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

> A14B za pretvorbo besedila v video s podporo za LoRA in neobveznim vzvratnim predvajanjem

Ustvari videoposnetek iz besedilnega `prompt` in uporabi uteži LoRA, navedene v `loras`. `reverse_video` predvaja posnetek vzvratno. `num_frames` sega od 17 do 161, `resolution` je 480p, 580p ali 720p, razmerje stranic pa 16:9, 9:16 ali 1:1. Na voljo sta interpolacija film ali rife ter dve lestvici vodenja.

- 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/sl/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 |  |  | Besedilna navodila, ki usmerjajo generiranje videa. |
| `negative_prompt` |  | string | no | `` |  | Negativni poziv, uporabljen pri generiranju videa. |
| `num_frames` |  | integer | no | `81` | 17 – 161 | Število sličic za generiranje z vključujočim razponom 17–161. |
| `frames_per_second` |  | integer \| null | no | `16` | 4 – 60 | Hitrost sličic ustvarjenega videa, ki sprejema vrednosti od 4 do 60; nastavitev `adjust_fps_for_interpolation` na false ohrani to hitrost nespremenjeno. Pri uporabljeni interpolaciji in vrednosti `adjust_fps_for_interpolation` true (privzeto) to hitrost pomnožite z ena plus številom interpoliranih sličic, da dobite končni FPS. Na primer, 16 FPS z 1 interpolirano sličico postane 32 FPS. |
| `seed` |  | integer \| null | no |  |  | Seme, ki omogoča ponovljivo generiranje. Če ga nastavite na None, se seme izbere naključno. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `580p`, `720p` | Ločljivost izhodnega videa: 480p, 580p ali 720p. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `16:9`, `9:16`, `1:1` | Razmerje stranic izhodnega videa, bodisi 16:9 bodisi 9:16. |
| `num_inference_steps` |  | integer | no | `27` | 2 – 40 | Število korakov vzorčenja; večje število pomeni daljšo obdelavo in boljšo kakovost. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Varnostno preverjanje vhoda pred obdelavo, ki ga lahko izklopijo le pooblaščeni računi. Zahteve brez pooblastila se vedno preverijo; sicer true omogoči preverjanje. |
| `enable_output_safety_checker` |  | boolean | no | `false` |  | Določa varnostno preverjanje ustvarjenega videa; nastavitev true omogoči preverjanje po generiranju. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | Velikemu jezikovnemu modelu dovoli obogatitev poziva s podrobnostmi. Ta razširitev ne spremeni pomena poziva. |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | Nastavitev pospeševanja, pri kateri je priporočena izbira 'regular'. Večje pospeševanje zmanjša kakovost, da skrajša čas generiranja. |
| `guidance_scale` |  | number | no | `3.5` | 1 – 10 | Jakost Classifier-free guidance, pri kateri večje merilo pomeni možno izgubo kakovosti v zameno za natančnejše ujemanje s pozivom. |
| `guidance_scale_2` |  | number | no | `4` | 1 – 10 | Lestvica vodenja, ki določa, kako natančno model v drugi fazi sledi pozivu. |
| `shift` |  | number | no | `5` | 1 – 10 | Nastavitev zamika videa z dovoljenim razponom 1.0–10.0. |
| `interpolator_model` |  | enum (string) | no | `film` | `none`, `film`, `rife` | Nastavitev interpolacije sličic: izberite None za preskok interpolacije ali določite model, ki jo bo izvedel. |
| `num_interpolated_frames` |  | integer | no | `1` | 0 – 4 | V vsako vrzel med dvema ustvarjenima sličicama vstavite 0–4 interpoliranih sličic. |
| `adjust_fps_for_interpolation` |  | boolean | no | `true` |  | Preklop za prilagoditev hitrosti sličic: false ohrani podani FPS, true pa ga pomnoži z ena plus številom interpoliranih sličic. Tako 16 FPS postane 32 FPS, ko je interpolirana 1 sličica in je prilagoditev omogočena. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Nastavitev kakovosti izhodnega videa, pri kateri višja vrednost zamenja večje datoteke za boljši videz. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Način zapisovanja videa, pri katerem balanced ponuja kompromis med hitrostjo in kakovostjo. Izberite small za najmanjše datoteke in najpočasnejše zapisovanje; hitrejše zapisovanje vrne rezultate prej, vendar ustvari večje datoteke. |
| `loras` |  | array of object {path, weight_name, scale, transformer} | no | `[]` |  | Uteži LoRA, uporabljene med sklepanjem. |
| `loras[].path` |  | string | yes |  |  | Lokacija uteži LoRA, navedena kot URL ali pot. |
| `loras[].weight_name` |  | string \| null | no |  |  | Ime uteži LoRA, zahtevano samo, če repozitorij Hugging Face vsebuje več kot 1 datoteko safetensors. To ime se uporabi samo, ko `path` določa tak repozitorij. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Množilnik, uporabljen za utež LoRA, preden se združi z osnovnim modelom. |
| `loras[].transformer` |  | enum (string) | no | `high` | `high`, `low`, `both` | Cilj transformatorja za utež LoRA. Izberite 'high' za transformator z visokim šumom, 'low' za transformator z nizkim šumom ali 'both' za nalaganje uteži v oba transformatorja. |
| `reverse_video` |  | boolean | no | `false` |  | Preklop za obračanje videa; true obrne predvajanje. |

## 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 datoteka, ustvarjena z generiranjem. |
| `prompt` | string | Besedilni poziv, uporabljen za generiranje videa. |
| `seed` | integer | Vrednost semena, uporabljena pri generiranju. |

## 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