# Wan-2.1 LoRA Trainer · T2V API

> چھوٹے Wan 2.1 ٹیکسٹ سے ویڈیو 1.3B ماڈل کے لیے LoRA ٹرینر، تصاویر یا ویڈیوز کی zip سے

`training_data_url` پر موجود zip آرکائیو سے Wan 2.1 ٹیکسٹ سے ویڈیو 1.3B کے لیے LoRA ٹرین کرتا ہے، جس میں ٹیکسٹ فائلوں کے طور پر کیپشن شامل ہوسکتے ہیں۔ `number_of_steps` کو 100 سے 20000 کے درمیان مقرر کریں اور `learning_rate` کو ایڈجسٹ کریں۔ `trigger_phrase` اختیاری ہے، جبکہ `auto_scale_input` ویڈیو کو 16fps پر 81 فریموں میں تبدیل کرتا ہے۔ LoRA وزن اور config فائل واپس کرتا ہے۔

- Model id: `wan-trainer/t2v`
- Made by: Alibaba
- Category: Training
- Output: file
- Price: $0.005 / step (cheapest run $2.00 (2000 API credits))
- Run: `POST https://api.doitong.com/v1/run/wan-trainer/t2v`
- Quote: `POST https://api.doitong.com/v1/quote/wan-trainer/t2v`
- Docs page: https://doitong.com/ur/developers/models/wan-trainer/t2v

## Pricing

Price: **$0.005 / step**. Billed per training step (`number_of_steps`).

The full example input below costs $2.00 = 2000 API credits (400 steps × 5 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-trainer/t2v` 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 |  |  | مستقل انداز والی تصاویر کے لیے تربیتی ZIP archive کا URL، جس میں اختیاری caption متنی فائلیں شامل ہو سکتی ہیں جو متعلقہ تصویر یا ویڈیو فائلوں کے نام سے مطابقت رکھتی ہوں۔ کم از کم 10 تصاویر، ویڈیوز، یا دونوں کا مجموعہ شامل کرنے کا ہدف رکھیں؛ بڑے مجموعے بہتر ہیں۔ |
| `number_of_steps` |  | integer | no | `400` | 100 – 20,000 | تربیت کے لیے steps کی کل تعداد۔ |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Learning-rate setting: شرح بڑھانے سے تربیت ممکنہ طور پر تیز ہوتی ہے، مگر overfitting کا خطرہ بڑھتا ہے۔ |
| `trigger_phrase` |  | string | no | `` |  | وہ فقرہ جو ماڈل کو تصویر تخلیق کرنے کا پرامپٹ دیتا ہے۔ |
| `auto_scale_input` |  | boolean | no | `false` |  | آیا ان پٹ ویڈیو کا سکیل خودکار طور پر تبدیل کرنا ہے: true اس کی لمبائی 81 فریم اور فریم ریٹ 16 fps پر سیٹ کرتا ہے۔ |

## 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-trainer/t2v?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-trainer/t2v?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-trainer/t2v", 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-trainer/t2v",
      "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-trainer/t2v",
  "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": 2000,
    "usd": "2.00",
    "charged": 2000,
    "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} | تربیت سے تیار کردہ LoRA weights کی طرف اشارہ کرنے والا URL۔ |
| `config_file` | object {url, content_type, file_name, file_size} | inference اینڈ پوائنٹس کو شروع کرنے کی configuration۔ |

## Related models

- [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 14b](https://doitong.com/developers/models/wan-trainer/t2v-14b.md) (`wan-trainer/t2v-14b`): $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-2.2 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-trainer/t2v
- 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-trainer/t2v
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/wan-trainer/t2v