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

> A14B text-to-video LoRA podporasa thaj opcionalne palal vakerdo playbackosa

Kerel video katar jek teksto `prompt` thaj aplikinel LoRA pharipen save ande `loras`. `reverse_video` del o klipo palpale. `num_frames` džal katar 17 dži 161, `resolution` si 480p, 580p vaj 720p, thaj proporcija si 16:9, 9:16 vaj 1:1. Film vaj rife interpolacija thaj duj lidžaripaski skali si dostupne.

- 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/rom/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 |  |  | Tekstune instrukcije save dromar videoqo generiripe. |
| `negative_prompt` |  | string | no | `` |  | Negačutno promptos čhivdo kana kerel jekh video. |
| `num_frames` |  | integer | no | `81` | 17 – 161 | Kadroengo gendo vaś o keripe, inkluzivno range 17–161. |
| `frames_per_second` |  | integer \| null | no | `16` | 4 – 60 | Kerde videosko kadroengo frekvenca, leľipen 4 dži 60; kana `adjust_fps_for_interpolation` hin false, ačhavel la frekvenca bi averipen. Kana interpolacija hin labardi thaj `adjust_fps_for_interpolation` hin true (lakro standardno), množin e frekvenca jekh plus interpolime kadroengo gendoha vaś paluno FPS. Sar primer, 16 FPS jekh interpolime kadroha keren 32 FPS. |
| `seed` |  | integer \| null | no |  |  | Seed savo kerel o generiripe reproducibilno. Te postavil pes pe None, randomno kidel pes seed. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `580p`, `720p` | Rezolucija e rezultatsko videoski: 480p, 580p vaj 720p. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `16:9`, `9:16`, `1:1` | Rezultato-videosko aspect ratio, vaj 16:9 vaj 9:16. |
| `num_inference_steps` |  | integer | no | `27` | 2 – 40 | Gin e sampling-krokingo, savo zamenel majdur procesiripe vaś majlačhi kvaliteta kana o gin barol. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Inputoski sigurnostno kontrola anglal procesovanie, savi šaj te čhinel ča authorized konto. Mangipena bi autorizacija hin sajek kontrolime; averčhane true lačarel kontrola. |
| `enable_output_safety_checker` |  | boolean | no | `false` |  | Kontrolinel sigurnostno kontrola e kerde videoski; true lačarel kontrola palal keripe. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | Mukhel jekh baro čhibako modelo te pheravel o prompto detalonca. O pheripe na paruverel promptosko arakhlo. |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | Akceleracijako nastavenie, 'regular' hin rekomendime. Baro akceleracija lel avri kvalita, hoj te skurčarel vaxtosko keripe. |
| `guidance_scale` |  | number | no | `3.5` | 1 – 10 | Zor e classifier-free guidance-ski, savo zamenel šajutni kvaliteta-losaripen vaś majpašuno promptosko sovipen kana e skala barol. |
| `guidance_scale_2` |  | number | no | `4` | 1 – 10 | Guidance scale savo kontrolinel sar blizko o modelo džal palal prompto andro leski dujto faza. |
| `shift` |  | number | no | `5` | 1 – 10 | Videosko shiftosko nastavenie, dovolen no range 1.0–10.0. |
| `interpolator_model` |  | enum (string) | no | `film` | `none`, `film`, `rife` | Kadroengo interpolacijako nastavenie: thov None te na kerel interpolacija, vaj specifikin o modelo savo la kerel. |
| `num_interpolated_frames` |  | integer | no | `1` | 0 – 4 | Thov 0–4 interpolime kadro andro sako khub maškar duj kerde kadro. |
| `adjust_fps_for_interpolation` |  | boolean | no | `true` |  | Kadroengo frekvencijako adjustosko toggle: false ačhavel deďi FPS, true množinel la jekhe plus interpolime kadroengo gendoha. Kadaleha 16 FPS keren 32 FPS kana interpolimen jekh kadro thaj adjustovanie hin aktivno. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Rezultatsko videoski kvalitetsko nastavenie, savo lel bareder fajla vaś feder vizualna kana barol. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Videosko lekharepesko modo; balanced del kompromiso maškar sigipen thaj kvalita. Thov small vaś ciklone fajla thaj naj-sloweder lekharipe; sigeder lekharipe anel rezultatura sigeder, numa kerel bareder fajla. |
| `loras` |  | array of object {path, weight_name, scale, transformer} | no | `[]` |  | LoRA-oske vajana leabarde andro inferenca. |
| `loras[].path` |  | string | yes |  |  | Lokacia e LoRA vazipnaski, specifikime sar URL vaj drom. |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA vajesko nav, trebuno numa kana jekh Hugging Face repositorio si le bareder katar 1 safetensors fajlo. Kada nav si vaparavel numa kana `path` identifikuil kadava repositorio. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Multiplikator dino e LoRA vazipnaske anglal mergipen ande o bazno modelo. |
| `loras[].transformer` |  | enum (string) | no | `high` | `high`, `low`, `both` | Transformerosko than vaś LoRA-osko vajan. Thov 'high' vaś high-noise transformer, 'low' vaś low-noise transformer, vaj 'both' vaś te pherel o vajan andro duj transformerura. |
| `reverse_video` |  | boolean | no | `false` |  | Videosko palem-avripen; true keren palem-avri e prehrava. |

## 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} | Video-fajlo kerdino generiripeça. |
| `prompt` | string | Tekstovo prompto labardo vaś videosko keripe. |
| `seed` | integer | Seed-valoro aplikovano ko generiripe. |

## 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