# VideoSeal Watermarking

> Run VideoSeal Watermarking by infsh online or via API. from $0.00055833/sec, no setup.

**URL:** https://inference.sh/apps/infsh/videoseal
**Last updated:** 2026-11-03

---

embed or detect imperceptible watermarking for images and videos.

- app: `infsh/videoseal`
- maker: [infsh](https://inference.sh/apps/infsh.md)
- category: [video](https://inference.sh/apps/category/video.md)
- tasks: [media utilities](https://inference.sh/apps/tag/media-utilities.md)
- price: from $0.00055833/sec
- runs via: api, javascript and python sdk, belt cli, mcp

## videoseal API Guide

### About
embed or detect imperceptible watermarking for images and videos.

### Get an API Key
Create one in [settings → api keys](https://app.inference.sh/settings/keys). The examples read it from `INFERENCE_API_KEY`.

```bash
export INFERENCE_API_KEY="inf_your_key"
```

### 1. Calling the API

#### Install the Client

```bash
pip install inferencesh
```

#### Run and Get Result
Submit a request and wait for the final result.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @7xejgeh3 pinned
result = client.run({
        "app": "infsh/videoseal@7xejgeh3",
        "input": {
            "file": "https://example.com/file.png"
        }
    })

print(result["output"])
```

#### Stream Live Updates
Get real-time progress updates as the task runs.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @7xejgeh3 pinned
## stream=True yields updates as they arrive
for update in client.run({
        "app": "infsh/videoseal@7xejgeh3",
        "input": {
            "file": "https://example.com/file.png"
        }
    }, stream=True):
    if update.get("progress"):
        print(f"progress: {update['progress']}%")
    if update.get("output"):
        print(f"output: {update['output']}")
```

#### Run in the Background
Start the task and get its id back right away, then check on it with `GET /tasks/{id}`. It is done when `status` is 10 (completed), 11 (failed) or 12 (cancelled).

```python
import os
from inferencesh import inference, TaskStatus

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## returns as soon as the task is created
task = client.run({
        "app": "infsh/videoseal@7xejgeh3",
        "input": {
            "file": "https://example.com/file.png"
        }
    }, wait=False)

## later: read its current state
state = client.tasks.get(task["id"]).data
if state["status"] == TaskStatus.COMPLETED:
    print(state["output"])
```

### 2. Files

#### Automatic Upload

```python
## local file paths are automatically uploaded
result = client.run({
    "app": "infsh/videoseal@7xejgeh3",
    "input": {
        "image": "/path/to/local/image.png",  # detected & uploaded
        "audio": "https://example.com/audio.mp3",  # url passed through
    }
})
```

#### Manual Upload

```python
## upload and get a hosted URL
file = client.files.upload("/path/to/file.png")
print(file.uri)  # https://cloud.inference.sh/...
```

### 3. Schema

#### Input
- **file** (string) *required*: Input image or video file
- **mode** (string): Operation mode: 'embed' to add watermark, 'detect' to check for watermark
- **scaling_w** (number): Watermark strength (0.1-0.5). Higher = more robust but more visible. Default: 0.2
- **message** (string): Optional text message to embed (UTF-8, at most 31 bytes for the 256-bit models). Detect mode returns it in detection.message. If not provided, a random message is used.

#### Output
- **detection** (object): Detection results (for detect mode)
- **mode** (string): Operation mode that was performed
- **model_used** (string): Model that was used
- **output_file** (string): Watermarked output file (for embed mode)

## other video apps

- [VACE](https://inference.sh/apps/pruna/vace.md): $0.40/video
- [P-Video-Animate](https://inference.sh/apps/pruna/p-video-animate.md): $0.03/sec 720p, $0.06/sec 1080p
- [P-Video-Upscale](https://inference.sh/apps/pruna/p-video-upscale.md): Std: $0.01/sec 720p, $0.02 1080p, $0.04 2k, $0.08 4k; high fps 2x
- [P-Video-2-Pro-Avatar](https://inference.sh/apps/pruna/p-video-2-pro-avatar.md): $0.02/sec 480p, $0.04/sec 768p, $0.06/sec 1080p, $0.08/sec 2k, $0.12/sec 4k
- [P-Video-2-Pro-Reference](https://inference.sh/apps/pruna/p-video-2-pro-reference.md): Output: cost $0.015-$0.105/sec, speed $0.02-$0.115/sec. Ref video: $0.03-$0.06/sec. Ref img: $0.005/img. Ref audio: $0.0015-$0.0025/sec

## faq

### how much does VideoSeal Watermarking cost?

from $0.00055833/sec. you pay per run from your inference shell credits. no subscription is required.

### how do I run VideoSeal Watermarking via api?

call infsh/videoseal with the javascript or python sdk, the rest api, or `belt app run infsh/videoseal` from a terminal. the api reference on this page lists every input.

### who makes VideoSeal Watermarking?

VideoSeal Watermarking is made by infsh and runs on inference shell. every infsh app is listed at https://inference.sh/apps/infsh.

### can my agent use VideoSeal Watermarking?

yes. agents call it as a tool through the inference shell mcp server (https://api.inference.sh/mcp), the belt cli or the sdk, and pay per run like you do.

### what are the alternatives to VideoSeal Watermarking?

other video apps on inference shell include VACE, P-Video-Animate, P-Video-Upscale, P-Video-2-Pro-Avatar. they run through the same api, so switching is a one-line change.