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

> تحويل النص إلى فيديو باستخدام Wan 2.1 مع أوزان LoRA مخصصة ووضع turbo والعكس الاختياري

ينشئ فيديو من `prompt` نصي ويطبق LoRA مخصصة مدرجة في `loras`. تكون الدقة 480p أو 580p أو 720p، ونسبة الأبعاد 16:9 أو 9:16. تتراوح `num_frames` بين 81 و100 و`frames_per_second` بين 5 و24. يكون `turbo_mode` مفعّلًا افتراضيًا، ويشغّل `reverse_video` النتيجة بالعكس.

- 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/ar/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 |  |  | تعليمات نصية لتوجيه توليد الفيديو. |
| `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` |  | الموجّه السلبي المطبق عند توليد فيديو. |
| `num_frames` |  | integer \| null | no | `81` | 81 – 100 | عدد الإطارات المستخدمة في التوليد، من 81 إلى 100 شاملًا. |
| `frames_per_second` |  | integer \| null | no | `16` | 5 – 24 | معدل إطارات الفيديو المُولَّد بالإطارات في الثانية، من 5 إلى 24. |
| `seed` |  | integer \| null | no |  |  | بذرة تجعل التوليد قابلًا لإعادة الإنتاج. يؤدي ضبطها على None إلى اختيار بذرة عشوائيًا. |
| `resolution` |  | enum (string) | no | `480p` | `480p`, `580p`, `720p` | دقة الفيديو الناتج: 480p أو 580p أو 720p. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `9:16`, `16:9` | نسبة عرض فيديو المخرج إلى ارتفاعه، إما 16:9 أو 9:16. |
| `num_inference_steps` |  | integer | no | `30` | 2 – 40 | عدد خطوات أخذ العينات، حيث تُبادَل مدة المعالجة الأطول بجودة أفضل مع زيادة العدد. |
| `enable_safety_checker` |  | boolean | no | `false` |  | مفتاح فحص الأمان الذي يتطلب إعداد إيقافه تفويض الحساب. من دون هذا التفويض، يُفحص كل طلب بغض النظر عن الإعداد؛ وتفعّل true أداة الفحص. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | يتحكم في تفعيل توسيع الموجّه. |
| `turbo_mode` |  | boolean | no | `true` |  | مفتاح التوليد الأسرع للفيديو. يؤدي تفعيله إلى تقليل وقت التوليد دون فقدان ملحوظ في الجودة المرئية. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | أوزان LoRA المطبقة أثناء الاستدلال. |
| `loras[].path` |  | string | yes |  |  | موقع أوزان LoRA، محدد كرابط أو مسار. |
| `loras[].weight_name` |  | string \| null | no |  |  | اسم وزن LoRA، مطلوب فقط عندما يحتوي مستودع Hugging Face على أكثر من ملف safetensors واحد. يُستخدم هذا الاسم فقط عندما يحدد `path` مستودعًا من هذا النوع. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | المضاعف المطبق على وزن LoRA قبل دمجه في النموذج الأساسي. |
| `reverse_video` |  | boolean | no | `false` |  | مفتاح عكس الفيديو؛ تؤدي القيمة true إلى عكس التشغيل. |

## 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} | ملف الفيديو الناتج عن التوليد. |
| `seed` | integer | قيمة البذرة المطبّقة أثناء التوليد. |

## 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
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- [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