# Wan-2.2 Text-to-Video A14B with LoRAs API

> Text-to-video A14B dengan sokongan LoRA dan main balik terbalik pilihan

Mencipta video daripada `prompt` teks sambil menggunakan pemberat LoRA yang disenaraikan dalam `loras`. `reverse_video` memainkan klip secara terbalik. `num_frames` berjulat daripada 17 hingga 161, `resolution` ialah 480p, 580p atau 720p, dan nisbah aspek ialah 16:9, 9:16 atau 1:1. Interpolasi film atau rife serta dua skala panduan tersedia.

- Model id: `wan/v2.2-a14b/text-to-video/lora`
- Made by: Alibaba
- Category: Text to Video
- Output: video
- Estimated price per run: from $0.507 / run (cheapest run $0.507 (507 API credits))
- Run: `POST https://api.doitong.ru/v1/run/wan/v2.2-a14b/text-to-video/lora`
- Quote: `POST https://api.doitong.ru/v1/quote/wan/v2.2-a14b/text-to-video/lora`
- Docs page: https://doitong.com/ms/developers/models/wan/v2.2-a14b/text-to-video/lora

## Pricing

Price: **from $0.507 / run** (the lowest tier; the quote prices your exact input). Estimated from the typical cost of one run. The quote is exactly what the run charges.

Price rules (the first rule that matches the input wins; a parameter you leave out counts as its default):

- When `num_frames` > 81 and `resolution` = 480p: base price × 2
- When `num_frames` > 81 and `resolution` = 580p: base price × 2
- When `num_frames` > 81 and `resolution` = 720p: base price × 2

The full example input below costs $0.507 = 507 API credits (1 request × 507 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/v2.2-a14b/text-to-video/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 |  |  | Arahan teks yang memandu penjanaan video. |
| `negative_prompt` |  | string | no | `` |  | Prompt negatif yang digunakan semasa menjana video. |
| `num_frames` |  | integer | no | `81` | 17 – 161 | Bilangan bingkai untuk penjanaan, dengan julat inklusif 17–161. |
| `frames_per_second` |  | integer \| null | no | `16` | 4 – 60 | Kadar bingkai video yang dijana, menerima nilai dari 4 hingga 60; menetapkan `adjust_fps_for_interpolation` kepada false mengekalkan kadar ini tanpa perubahan. Dengan interpolasi dan `adjust_fps_for_interpolation` true (lalainya), darabkan kadar ini dengan satu tambah bilangan bingkai interpolasi untuk mendapatkan FPS akhir. Contohnya, 16 FPS dengan 1 bingkai interpolasi menjadi 32 FPS. |
| `seed` |  | integer \| null | no |  |  | Seed yang menjadikan penjanaan boleh diulang. Menetapkannya kepada None memilih seed secara rawak. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `580p`, `720p` | Resolusi video output: 480p, 580p atau 720p. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `16:9`, `9:16`, `1:1` | Nisbah aspek video output, sama ada 16:9 atau 9:16. |
| `num_inference_steps` |  | integer | no | `27` | 2 – 40 | Bilangan langkah pensampelan, yang menukar masa pemprosesan lebih lama dengan kualiti lebih baik apabila bilangannya meningkat. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Pemeriksaan keselamatan input sebelum pemprosesan, yang hanya boleh dimatikan oleh akaun yang diberi kuasa. Permintaan tanpa kebenaran sentiasa diperiksa; jika tidak, true mendayakan pemeriksaan. |
| `enable_output_safety_checker` |  | boolean | no | `false` |  | Mengawal pemeriksaan keselamatan video yang dijana; menetapkan true mendayakan semakan selepas penjanaan. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | Benarkan model bahasa besar memperkaya prompt dengan butiran. Mendayakan pengembangan ini mengekalkan maksud prompt. |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | Tetapan pecutan, dengan 'regular' sebagai pilihan disyorkan. Pecutan lebih tinggi mengorbankan kualiti untuk memendekkan masa penjanaan. |
| `guidance_scale` |  | number | no | `3.5` | 1 – 10 | Kekuatan panduan tanpa pengelas, yang menukar kemungkinan kehilangan kualiti dengan padanan prompt yang lebih rapat apabila skala meningkat. |
| `guidance_scale_2` |  | number | no | `4` | 1 – 10 | Skala panduan yang mengawal sejauh mana model mengikuti prompt semasa peringkat keduanya. |
| `shift` |  | number | no | `5` | 1 – 10 | Tetapan anjakan video, dengan julat yang dibenarkan 1.0–10.0. |
| `interpolator_model` |  | enum (string) | no | `film` | `none`, `film`, `rife` | Tetapan interpolasi bingkai: pilih None untuk melangkau interpolasi, atau nyatakan model untuk melaksanakannya. |
| `num_interpolated_frames` |  | integer | no | `1` | 0 – 4 | Sisipkan 0–4 bingkai interpolasi ke dalam setiap ruang antara dua bingkai yang dijana. |
| `adjust_fps_for_interpolation` |  | boolean | no | `true` |  | Suis pelarasan kadar bingkai: false mengekalkan FPS yang dibekalkan, manakala true menskalakannya dengan satu tambah bilangan bingkai interpolasi. Oleh itu, 16 FPS menjadi 32 FPS apabila 1 bingkai diinterpolasi dan pelarasan didayakan. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Tetapan kualiti video output yang menukar saiz fail lebih besar untuk visual lebih baik apabila tetapan meningkat. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Mod penulisan video, dengan balanced menawarkan kompromi antara kelajuan dan kualiti. Pilih small untuk fail terkecil dan penulisan paling perlahan; penulisan lebih pantas mengembalikan hasil lebih awal tetapi menghasilkan fail lebih besar. |
| `loras` |  | array of object {path, weight_name, scale, transformer} | no | `[]` |  | Berat LoRA yang digunakan semasa inferens. |
| `loras[].path` |  | string | yes |  |  | Lokasi berat LoRA, yang dinyatakan sebagai URL atau laluan. |
| `loras[].weight_name` |  | string \| null | no |  |  | Nama berat LoRA, hanya diperlukan apabila repositori Hugging Face mengandungi lebih daripada 1 fail safetensors. Nama ini hanya digunakan apabila `path` mengenal pasti repositori sedemikian. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Pengganda yang digunakan pada berat LoRA sebelum digabungkan dengan model asas. |
| `loras[].transformer` |  | enum (string) | no | `high` | `high`, `low`, `both` | Destinasi transformer untuk berat LoRA. Pilih 'high' untuk transformer hingar tinggi, 'low' untuk transformer hingar rendah, atau 'both' untuk memuatkan berat ke dalam kedua-dua transformer. |
| `reverse_video` |  | boolean | no | `false` |  | Suis pembalikan video; true membalikkan main balik. |

## 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/v2.2-a14b/text-to-video/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/v2.2-a14b/text-to-video/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/v2.2-a14b/text-to-video/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/v2.2-a14b/text-to-video/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/v2.2-a14b/text-to-video/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": 507,
    "usd": "0.507",
    "charged": 507,
    "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
    },
    "prompt": "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere.",
    "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} | Fail video yang dihasilkan oleh penjanaan. |
| `prompt` | string | Prompt teks yang digunakan untuk menjana video. |
| `seed` | integer | Nilai seed yang digunakan semasa penjanaan. |

## Related models

- [Wan v2.2 A14B Text-to-Image A14B with LoRAs](https://doitong.com/developers/models/wan/v2.2-a14b/text-to-image/lora.md) (`wan/v2.2-a14b/text-to-image/lora`): $0.05 / image
- [Wan v2.2 A14B Image-to-Video A14B with LoRAs](https://doitong.com/developers/models/wan/v2.2-a14b/image-to-video/lora.md) (`wan/v2.2-a14b/image-to-video/lora`): from $1.006 / run
- [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

## Links

- HTML page: https://doitong.com/developers/models/wan/v2.2-a14b/text-to-video/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/v2.2-a14b/text-to-video/lora
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/wan/v2.2-a14b/text-to-video/lora