# Wan-2.2 LoRA Trainer · T2V A14b API

> آموزش LoRA سفارشی از فایل zip تصاویر یا ویدئوها برای استفاده با مدل تبدیل متن به ویدئوی Wan 2.2 A14B

یک LoRA را روی آرشیو zip خودتان که به‌صورت `training_data_url` ارسال می‌شود آموزش می‌دهد تا تولید متن به ویدئو سفارشی شود. آرشیو می‌تواند شامل تصاویر و ویدئوها به‌همراه فایل‌های متنی زیرنویس اختیاری باشد و حداقل 10 مورد پیشنهاد می‌شود. `number_of_steps` (از 100 تا 20000، پیش‌فرض 400)، `learning_rate` و `trigger_phrase` را تنظیم کنید. خروجی یک فایل وزن‌های LoRA و یک فایل پیکربندی است.

- Model id: `wan-22-trainer/t2v-a14b`
- Made by: Alibaba
- Category: Training
- Output: file
- Price: $0.004 / step (cheapest run $1.60 (1600 API credits))
- Run: `POST https://api.doitong.com/v1/run/wan-22-trainer/t2v-a14b`
- Quote: `POST https://api.doitong.com/v1/quote/wan-22-trainer/t2v-a14b`
- Docs page: https://doitong.com/fa/developers/models/wan-22-trainer/t2v-a14b

## Pricing

Price: **$0.004 / step**. Billed per training step (`number_of_steps`).

The full example input below costs $1.60 = 1600 API credits (400 steps × 4 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/wan-22-trainer/t2v-a14b` 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 |  |  | URL بایگانی ZIP آموزشی برای تصاویر دارای سبک یکدست، با امکان افزودن فایل‌های متنی کپشن که نامشان با فایل تصویر یا ویدیوی متناظر یکسان است. هدف، گنجاندن حداقل 10 تصویر، ویدیو یا ترکیبی از هر دو است؛ مجموعه‌های بزرگ‌تر ترجیح داده می‌شوند. |
| `number_of_steps` |  | integer | no | `400` | 100 – 20,000 | تعداد کل گام‌های آموزش. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | تنظیم نرخ یادگیری: افزایش نرخ می‌تواند آموزش را سریع‌تر کند، اما خطر بیش‌برازش را نیز افزایش می‌دهد. |
| `trigger_phrase` |  | string | no | `` |  | عبارتی که مدل را به تولید تصویر وادار می‌کند. |
| `auto_scale_input` |  | boolean | no | `false` |  | تعیین می‌کند آیا ویدیوی ورودی به‌طور خودکار مقیاس‌گذاری شود یا نه: مقدار true طول آن را به 81 فریم و نرخ فریم آن را به 16 fps تنظیم می‌کند. |

## Example input

Required parameters only:

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

A fuller example:

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

## 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-trainer/t2v-a14b?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "training_data_url": "https://example.com/your-file.zip"
  }'

# 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-trainer/t2v-a14b?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "training_data_url": "https://example.com/your-file.zip"
  }),
});
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",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/wan-22-trainer/t2v-a14b", 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-trainer/t2v-a14b",
      "input": {
        "training_data_url": "https://example.com/your-file.zip"
      },
      "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-trainer/t2v-a14b",
  "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": 1600,
    "usd": "1.60",
    "charged": 1600,
    "refunded": 0
  },
  "input": {
    "training_data_url": "https://example.com/your-file.zip"
  },
  "output": {
    "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
    },
    "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 |
|---|---|---|
| `lora_file` | object {url, content_type, file_name, file_size} | URL اشاره‌کننده به وزن‌های LoRA تولیدشده از آموزش. |
| `config_file` | object {url, content_type, file_name, file_size} | پیکربندی برای مقداردهی اولیهٔ نقطه‌های پایانی استنتاج. |

## Related models

- [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
- [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 2.2 14B Image Trainer](https://doitong.com/developers/models/wan-22-image-trainer.md) (`wan-22-image-trainer`): from $0.45 / 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
- [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-trainer/t2v-a14b
- 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-trainer/t2v-a14b
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/wan-22-trainer/t2v-a14b