successfully generated video

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_API_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      "model": "luma/ray-2",
      "prompt": "A menacing evil dragon appears in a distance above the tallest mountain, then rushes toward the camera with its jaws open, revealing massive fangs. We see it's coming."
    }),
  });

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

main();

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
    }
  }
}

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 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
    }
  }
}

Full Example: Generating and Retrieving the Video From the Server

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

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

    data = {
        "model": "luma/ray-2",
        "prompt": "The camera moves down, dives underwater and moves through a dark, moody world of greenish light and drifting plants. Giant white koi fish emerge from the shadows and turn curiously toward the camera as it passes, their scales shimmering faintly in the murky depths.",
        "keyframes":{    
            "frame0": {
                "type": "image",
                "url": "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/landscape.jpg",
            },
            "frame1": {
                "type": "image",
                "url": "https://cdn.aimlapi.com/assets/content/white-fish.png",
            },
        },
        "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}/video/generations"
    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("Generation 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: "luma/ray-2",
      prompt: "The camera moves down, dives underwater and moves through a dark, moody world of greenish light and drifting plants. Giant white koi fish emerge from the shadows and turn curiously toward the camera as it passes, their scales shimmering faintly in the murky depths.",
      keyframes: {
        frame0: {
          type: "image",
          url: "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/landscape.jpg"
        },
        frame1: {
          type: "image",
          url: "https://cdn.aimlapi.com/assets/content/white-fish.png"
        }
      },
      duration: "5"
  });

  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 10 seconds 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

Generation ID:   7c880e75-9892-4238-8464-49cb0c6deabd:luma/ray-2
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': '7c880e75-9892-4238-8464-49cb0c6deabd:luma/ray-2', 'status': 'completed', 'video': {'url': 'https://cdn.aimlapi.com/luma/dream_machine/25f7f772-d8f4-494e-9cdf-ab5a2c7ce3fe/2a50c77c-2e44-4bfd-915b-9df61f5ff202_resultdbe5e2f21db1effd.mp4'}}

Processing time: ~1 min 48 sec.

Original (1280x720, without sound):

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Last updated 9 days ago

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