# Wan 3.0 Image to Video

> Animate a still photo into a clip of up to 30 seconds at 480P, 720P or 1080P, optionally pinning a closing frame so the model generates the motion between two images, with a matching soundtrack included.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/wan-video/wan/v3.0/image-to-video`
- **Model ID**: `wan-video/wan/v3.0/image-to-video`
- **Category**: image-to-video
- **Kind**: inference
- **Tags**: wan, wan-3.0, alibaba, image-to-video, animate-photo, first-last-frame, keyframe, video-generation, audio, sound, 30-second-video, long-form-video

## Pricing

- **480P**: $0.05 per output second
- **720P**: $0.1 per output second
- **1080P**: $0.2 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/wan-video/wan/v3.0/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/wan-video/wan/v3.0/image-to-video/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/wan-video/wan/v3.0/image-to-video/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/wan-video/wan/v3.0/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

- **`seed`** (`integer`, _optional_):
  Random seed for reproducible results. Omit for a different clip each run.
  - Range: `0` to `2147483647`

- **`audio`** (`boolean`, _optional_):
  Whether the delivered clip carries an audio track. On by default; set it to false for a silent video. The price is the same either way.
  - Default: `true`

- **`prompt`** (`string`, _optional_):
  Optional description of the motion and camera movement to animate. The scene is already fixed by the opening frame, so describe what moves rather than re-describing the picture. Chinese and English are supported, up to 20,000 characters.

- **`duration`** (`integer`, _optional_):
  Length of the generated video in whole seconds (2-30). Cost scales directly with this value.
  - Default: `5`
  - Range: `2` to `30`

- **`resolution`** (`ResolutionEnum`, _optional_):
  Output video resolution. 480P bills at $0.05 per second of finished video, 720P at $0.10, and 1080P (the default) at $0.20, so draft at 480P and re-run the keeper higher.
  - Default: `"1080P"`
  - Options: `"480P"`, `"720P"`, `"1080P"`

- **`aspect_ratio`** (`AspectRatioEnum`, _optional_):
  Frame shape of the generated video. The default \`adaptive\` pins no particular shape; set an explicit ratio when the delivery slot is fixed.
  - Default: `"adaptive"`
  - Options: `"16:9"`, `"9:16"`, `"1:1"`, `"4:3"`, `"3:4"`, `"adaptive"`

- **`end_image_url`** (`string`, _optional_):
  Optional closing frame. Supply it and the model generates the transition from the opening frame to this one; it cannot be used on its own, without an opening frame. Same formats and size limits as the opening frame, and it should share its aspect ratio.

- **`start_image_url`** (`string`, _required_):
  The opening frame the video animates from. JPEG or PNG (alpha channel not supported); width and height each between 240 and 8000 px, aspect ratio between 1:8 and 8:1, up to 20 MB.

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "audio": true,
  "prompt": "The plant grows and opens into full bloom, petals unfurling, one continuous time-lapse.",
  "duration": 10,
  "resolution": "720P",
  "aspect_ratio": "adaptive",
  "end_image_url": "https://media.modelrunner.ai/wvwMFm20HXDjTzNG0yu19.jpeg",
  "start_image_url": "https://media.modelrunner.ai/8GJHgzkT56lyndCqndaN4.jpeg"
}
```

**Output**

```json
"https://media.modelrunner.ai/nVT6Xc7ytT0elptpkvoac.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/wan-video/wan/v3.0/image-to-video \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "audio": true,
  "prompt": "The plant grows and opens into full bloom, petals unfurling, one continuous time-lapse.",
  "duration": 10,
  "resolution": "720P",
  "aspect_ratio": "adaptive",
  "end_image_url": "https://media.modelrunner.ai/wvwMFm20HXDjTzNG0yu19.jpeg",
  "start_image_url": "https://media.modelrunner.ai/8GJHgzkT56lyndCqndaN4.jpeg"
}')

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("wan-video/wan/v3.0/image-to-video", {
  input: {
  "audio": true,
  "prompt": "The plant grows and opens into full bloom, petals unfurling, one continuous time-lapse.",
  "duration": 10,
  "resolution": "720P",
  "aspect_ratio": "adaptive",
  "end_image_url": "https://media.modelrunner.ai/wvwMFm20HXDjTzNG0yu19.jpeg",
  "start_image_url": "https://media.modelrunner.ai/8GJHgzkT56lyndCqndaN4.jpeg"
}
});

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

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "wan-video/wan/v3.0/image-to-video",
        arguments={
  "audio": true,
  "prompt": "The plant grows and opens into full bloom, petals unfurling, one continuous time-lapse.",
  "duration": 10,
  "resolution": "720P",
  "aspect_ratio": "adaptive",
  "end_image_url": "https://media.modelrunner.ai/wvwMFm20HXDjTzNG0yu19.jpeg",
  "start_image_url": "https://media.modelrunner.ai/8GJHgzkT56lyndCqndaN4.jpeg"
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/wan-video/wan/v3.0/image-to-video)
- [OpenAPI Schema](https://modelrunner.ai/models/wan-video/wan/v3.0/image-to-video/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/wan-video/wan/v3.0/image-to-video/llms.txt)