# Wan 2.2 14B Image Trainer API

> Treina uma LoRA Wan 2.2 14B com suas imagens para um elemento ou estilo, retornando arquivos LoRA de baixo e alto ruído

Ajusta uma LoRA para text-to-image do Wan 2.2 a partir de um conjunto de dados em `training_data_url`, associado a um `trigger_phrase` obrigatório. `steps` varia de 10 a 6000 e `learning_rate` de 1e-6 a 0.1. Detecção facial, recorte facial e máscaras podem ser alternados, e `is_style` desativa as opções específicas de rosto. As saídas incluem um arquivo de configuração e arquivos LoRA separados de baixo e alto ruído.

- Model id: `wan-22-image-trainer`
- Made by: Alibaba
- Category: Training
- Output: file
- Price per run: from $0.45 / run (cheapest run $0.45 (450 API credits))
- Run: `POST https://api.doitong.com/v1/run/wan-22-image-trainer`
- Quote: `POST https://api.doitong.com/v1/quote/wan-22-image-trainer`
- Docs page: https://doitong.com/pt-BR/developers/models/wan-22-image-trainer

## Pricing

Price: **from $0.45 / run** (the lowest tier; the quote prices your exact input). Price per run: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $4.50 = 4500 API credits (1 run × $4.50).

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/wan-22-image-trainer` 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 |
|---|---|---|---|---|---|---|
| `training_data_url` |  | string | yes |  |  | Local dos dados de treinamento como URL. |
| `trigger_phrase` |  | string | yes |  |  | Frase usada para acionar o modelo. |
| `include_synthetic_captions` |  | boolean | no | `false` |  | Controla a inclusão de legendas sintéticas. |
| `use_face_detection` |  | boolean | no | `true` |  | Ativação do redimensionamento centralizado no rosto para imagens de treinamento: habilitar a detecção facial faz com que o centro do rosto detectado se torne o centro da imagem durante o redimensionamento. |
| `use_face_cropping` |  | boolean | no | `false` |  | Ativação do recorte facial para imagens de treinamento, adicionando um recorte ao redor do rosto antes da etapa de redimensionamento quando habilitada. |
| `use_masks` |  | boolean | no | `true` |  | Controla o uso de máscaras nos dados de treinamento. |
| `steps` |  | integer | no | `1000` | 10 – 6,000 | Quantidade total de etapas de treinamento. |
| `learning_rate` |  | number | no | `0.0007` | 0.000001 – 0.1 | Taxa de aprendizado durante o treinamento. |
| `is_style` |  | boolean | no | `false` |  | Designação de dados de estilo para treinamento; defini-la como true desativa recursos específicos de rosto, incluindo mascaramento e detecção facial. |

## Example input

Required parameters only:

```json
{
  "training_data_url": "https://example.com/your-file.zip",
  "trigger_phrase": "example"
}
```

A fuller example:

```json
{
  "training_data_url": "https://example.com/your-file.zip",
  "trigger_phrase": "example"
}
```

## 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/wan-22-image-trainer?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "training_data_url": "https://example.com/your-file.zip",
    "trigger_phrase": "example"
  }'

# 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/wan-22-image-trainer?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "training_data_url": "https://example.com/your-file.zip",
    "trigger_phrase": "example"
  }),
});
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 = {
    "training_data_url": "https://example.com/your-file.zip",
    "trigger_phrase": "example",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/wan-22-image-trainer", 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": "wan-22-image-trainer",
      "input": {
        "training_data_url": "https://example.com/your-file.zip",
        "trigger_phrase": "example"
      },
      "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": "wan-22-image-trainer",
  "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": 4500,
    "usd": "4.50",
    "charged": 4500,
    "refunded": 0
  },
  "input": {
    "training_data_url": "https://example.com/your-file.zip",
    "trigger_phrase": "example"
  },
  "output": {
    "diffusers_lora_file": {
      "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
    },
    "high_noise_lora": {
      "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
    },
    "config_file": {
      "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
    }
  },
  "outputs": [
    {
      "type": "file",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "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 |
|---|---|---|
| `diffusers_lora_file` | object {url, content_type, file_name, file_size} | Arquivo LoRA para baixo ruído. |
| `high_noise_lora` | object {url, content_type, file_name, file_size} | Arquivo LoRA para alto ruído. |
| `config_file` | object {url, content_type, file_name, file_size} | Arquivo de configuração compatível com endpoints de inferência após o treinamento. |

## Related models

- [Qwen Image 2512 Trainer](https://doitong.com/developers/models/qwen-image-2512-trainer.md) (`qwen-image-2512-trainer`): from $0.75 / run
- [Qwen Image Edit 2509 Trainer](https://doitong.com/developers/models/qwen-image-edit-2509-trainer.md) (`qwen-image-edit-2509-trainer`): from $0.40 / run
- [Wan-2.1 LoRA Trainer · I2V 720p](https://doitong.com/developers/models/wan-trainer/i2v-720p.md) (`wan-trainer/i2v-720p`): $0.005 / step
- [Wan-2.1 LoRA Trainer · T2V](https://doitong.com/developers/models/wan-trainer/t2v.md) (`wan-trainer/t2v`): $0.005 / step
- [Wan-2.1 LoRA Trainer · T2V 14b](https://doitong.com/developers/models/wan-trainer/t2v-14b.md) (`wan-trainer/t2v-14b`): $0.005 / step
- [Wan-2.2 LoRA Trainer · I2V A14b](https://doitong.com/developers/models/wan-22-trainer/i2v-a14b.md) (`wan-22-trainer/i2v-a14b`): $0.005 / step
- [Wan-2.2 LoRA Trainer · T2V A14b](https://doitong.com/developers/models/wan-22-trainer/t2v-a14b.md) (`wan-22-trainer/t2v-a14b`): $0.004 / step
- [Z Image Trainer](https://doitong.com/developers/models/z-image-trainer.md) (`z-image-trainer`): $0.00226 / step

## Links

- HTML page: https://doitong.com/developers/models/wan-22-image-trainer
- 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=wan-22-image-trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/wan-22-image-trainer