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

> Text-to-video A14B dengan dukungan LoRA dan pemutaran terbalik opsional

Membuat video dari `prompt` teks sambil menerapkan bobot LoRA yang tercantum dalam `loras`. `reverse_video` memutar klip secara mundur. `num_frames` berkisar dari 17 hingga 161, `resolution` berupa 480p, 580p, atau 720p, dan rasio aspek berupa 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/id/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 |  |  | Instruksi teks yang memandu pembuatan video. |
| `negative_prompt` |  | string | no | `` |  | Prompt negatif yang diterapkan saat membuat video. |
| `num_frames` |  | integer | no | `81` | 17 – 161 | Jumlah bingkai untuk pembuatan, dengan rentang inklusif 17–161. |
| `frames_per_second` |  | integer \| null | no | `16` | 4 – 60 | Laju bingkai video yang dihasilkan, menerima nilai dari 4 hingga 60; mengatur `adjust_fps_for_interpolation` ke false mempertahankan laju ini tanpa perubahan. Saat interpolasi digunakan dan `adjust_fps_for_interpolation` true (bawaannya), kalikan laju ini dengan satu ditambah jumlah bingkai yang diinterpolasi untuk memperoleh FPS akhir. Misalnya, 16 FPS dengan 1 bingkai yang diinterpolasi menjadi 32 FPS. |
| `seed` |  | integer \| null | no |  |  | Seed yang membuat pembuatan dapat direproduksi. Mengaturnya ke None akan memilih seed secara acak. |
| `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` | Rasio aspek video output, yaitu 16:9 atau 9:16. |
| `num_inference_steps` |  | integer | no | `27` | 2 – 40 | Jumlah langkah sampling, yang menukar waktu pemrosesan lebih lama dengan kualitas lebih baik seiring bertambahnya jumlah. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Penyaringan keamanan input sebelum pemrosesan, yang hanya dapat dinonaktifkan oleh akun berwenang. Permintaan tanpa otorisasi selalu disaring; jika tidak, true mengaktifkan penyaringan. |
| `enable_output_safety_checker` |  | boolean | no | `false` |  | Mengontrol penyaringan keamanan video yang dihasilkan; nilai true mengaktifkan pemeriksaan setelah pembuatan. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | Izinkan large language model memperkaya prompt dengan detail. Mengaktifkan ekspansi ini tidak mengubah makna prompt. |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | Pengaturan akselerasi, dengan 'regular' sebagai pilihan yang direkomendasikan. Akselerasi yang lebih tinggi mengorbankan kualitas untuk mempersingkat waktu pembuatan. |
| `guidance_scale` |  | number | no | `3.5` | 1 – 10 | Kekuatan panduan tanpa classifier, yang menukar kemungkinan penurunan kualitas dengan pencocokan prompt yang lebih dekat seiring meningkatnya skala. |
| `guidance_scale_2` |  | number | no | `4` | 1 – 10 | Skala panduan yang mengontrol seberapa dekat model mengikuti prompt selama tahap keduanya. |
| `shift` |  | number | no | `5` | 1 – 10 | Pengaturan pergeseran video, dengan rentang yang diizinkan 1.0–10.0. |
| `interpolator_model` |  | enum (string) | no | `film` | `none`, `film`, `rife` | Pengaturan interpolasi bingkai: pilih None untuk melewati interpolasi, atau tentukan model untuk melakukannya. |
| `num_interpolated_frames` |  | integer | no | `1` | 0 – 4 | Sisipkan 0–4 bingkai yang diinterpolasi ke setiap celah antara dua bingkai yang dihasilkan. |
| `adjust_fps_for_interpolation` |  | boolean | no | `true` |  | Sakelar penyesuaian laju bingkai: false mempertahankan FPS yang diberikan, sedangkan true mengalikannya dengan satu ditambah jumlah bingkai yang diinterpolasi. Jadi, 16 FPS menjadi 32 FPS saat 1 bingkai diinterpolasi dan penyesuaian diaktifkan. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Pengaturan kualitas video output yang menukar ukuran file lebih besar dengan visual yang lebih baik saat nilainya meningkat. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Mode penulisan video, dengan balanced sebagai kompromi antara kecepatan dan kualitas. Pilih small untuk file terkecil dan penulisan paling lambat; penulisan yang lebih cepat mengembalikan hasil lebih awal tetapi menghasilkan file yang lebih besar. |
| `loras` |  | array of object {path, weight_name, scale, transformer} | no | `[]` |  | Bobot LoRA yang diterapkan selama inferensi. |
| `loras[].path` |  | string | yes |  |  | Lokasi bobot LoRA, yang ditentukan sebagai URL atau path. |
| `loras[].weight_name` |  | string \| null | no |  |  | Nama bobot LoRA, hanya wajib jika repositori Hugging Face berisi lebih dari 1 file safetensors. Nama ini hanya digunakan saat `path` mengidentifikasi repositori tersebut. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Pengali yang diterapkan pada bobot LoRA sebelum digabungkan ke model dasar. |
| `loras[].transformer` |  | enum (string) | no | `high` | `high`, `low`, `both` | Tujuan transformer untuk bobot LoRA. Pilih 'high' untuk transformer noise tinggi, 'low' untuk transformer noise rendah, atau 'both' untuk memuat bobot ke kedua transformer. |
| `reverse_video` |  | boolean | no | `false` |  | Sakelar pembalikan video; true membalikkan pemutaran. |

## 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} | File video yang dihasilkan oleh pembuatan. |
| `prompt` | string | Prompt teks yang digunakan untuk menghasilkan video. |
| `seed` | integer | Nilai seed yang diterapkan selama pembuatan. |

## 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