# BiRefNet V2 Background Removal API

> Fjerner åbne baggrunde og udfører matting ved op til 2304 px med valgfrit maskeoutput.

BiRefNet V2 adskiller motivet fra baggrunden med flere modelvarianter (light, heavy, matting, portrait, dynamic) og arbejdsopløsninger op til 2304x2304. Returnerer udskæringen og valgfrit masken i PNG, WebP eller GIF.

- Model id: `birefnet/v2`
- Made by: BiRefNet
- Category: Background Removal
- Output: image
- Estimated price per run: $0.002 / run (cheapest run $0.002 (2 API credits))
- Run: `POST https://api.doitong.com/v1/run/birefnet/v2`
- Quote: `POST https://api.doitong.com/v1/quote/birefnet/v2`
- Docs page: https://doitong.com/da/developers/models/birefnet/v2

## Pricing

Price: **$0.002 / run**. Estimated from the typical cost of one run. The quote is exactly what the run charges.

The full example input below costs $0.002 = 2 API credits (1 request × 2 API credits).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.com/v1/quote/birefnet/v2` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `model` |  | enum (string) | no | `General Use (Light)` | `General Use (Light)`, `General Use (Light 2K)`, `General Use (Heavy)`, `Matting`, `Portrait`, `General Use (Dynamic)` | Valg af model til fjernelse af baggrund: 'General Use (Light)' svarer til BiRefNet, er BiRefNet-lagerets oprindelige model og anbefales til de fleste formål. 'General Use (Light 2K)' svarer til BiRefNet_lite-2K og bruger den oprindelige model, der er trænet på 2K-billeder. 'General Use (Heavy)' svarer til BiRefNet_lite og giver større nøjagtighed med langsommere behandling. 'Matting' svarer til BiRefNet-matting og er specifikt trænet til billedmatting. 'Portrait' svarer til BiRefNet-portrait og er specifikt trænet på portrætbilleder. 'General Use (Dynamic)' svarer til BiRefNet_dynamic og understøtter variable opløsninger fra 256x256 til 2304x2304. |
| `operating_resolution` |  | enum (string) | no | `1024x1024` | `1024x1024`, `2048x2048`, `2304x2304` | Arbejdsopløsning, hvor '2304x2304' er begrænset til modellen 'General Use (Dynamic)'. For inputbilleder i høj opløsning forbedrer højere indstillinger outputtets nøjagtighed. |
| `output_mask` |  | boolean | no | `false` |  | Styrer, om masken til fjernelse af baggrunden inkluderes i outputtet. |
| `refine_foreground` |  | boolean | no | `true` |  | Styrer forgrundsforfinelse baseret på den estimerede maske. |
| `image_url` | `image` | string (image URL) | yes |  |  | URL, der identificerer billedet, hvis baggrund skal fjernes. |
| `output_format` |  | enum (string) | no | `png` | `webp`, `png`, `gif` | Format for det resulterende billede. |
| `mask_only` |  | boolean | no | `false` |  | Kontakt for kun-maske-output: `True` omgår forgrundsforfinelse og lader billedet være uændret, mens kun segmenteringsmasken returneres. Når der kun er brug for en maske, undgår dette ekstra beregning og reducerer overførte data. |

File parameters (`image`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## Example input

Required parameters only:

```json
{
  "image": "https://content.doitong.com/images/showcase/v3/gen_sneaker_ad.jpg"
}
```

A fuller example:

```json
{
  "image": "https://content.doitong.com/images/showcase/v3/gen_sneaker_ad.jpg"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/birefnet/v2?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "https://content.doitong.com/images/showcase/v3/gen_sneaker_ad.jpg"
  }'

# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/v1";
const headers = {
  "Authorization": "Bearer " + process.env.DOITONG_API_KEY,
  "Content-Type": "application/json",
};

// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/birefnet/v2?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "image": "https://content.doitong.com/images/showcase/v3/gen_sneaker_ad.jpg"
  }),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);

// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
  res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
  request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
```

### Python

```python
import os
import requests

API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "image": "https://content.doitong.com/images/showcase/v3/gen_sneaker_ad.jpg",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/birefnet/v2", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
    raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])

# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
    request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
    raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
```

### MCP

The MCP server at `https://api.doitong.com/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "birefnet/v2",
      "input": {
        "image": "https://content.doitong.com/images/showcase/v3/gen_sneaker_ad.jpg"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "birefnet/v2",
  "status": "succeeded",
  "created_at": "2026-10-20T12:00:00Z",
  "started_at": "2026-10-20T12:00:02Z",
  "completed_at": "2026-10-20T12:00:09Z",
  "queue_position": null,
  "cost": {
    "credits": 2,
    "usd": "0.002",
    "charged": 2,
    "refunded": 0
  },
  "input": {
    "image": "https://content.doitong.com/images/showcase/v3/gen_sneaker_ad.jpg"
  },
  "output": {
    "image": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768
    },
    "mask_image": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768
    }
  },
  "outputs": [
    {
      "type": "image",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `image` | object {url, content_type, file_name, file_size, width, height} | Billede efter fjernelse af baggrunden eller segmenteringsmasken, når `mask_only` er `True`. |
| `mask_image` | object {url, content_type, file_name, file_size, width, height} \| null | Maske anvendt under fjernelse af baggrunden. |

## Related models

- [Birefnet Background Removal](https://doitong.com/developers/models/birefnet.md) (`birefnet`): $0.002 / run
- [Bria RMBG 2.0](https://doitong.com/developers/models/bria/background/remove.md) (`bria/background/remove`): from $0.018 / request
- [Birefnet](https://doitong.com/developers/models/birefnet/v2/video.md) (`birefnet/v2/video`): $0.000575 / second of processing time
- [ben-v2-image](https://doitong.com/developers/models/ben/v2/image.md) (`ben/v2/image`): $0.102 / run
- [Ben-Video-Bg-Rm](https://doitong.com/developers/models/ben/v2/video.md) (`ben/v2/video`): $0.001 / megapixel of input video
- [Bria Guided Remove Background](https://doitong.com/developers/models/bria/guided-remove-background.md) (`bria/guided-remove-background`): $0.03 / image
- [Bria's VRMBG 3.0](https://doitong.com/developers/models/bria/video/background-removal/v3.md) (`bria/video/background-removal/v3`): $0.05 / second of input video
- [Feynobg Background Remover](https://doitong.com/developers/models/feynobg.md) (`feynobg`): $0.001 / request

## Links

- HTML page: https://doitong.com/developers/models/birefnet/v2
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=birefnet/v2
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/birefnet/v2