> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Video Generation

> Generate videos from text prompts in minutes

## Overview

Generate videos from text descriptions, reference images, or existing videos.

## Minimal Example (Synchronous)

Note: Synchronous generation can take 5-15 minutes to complete for certain models, we recommend using the async queue and polling for long-running jobs (See Below).

<CodeGroup>
  ```python Python theme={null}
  import requests

  response = requests.post(
      "https://hub.oxen.ai/api/ai/videos/generate",
      headers={
          "Authorization": "Bearer YOUR_API_KEY",
          "Content-Type": "application/json",
      },
      json={
          "model": "kling-video-v2-6-pro-text-to-video",
          "prompt": "A red balloon floating upward through blue sky",
          "duration": 5,
      },
  )

  data = response.json()
  print("Video URL:", data["videos"][0]["url"])
  ```

  ```bash cURL theme={null}
  curl -X POST https://hub.oxen.ai/api/ai/videos/generate \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "kling-video-v2-6-pro-text-to-video",
      "prompt": "A red balloon floating upward through blue sky",
      "duration": 5
    }'
  ```
</CodeGroup>

## Async Generation (Recommended)

For video generation, using the async queue avoids long-lived HTTP connections:

<CodeGroup>
  ```python Python theme={null}
  import requests
  import time

  API_KEY = "YOUR_API_KEY"
  MODEL = "kling-video-v2-6-pro-text-to-video"
  HEADERS = {
      "Authorization": f"Bearer {API_KEY}",
      "Content-Type": "application/json",
  }

  # 1. Enqueue
  response = requests.post(
      "https://hub.oxen.ai/api/ai/queue",
      headers=HEADERS,
      json={
          "model": MODEL,
          "prompt": "A sunset timelapse over the ocean",
          "duration": 5,
      },
  )
  generation_id = response.json()["generations"][0]["generation_id"]
  print(f"Enqueued generation: {generation_id}")

  # 2. Poll until done
  while True:
      data = requests.get(
          f"https://hub.oxen.ai/api/ai/queue/{generation_id}",
          headers=HEADERS,
      ).json()
      print(f"Status: {data['status']}")
      if data["status"] in {"succeeded", "failed", "cancelled"}:
          break
      time.sleep(10)

  if data["status"] == "succeeded":
      print(f"Done! Result: {data['result_url']}")
  else:
      print(f"Generation {data['status']}: {data.get('error_message')}")
  ```

  ```bash cURL theme={null}
  # 1. Enqueue
  curl -X POST https://hub.oxen.ai/api/ai/queue \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "kling-video-v2-6-pro-text-to-video",
      "prompt": "A sunset timelapse over the ocean",
      "duration": 5
    }'

  # 2. Poll until done
  curl -H "Authorization: Bearer YOUR_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue?model=kling-video-v2-6-pro-text-to-video"
  ```
</CodeGroup>

## What's Next

* [Video Generation Reference](/inference-api/reference/video_generation) for the full parameter list
* [Kling O3 Pro: Reference to Video](/inference-api/reference/models/kling_o3_pro_reference_to_video) for multi-shot video with reference images
* [Async Queue Reference](/inference-api/reference/async_queue) for batch generation