# RIFE Video Interpolation

> Interpolate new in-between frames to boost a video's frame rate and produce smooth slow-motion.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/megvii-research/rife/video`
- **Model ID**: `megvii-research/rife/video`
- **Category**: video-to-video
- **Kind**: inference
- **Tags**: rife, frame-interpolation, interpolation, video-to-video, fps, slow-motion, megvii

## Pricing

- **Estimated Price**: $0.0032365 average per output

## 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/megvii-research/rife/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/megvii-research/rife/video/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/megvii-research/rife/video/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/megvii-research/rife/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

- **`fps`** (`integer`, _optional_):
  Target output frame rate (1-60). Only used when use_calculated_fps is false.
  - Default: `8`
  - Range: `1` to `60`

- **`loop`** (`boolean`, _optional_):
  Interpolate from the final frame back to the first to create a seamless looping video.
  - Default: `false`

- **`video_url`** (`string`, _required_):
  URL of the video whose frame rate to interpolate.

- **`num_frames`** (`integer`, _optional_):
  Number of intermediate frames to generate between each pair of input frames (1-4). Higher values yield a higher output frame rate and smoother slow-motion.
  - Default: `1`
  - Range: `1` to `4`

- **`use_calculated_fps`** (`boolean`, _optional_):
  Derive the output frame rate from the input frame rate times num_frames. When false, the fps value is used instead.
  - Default: `true`

- **`use_scene_detection`** (`boolean`, _optional_):
  Split the video into scenes before interpolating so frames are not blended across hard cuts.
  - Default: `false`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "fps": 8,
  "loop": false,
  "video_url": "https://media.modelrunner.ai/wIRSbtfvYKHdIGXV-rife-interpolation-input.mp4",
  "num_frames": 2,
  "use_calculated_fps": true,
  "use_scene_detection": false
}
```

**Output**

```json
"https://media.modelrunner.ai/2vrjAtnYjL7ef0JrH3Hkj.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/megvii-research/rife/video \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "fps": 8,
  "loop": false,
  "video_url": "https://media.modelrunner.ai/wIRSbtfvYKHdIGXV-rife-interpolation-input.mp4",
  "num_frames": 2,
  "use_calculated_fps": true,
  "use_scene_detection": 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("megvii-research/rife/video", {
  input: {
  "fps": 8,
  "loop": false,
  "video_url": "https://media.modelrunner.ai/wIRSbtfvYKHdIGXV-rife-interpolation-input.mp4",
  "num_frames": 2,
  "use_calculated_fps": true,
  "use_scene_detection": false
}
});

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

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "megvii-research/rife/video",
        arguments={
  "fps": 8,
  "loop": false,
  "video_url": "https://media.modelrunner.ai/wIRSbtfvYKHdIGXV-rife-interpolation-input.mp4",
  "num_frames": 2,
  "use_calculated_fps": true,
  "use_scene_detection": false
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/megvii-research/rife/video)
- [OpenAPI Schema](https://modelrunner.ai/models/megvii-research/rife/video/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/megvii-research/rife/video/llms.txt)