# Grok Imagine 1.5 Image to Video

> Turn a still image into a short video clip guided by a motion prompt, with synchronized audio generated automatically and included free.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/xai/grok-imagine-1.5/image-to-video`
- **Model ID**: `xai/grok-imagine-1.5/image-to-video`
- **Category**: image-to-video
- **Kind**: inference
- **Tags**: xai, grok, image-to-video, audio

## Pricing

- **Price**: $0.14 per output second

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/xai/grok-imagine-1.5/image-to-video` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/xai/grok-imagine-1.5/image-to-video/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/xai/grok-imagine-1.5/image-to-video/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/xai/grok-imagine-1.5/image-to-video/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`prompt`** (`string`, _required_):
  Describe the motion and action you want in the generated video.

- **`duration`** (`integer`, _optional_):
  Length of the generated video in seconds (1-15).
  - Default: `6`
  - Range: `1` to `15`

- **`image_url`** (`string`, _required_):
  The URL of the starting image to animate into a video.

- **`resolution`** (`resolution`, _optional_):
  Video resolution - 480p for faster, cheaper generation, 720p for a sharper result.
  - Default: `"720p"`
  - Options: `"480p"`, `"720p"`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "The brass pendulum swings steadily back and forth and the weights slowly descend while warm afternoon light drifts across the white wall and fine dust motes float through the air",
  "duration": 5,
  "image_url": "https://media.modelrunner.ai/nDaJs47xN2swuGDYlMDS4.jpeg",
  "resolution": "720p"
}
```

**Output**

```json
"https://media.modelrunner.ai/NZslI7JzCyIfOZTZlPfsw.mp4"
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/xai/grok-imagine-1.5/image-to-video \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "The brass pendulum swings steadily back and forth and the weights slowly descend while warm afternoon light drifts across the white wall and fine dust motes float through the air",
  "duration": 5,
  "image_url": "https://media.modelrunner.ai/nDaJs47xN2swuGDYlMDS4.jpeg",
  "resolution": "720p"
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

```javascript
import { modelrunner } from "@modelrunner/client";

const result = await modelrunner.subscribe("xai/grok-imagine-1.5/image-to-video", {
  input: {
  "prompt": "The brass pendulum swings steadily back and forth and the weights slowly descend while warm afternoon light drifts across the white wall and fine dust motes float through the air",
  "duration": 5,
  "image_url": "https://media.modelrunner.ai/nDaJs47xN2swuGDYlMDS4.jpeg",
  "resolution": "720p"
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "xai/grok-imagine-1.5/image-to-video",
        arguments={
  "prompt": "The brass pendulum swings steadily back and forth and the weights slowly descend while warm afternoon light drifts across the white wall and fine dust motes float through the air",
  "duration": 5,
  "image_url": "https://media.modelrunner.ai/nDaJs47xN2swuGDYlMDS4.jpeg",
  "resolution": "720p"
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/xai/grok-imagine-1.5/image-to-video)
- [OpenAPI Schema](https://modelrunner.ai/models/xai/grok-imagine-1.5/image-to-video/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/xai/grok-imagine-1.5/image-to-video/llms.txt)