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

> A14B text-to-video b'appoġġ għal LoRA u playback invers fakultattiv

Joħloq vidjo minn `prompt` tat-test filwaqt li japplika piżijiet LoRA elenkati f'`loras`. `reverse_video` idoqq il-klipp lura. `num_frames` ivarja minn 17 sa 161, `resolution` huwa 480p, 580p jew 720p, u l-proporzjon tal-aspett huwa 16:9, 9:16 jew 1:1. Huma disponibbli l-interpolazzjoni Film jew rife u żewġ skali ta' gwida.

- 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/mt/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 |  |  | Istruzzjonijiet testwali li jiggwidaw il-ġenerazzjoni tal-vidjo. |
| `negative_prompt` |  | string | no | `` |  | Prompt negattiv applikat waqt il-ġenerazzjoni ta’ vidjo. |
| `num_frames` |  | integer | no | `81` | 17 – 161 | Għadd ta’ frejms għall-ġenerazzjoni, b’firxa inklużiva ta’ 17–161. |
| `frames_per_second` |  | integer \| null | no | `16` | 4 – 60 | Rata tal-frejms tal-vidjo ġġenerat, li taċċetta valuri minn 4 sa 60; l-issettjar ta’ `adjust_fps_for_interpolation` għal false iżomm din ir-rata kif inhi. Meta tintuża l-interpolazzjoni u `adjust_fps_for_interpolation` ikun true (il-valur prestabbilit tiegħu), immultiplika din ir-rata b’wieħed flimkien mal-għadd ta’ frejms interpolati biex tikseb l-FPS finali. Pereżempju, 16 FPS b’frejm interpolat wieħed isiru 32 FPS. |
| `seed` |  | integer \| null | no |  |  | Seed li jagħmel il-ġenerazzjoni riproduċibbli. L-issettjar tiegħu għal None jagħżel seed b’mod każwali. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `580p`, `720p` | Riżoluzzjoni tal-vidjo output: 480p, 580p jew 720p. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `16:9`, `9:16`, `1:1` | Proporzjon tal-aspett tal-vidjo tal-output, jew 16:9 jew 9:16. |
| `num_inference_steps` |  | integer | no | `27` | 2 – 40 | Għadd ta’ passi ta’ kampjunar; għadd ogħla jżid il-ħin tal-ipproċessar iżda jtejjeb il-kwalità. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Verifika tas-sigurtà tal-input qabel l-ipproċessar, li kontijiet awtorizzati biss jistgħu jitfu. Talbiet mingħajr awtorizzazzjoni dejjem jiġu vverifikati; inkella, true jattiva l-verifika. |
| `enable_output_safety_checker` |  | boolean | no | `false` |  | Jikkontrolla l-verifika tas-sigurtà tal-vidjo ġġenerat; true jattiva kontroll wara l-ġenerazzjoni. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | Ħalli mudell lingwistiku kbir jarrikkixxi l-prompt bid-dettalji. L-attivazzjoni ta’ din l-espansjoni ma tbiddilx it-tifsira tal-prompt. |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | Setting tal-aċċelerazzjoni, b’'regular' bħala l-għażla rakkomandata. Aċċelerazzjoni ogħla tissagrifika l-kwalità biex tqassar il-ħin tal-ġenerazzjoni. |
| `guidance_scale` |  | number | no | `3.5` | 1 – 10 | Qawwa tal-gwida mingħajr klassifikatur; skala ogħla tqarreb ir-riżultat lejn il-prompt iżda tista’ tnaqqas il-kwalità. |
| `guidance_scale_2` |  | number | no | `4` | 1 – 10 | Skala ta’ gwida li tikkontrolla kemm il-mudell isegwi l-prompt matul it-tieni stadju tiegħu. |
| `shift` |  | number | no | `5` | 1 – 10 | Setting tal-bidla tal-vidjo, b’firxa permessa ta’ 1.0–10.0. |
| `interpolator_model` |  | enum (string) | no | `film` | `none`, `film`, `rife` | Setting tal-interpolazzjoni tal-frejms: agħżel None biex taqbeż l-interpolazzjoni, jew speċifika l-mudell li jwettaqha. |
| `num_interpolated_frames` |  | integer | no | `1` | 0 – 4 | Daħħal 0–4 frejms interpolati f’kull vojt bejn żewġ frejms iġġenerati. |
| `adjust_fps_for_interpolation` |  | boolean | no | `true` |  | Toggle għall-aġġustament tar-rata tal-frejms: false iżomm l-FPS ipprovdut, filwaqt li true jiskalah b’wieħed flimkien mal-għadd ta’ frejms interpolati. Għalhekk, 16 FPS isiru 32 FPS meta jiġi interpolat frejm wieħed u l-aġġustament ikun attivat. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Setting tal-kwalità tal-vidjo output li jiskambja fajls akbar għal viżwali mtejba hekk kif jiżdied is-setting. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Modalità tal-kitba tal-vidjo, b’balanced toffri kompromess bejn il-veloċità u l-kwalità. Agħżel small għall-iżgħar fajls u l-iktar kitba bil-mod; kitba aktar mgħaġġla tirritorna r-riżultati qabel iżda toħloq fajls akbar. |
| `loras` |  | array of object {path, weight_name, scale, transformer} | no | `[]` |  | Piżijiet LoRA applikati waqt l-inferenza. |
| `loras[].path` |  | string | yes |  |  | Post tal-piżijiet LoRA, speċifikat bħala URL jew mogħdija. |
| `loras[].weight_name` |  | string \| null | no |  |  | Isem tal-piż tal-LoRA, meħtieġ biss meta repożitorju ta' Hugging Face jkun fih aktar minn 1 fajl safetensors. Dan l-isem jintuża biss meta `path` jidentifika repożitorju bħal dan. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Multiplikatur applikat għall-piż LoRA qabel ma jingħaqad mal-mudell bażi. |
| `loras[].transformer` |  | enum (string) | no | `high` | `high`, `low`, `both` | Destinazzjoni tat-transformer għall-piż LoRA. Agħżel 'high' għat-transformer ta’ noise għoli, 'low' għal dak ta’ noise baxx, jew 'both' biex tgħabbi l-piż fiż-żewġ transformers. |
| `reverse_video` |  | boolean | no | `false` |  | Toggle għar-rivers tal-vidjo; true ireġġa’ lura l-plejbek. |

## 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} | Fajl vidjo prodott mill-ġenerazzjoni. |
| `prompt` | string | Prompt testwali użat biex jiġi ġġenerat il-vidjo. |
| `seed` | integer | Valur ta’ seed applikat waqt il-ġenerazzjoni. |

## 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