# Seedance 1.0 Pro Fast

> Generate a short, silent video clip from a text prompt or a first-frame image — the fast, lower-cost tier of Seedance 1.0 Pro at 480p/720p.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/bytedance/seedance-v1-pro-fast`
- **Model ID**: `bytedance/seedance-v1-pro-fast`
- **Category**: image-to-video
- **Kind**: inference
- **Tags**: video, text-to-video, image-to-video, video generation, bytedance, seedance

## Pricing

- **Price**: $0.03 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/bytedance/seedance-v1-pro-fast` 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/bytedance/seedance-v1-pro-fast/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/bytedance/seedance-v1-pro-fast/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/bytedance/seedance-v1-pro-fast/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. Set for reproducible generation

- **`image`** (`string`, _optional_):
  Input image used as the first frame for image-to-video generation

- **`prompt`** (`string`, _required_):
  Text prompt for video generation

- **`duration`** (`integer`, _optional_):
  Video duration in seconds
  - Default: `5`
  - Range: `3` to `12`

- **`resolution`** (`resolution`, _optional_):
  Video resolution
  - Default: `"720p"`
  - Options: `"480p"`, `"720p"`

- **`aspect_ratio`** (`aspect_ratio`, _optional_):
  Video aspect ratio. Ignored if an image is used.
  - Default: `"16:9"`
  - Options: `"16:9"`, `"4:3"`, `"1:1"`, `"3:4"`, `"9:16"`, `"21:9"`, `"9:21"`, `"adaptive"`

- **`camera_fixed`** (`boolean`, _optional_):
  Whether to fix camera position
  - Default: `false`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "a hot air balloon festival at sunrise over rolling lavender fields, balloons slowly lifting off, soft golden light",
  "duration": 5,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "camera_fixed": false
}
```

**Output**

```json
"https://media.modelrunner.ai/S9weNtR0KHwhxLJXYqIak.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/bytedance/seedance-v1-pro-fast \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "a hot air balloon festival at sunrise over rolling lavender fields, balloons slowly lifting off, soft golden light",
  "duration": 5,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "camera_fixed": false
}')

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("bytedance/seedance-v1-pro-fast", {
  input: {
  "prompt": "a hot air balloon festival at sunrise over rolling lavender fields, balloons slowly lifting off, soft golden light",
  "duration": 5,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "camera_fixed": false
}
});

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

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "bytedance/seedance-v1-pro-fast",
        arguments={
  "prompt": "a hot air balloon festival at sunrise over rolling lavender fields, balloons slowly lifting off, soft golden light",
  "duration": 5,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "camera_fixed": false
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/bytedance/seedance-v1-pro-fast)
- [OpenAPI Schema](https://modelrunner.ai/models/bytedance/seedance-v1-pro-fast/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/bytedance/seedance-v1-pro-fast/llms.txt)