# Wan-2.1 Text-to-Video with LoRAs API

> Wan 2.1 ٹیکسٹ ٹو ویڈیو جو حسبِ ضرورت LoRA وزن قبول کرتا ہے، ٹربو موڈ اور اختیاری ریورس پلے بیک کے ساتھ

متن کے `prompt` سے ویڈیو بناتا ہے اور `loras` میں درج حسب ضرورت LoRA لاگو کرتا ہے۔ ریزولوشن 480p، 580p یا 720p، اور نسبتِ ابعاد 16:9 یا 9:16 ہے۔ `num_frames` 81 سے 100 اور `frames_per_second` 5 سے 24 تک ہیں۔ `turbo_mode` ڈیفالٹ طور پر آن ہے، اور `reverse_video` نتیجہ الٹا چلاتا ہے۔

- Model id: `wan-t2v-lora`
- Made by: Alibaba
- Category: Text to Video
- Output: video
- Price: $0.75 / video (cheapest run $0.75 (750 API credits))
- Run: `POST https://api.doitong.ru/v1/run/wan-t2v-lora`
- Quote: `POST https://api.doitong.ru/v1/quote/wan-t2v-lora`
- Docs page: https://doitong.com/ur/developers/models/wan-t2v-lora

## Pricing

Price: **$0.75 / video**. Billed once per generated video.

The full example input below costs $0.75 = 750 API credits (1 video × 750 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.ru/v1/quote/wan-t2v-lora` 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 |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | ویڈیو تخلیق کی رہنمائی کرنے والی متنی ہدایات۔ |
| `negative_prompt` |  | string | no | `bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards` |  | ویڈیو تخلیق کرتے وقت لاگو ہونے والا منفی پرامپٹ۔ |
| `num_frames` |  | integer \| null | no | `81` | 81 – 100 | تخلیق کے لیے فریموں کی تعداد، 81 سے 100 تک شامل۔ |
| `frames_per_second` |  | integer \| null | no | `16` | 5 – 24 | تخلیق کردہ ویڈیو کا فریم ریٹ، فریم per سیکنڈ میں، 5 سے 24 تک۔ |
| `seed` |  | integer \| null | no |  |  | تخلیق کو قابلِ تکرار بنانے والا سیڈ۔ اسے None پر سیٹ کرنے سے سیڈ تصادفی طور پر منتخب ہوتا ہے۔ |
| `resolution` |  | enum (string) | no | `480p` | `480p`, `580p`, `720p` | آؤٹ پٹ ویڈیو ریزولوشن: 480p، 580p، یا 720p۔ |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `9:16`, `16:9` | آؤٹ پٹ ویڈیو کا aspect ratio، 16:9 یا 9:16۔ |
| `num_inference_steps` |  | integer | no | `30` | 2 – 40 | sampling steps کی تعداد، جس میں تعداد بڑھنے کے ساتھ زیادہ پروسیسنگ وقت کے بدلے بہتر معیار ملتا ہے۔ |
| `enable_safety_checker` |  | boolean | no | `false` |  | Safety-checking switch؛ off setting کے لیے account authorization ضروری ہے۔ authorization کے بغیر setting سے قطع نظر ہر درخواست check کی جاتی ہے؛ true checker فعال کرتا ہے۔ |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | متعین کرتا ہے کہ پرامپٹ کی توسیع فعال ہے یا نہیں۔ |
| `turbo_mode` |  | boolean | no | `true` |  | تیز تر ویڈیو تخلیق کا toggle۔ فعال کرنے سے بصری معیار میں نمایاں کمی کے بغیر تخلیق کا وقت کم ہوتا ہے۔ |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | Inference کے دوران لاگو کیے جانے والے LoRA weights۔ |
| `loras[].path` |  | string | yes |  |  | LoRA weights کا مقام، URL یا path کے طور پر۔ |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA weight name، صرف اس وقت ضروری ہے جب Hugging Face repository میں 1 سے زیادہ safetensors فائل ہو۔ یہ نام صرف اس وقت استعمال ہوتا ہے جب `path` ایسی repository کی شناخت کرے۔ |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | بنیادی ماڈل میں ضم کرنے سے پہلے LoRA weight پر لاگو ہونے والا multiplier۔ |
| `reverse_video` |  | boolean | no | `false` |  | ویڈیو الٹانے کا toggle؛ true پلے بیک کو الٹ دیتا ہے۔ |

## 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.ru/v1/run/wan-t2v-lora?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.ru/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.ru/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-t2v-lora?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.ru/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/wan-t2v-lora", 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.ru/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-t2v-lora",
      "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": "wan-t2v-lora",
  "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": 750,
    "usd": "0.75",
    "charged": 750,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "video": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_name": "0-output.mp4",
      "file_size": 1843200
    },
    "seed": 42
  },
  "outputs": [
    {
      "type": "video",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
      "content_type": "video/mp4",
      "file_size": 1843200,
      "width": 1280,
      "height": 720,
      "duration": 5,
      "stored": true
    }
  ],
  "error": null,
  "metrics": {
    "inference_seconds": 6.8
  },
  "urls": {
    "self": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | تخلیق سے تیار کردہ ویڈیو فائل۔ |
| `seed` | integer | تخلیق کے دوران لاگو ہونے والی سیڈ قدر۔ |

## Related models

- [Wan 3.0 Text to Video](https://doitong.com/developers/models/alibaba/wan-3.0/text-to-video.md) (`alibaba/wan-3.0/text-to-video`): from $0.05 / second
- [FLUX 3 Max Text to Video](https://doitong.com/developers/models/blackforestlabs/flux-3-max/text-to-video.md) (`blackforestlabs/flux-3-max/text-to-video`): from $0.0425 / second of output video
- [Grok Imagine Video 1.5 Text to Video](https://doitong.com/developers/models/xai/grok-imagine-video/v1.5/text-to-video.md) (`xai/grok-imagine-video/v1.5/text-to-video`): from $0.08 / second
- [Kling 3.0 Pro Text to Video](https://doitong.com/developers/models/kling-video/v3/pro/text-to-video.md) (`kling-video/v3/pro/text-to-video`): from $0.112 / second
- [LTX 2.5 Pro Text to Video](https://doitong.com/developers/models/lightricks/ltx-2.5/text-to-video/pro.md) (`lightricks/ltx-2.5/text-to-video/pro`): from $0.12 / second
- [MiniMax H3 Text to Video](https://doitong.com/developers/models/minimax/h3/text-to-video.md) (`minimax/h3/text-to-video`): from $0.05 / second
- [Seedance 2.0 Fast Text to Video](https://doitong.com/developers/models/bytedance/seedance-2.0/fast/text-to-video.md) (`bytedance/seedance-2.0/fast/text-to-video`): from $0.113 / second
- [Seedance 2.0 Text to Video](https://doitong.com/developers/models/bytedance/seedance-2.0/text-to-video.md) (`bytedance/seedance-2.0/text-to-video`): from $0.141 / second

## Links

- HTML page: https://doitong.com/developers/models/wan-t2v-lora
- 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-t2v-lora
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/wan-t2v-lora