# IP Adapter Face ID API

> Menjana imej yang sepadan dengan wajah daripada satu foto atau fail zip foto, berasaskan SD 1.5 atau SDXL

Menyesuaikan penjanaan dengan wajah yang diberikan sebagai `face_image_url`, atau zip beberapa wajah melalui `face_images_data_url` yang pembenamannya dipuratakan. `model_type` memilih antara varian SD 1.5 dan SDXL, manakala titik semak asas boleh diubah dengan parameter repo. `width` dan `height` berjulat 512 hingga 1024, dan `num_inference_steps` sehingga 200.

- Model id: `ip-adapter-face-id`
- Made by: Tencent
- Category: Image Editing
- Output: image
- Estimated price per run: $0.015 / image (cheapest run $0.06 (60 API credits))
- Run: `POST https://api.doitong.com/v1/run/ip-adapter-face-id`
- Quote: `POST https://api.doitong.com/v1/quote/ip-adapter-face-id`
- Docs page: https://doitong.com/ms/developers/models/ip-adapter-face-id

## Pricing

Price: **$0.015 / image**. Estimated from the typical cost of one run, per output (`num_samples`). The quote is exactly what the run charges.

The full example input below costs $0.06 = 60 API credits (4 images × 15 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/ip-adapter-face-id` 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

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `model_type` |  | enum (string) | no | `1_5-v1` | `1_5-v1`, `1_5-v1-plus`, `1_5-v2-plus`, `SDXL-v1`, `SDXL-v2-plus`, `1_5-auraface-v1` | Pemilihan model, dengan 1_5 sebagai lalai dan pilihan disyorkan untuk kebanyakan penggunaan. |
| `prompt` |  | string | yes |  |  | Terangkan imej yang anda mahu dijana dengan sedetail mungkin untuk mendapatkan hasil terbaik. |
| `face_image_url` |  | string (image URL) \| null | no |  |  | Imej rujukan padanan wajah, diskalakan dan dipangkas kepada 640x640 apabila dimensinya berbeza daripada saiz tersebut. |
| `face_images_data_url` |  | string \| null | no |  |  | URL arkib zip imej wajah, digunakan untuk membina ID wajah yang lebih tepat dengan mempuratakan embedding daripada imej yang diarkibkan. |
| `negative_prompt` |  | string | no | `blurry, low resolution, bad, ugly, low quality, pixelated, interpolated, compression artifacts, noisey, grainy` |  | Prompt negatif yang menyatakan butiran imej tidak diingini. Ia boleh mengecualikan warna, objek, pemandangan atau ciri dan kualiti halus seperti misai, kabur atau resolusi rendah. |
| `seed` |  | integer \| null | no |  |  | Seed untuk menghasilkan semula imej dalam Stable Diffusion. Imej boleh dihasilkan semula dengan tepat pada setiap larian dengan mengekalkan versi model, prompt dan seed tanpa perubahan. |
| `guidance_scale` |  | number | no | `7.5` | 0 – 16 | Kekuatan pematuhan prompt untuk pemilihan imej, dinyatakan sebagai CFG (Classifier Free Guidance). |
| `num_inference_steps` |  | integer | no | `50` | 1 – 200 | Bilangan langkah inferens penjanaan imej, dengan kualiti imej lebih baik pada bilangan lebih tinggi tetapi masa penjanaan lebih lama. |
| `num_samples` |  | integer | no | `4` | 1 – 4 | Bilangan sampel untuk ID wajah, lalainya ialah 4. Meningkatkan bilangan ini meningkatkan kualiti imej tetapi memanjangkan masa penjanaan. |
| `width` |  | integer | no | `512` | 512 – 1,024 | Lebar imej yang terhasil. |
| `height` |  | integer | no | `512` | 512 – 1,024 | Tinggi imej yang terhasil. |
| `face_id_det_size` |  | integer | no | `640` | 64 – 640 | Tetapkan saiz model pengesanan wajah, dan kurangkannya jika model tidak dapat mencari wajah dalam imej anda. Tetapan yang lebih besar memberikan pengesanan yang lebih tepat, tetapi berjalan lebih perlahan dan lebih berkemungkinan gagal mengesan wajah sepenuhnya. |
| `base_1_5_model_repo` |  | string | no | `SG161222/Realistic_Vision_V4.0_noVAE` |  | URL model asas 1.5; lalainya ialah `SG161222/Realistic_Vision_V4.0_noVAE`. |
| `base_sdxl_model_repo` |  | string | no | `SG161222/RealVisXL_V3.0` |  | URL model asas SDXL; lalainya ialah `SG161222/RealVisXL_V3.0`. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Pemeriksaan boleh dilonggarkan dengan menetapkannya kepada false, jika pemanggil dibenarkan. |

File parameters (`face_image_url`) 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
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

## 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/ip-adapter-face-id?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }'

# 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/ip-adapter-face-id?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  }),
});
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 = {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ip-adapter-face-id", 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": "ip-adapter-face-id",
      "input": {
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
      },
      "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": "ip-adapter-face-id",
  "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": 60,
    "usd": "0.06",
    "charged": 60,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "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
    },
    "seed": 42
  },
  "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} | Maklumat fail untuk imej yang dijana. |
| `seed` | integer | Seed penjanaan imej, termasuk nilai yang dipilih secara rawak jika input tidak membekalkan seed; jika tidak, nilai yang dibekalkan diulang. |

## Related models

- [FLUX.1 Kontext \[pro\]](https://doitong.com/developers/models/flux-pro/kontext.md) (`flux-pro/kontext`): $0.04 / image
- [GPT Image 2.5 Edit](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/edit.md) (`openai/gpt-image-2.5/flare/edit`): from $0.12 / image
- [Nano Banana 2 Edit](https://doitong.com/developers/models/nano-banana-2/edit.md) (`nano-banana-2/edit`): from $0.06 / image
- [Nano Banana Pro Edit](https://doitong.com/developers/models/nano-banana-pro/edit.md) (`nano-banana-pro/edit`): from $0.15 / image
- [Qwen Image 3 Edit](https://doitong.com/developers/models/alibaba/qwen-image-3/edit.md) (`alibaba/qwen-image-3/edit`): from $0.04 / image
- [Seedream 5.0 Pro Edit](https://doitong.com/developers/models/bytedance/seedream/v5/pro/edit.md) (`bytedance/seedream/v5/pro/edit`): from $0.0675 / run of output image
- [Hunyuan Image · v3 · Instruct · Edit](https://doitong.com/developers/models/hunyuan-image/v3/instruct/edit.md) (`hunyuan-image/v3/instruct/edit`): $0.174 / image
- [Hunyuan World](https://doitong.com/developers/models/hunyuan_world.md) (`hunyuan_world`): $0.15 / image

## Links

- HTML page: https://doitong.com/developers/models/ip-adapter-face-id
- 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=ip-adapter-face-id
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/ip-adapter-face-id