# Wan VACE 14B API

> Universāls VACE 14B video modelis: izvēlieties uzdevumu (dziļuma karte, poza, aizkrāsošana, paplašināšana, kadrējuma maiņa) ar daudzām neobligātām ievadēm

Universāls video ģenerators un redaktors, ko atlasa ar `task`: dziļuma karte, poza, aizkrāsošana, paplašināšana vai reframe. Ievades ietver avota `video_url`, maskas video vai attēlu, `ref_image_urls` un pirmā un pēdējā kadra URL. `num_frames` ir no 17 līdz 241, bet `resolution` — no 240p līdz 720p. Var atlasīt unipc, dpm++ un euler iztveršanas metodes.

- Model id: `wan-vace-14b`
- Made by: Alibaba
- Category: Video to Video
- Output: video
- Price per second of your output video: from $0.04 / second of output video (cheapest run $0.04 (40 API credits))
- Run: `POST https://api.doitong.com/v1/run/wan-vace-14b`
- Quote: `POST https://api.doitong.com/v1/quote/wan-vace-14b`
- Docs page: https://doitong.com/lv/developers/models/wan-vace-14b

## Pricing

Price: **from $0.04 / second of output video** (the lowest tier; the quote prices your exact input). Price per second of your output video: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

Price by tier:

- 240p: $0.04 / second of output video
- 360p: $0.04 / second of output video
- 480p: $0.04 / second of output video
- 580p: $0.06 / second of output video
- auto: $0.08 / second of output video
- 720p: $0.08 / second of output video

The full example input below costs $0.405 = 405 API credits (5.0625 s output video × $0.08 (auto)).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.com/v1/quote/wan-vace-14b` 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

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | Teksta norādes video ģenerēšanai. |
| `negative_prompt` |  | string | no | `letterboxing, borders, black bars, bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards` |  | Negatīvā uzvedne, kas tiek lietota video ģenerēšanas laikā. |
| `match_input_num_frames` |  | boolean | no | `false` |  | Ģenerētā video kadru skaita avots: false izmanto `num_frames`, savukārt true kopē ievades video kadru skaitu. |
| `num_frames` |  | integer | no | `81` | 17 – 241 | Izvades kadru skaits ar ietverošu diapazonu 81–241. |
| `match_input_frames_per_second` |  | boolean | no | `false` |  | Ģenerētā video kadru nomaiņas ātruma avots: false izmanto `frames_per_second`, savukārt true kopē ievades video kadru nomaiņas ātrumu. |
| `frames_per_second` |  | integer \| null | no | `16` | 5 – 30 | Ģenerētā video kadru nomaiņas ātrums, kas tiek lietots tikai tad, ja `match_input_frames_per_second` nav true; pieļaujamās vērtības ir 5–30 kadri sekundē. |
| `task` |  | enum (string) | no | `depth` | `depth`, `pose`, `inpainting`, `outpainting`, `reframe` | Uzdevuma veids, ko veic modelis. |
| `seed` |  | integer \| null | no |  |  | Sēkla, kas padara ģenerēšanu atkārtojamu. Iestatot None, sēkla tiek izvēlēta nejauši. |
| `resolution` |  | enum (string) | no | `auto` | `auto`, `240p`, `360p`, `480p`, `580p`, `720p` | Izvades video izšķirtspēja. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `auto`, `16:9`, `1:1`, `9:16` | Iegūtā video malu attiecība. |
| `num_inference_steps` |  | integer | no | `30` | 2 – 50 | Semplešanas soļu skaits; lielāks skaits nodrošina labāku kvalitāti, bet paildzina apstrādi. |
| `guidance_scale` |  | number | no | `5` | 1 – 10 | Uzvednes saskaņošanas stiprums, ko kontrolē ar klasifikatoram neatkarīgām norādēm; palielinot mērogu, ģenerētie attēli ciešāk atbilst tekstam. |
| `sampler` |  | enum (string) | no | `unipc` | `unipc`, `dpm++`, `euler` | Paraugu ņēmējs, kas tiek izmantots video ģenerēšanas laikā. |
| `shift` |  | number | no | `5` | 1 – 15 | Nobīdes iestatījums, kas tiek lietots video ģenerēšanas laikā. |
| `video_url` | `video` | string (video URL) \| null | no |  |  | Video atsauce modelim, kas, ja sniegta, ir URL uz avota failu. |
| `mask_video_url` |  | string (video URL) \| null | no |  |  | Maskas atsauce modelim, kas, ja sniegta, ir URL uz avota failu. |
| `mask_image_url` |  | string (image URL) \| null | no |  |  | Vadošā maska, kas sniegta ar faila URL un, ja pieejama, ir prioritāra pār maskas video URL. Modelis izmanto šo masku maskēta video izveides vadīšanai. |
| `ref_image_urls` |  | array of image URL | no |  |  | Neobligāti avota attēlu URL, kas nodrošina modelim izmantojamus atsauces attēlus. |
| `first_frame_url` | `image` | string \| null | no |  |  | Neobligāta pirmā kadra atsauce, kas nodrošināta ar URL un ko modelis izmanto video apstrādē. |
| `last_frame_url` | `end_image` | string \| null | no |  |  | Neobligāta pēdējā kadra atsauce, kas nodrošināta ar URL un ko modelis izmanto video apstrādē. |
| `enable_safety_checker` |  | boolean | no | `false` |  | Drošības pārbaudes slēdzis, kura izslēgšanai nepieciešama konta autorizācija. Bez šīs autorizācijas katrs pieprasījums tiek pārbaudīts neatkarīgi no iestatījuma; true iespējo pārbaudītāju. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | Kontrolē, vai ir iespējota uzvednes paplašināšana. |
| `preprocess` |  | boolean | no | `false` |  | Nosaka ievades video priekšapstrādi. |
| `acceleration` |  | enum (string) \| null | no | `regular` | `none`, `low`, `regular` | Secināšanas paātrināšanas iestatījums ar opciju 'none' vai 'regular': paātrināta apstrāde ievērojami palielina ātrumu, radot ļoti nelielas izmaiņas izvadē. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Iegūtā video kvalitātes līmenis. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Iegūtā video ierakstīšanas režīms. |
| `num_interpolated_frames` |  | integer | no | `0` | 0 – 5 | Interpolācijas reižu skaits: 0 atstāj sākotnējos kadrus bez interpolācijas; citas vērtības norāda, cik starpkadru jāģenerē starp tiem. |
| `temporal_downsample_factor` |  | integer | no | `0` | 0 – 5 | Video kadru izlaišanas koeficients, kas izteikts kā vesels skaitlis, ar automātisku iztveršanas palielināšanu pēc katra iztveršanas samazināšanas koeficienta. Izmantojiet 0, lai atspējotu laika iztveršanas samazināšanu. |
| `enable_auto_downsample` |  | boolean | no | `false` |  | Automātiska kadru skaita pielāgošana: iespējošana ļauj modelim apstrādāt piemērota ilguma video ar samazinātu laika iztveršanu, pēc tam ar interpolāciju atjaunojot sākotnējo kadru skaitu. |
| `auto_downsample_min_fps` |  | number | no | `15` | 1 – 60 | Minimālais kadru ātrums kadros sekundē, kas atļauts pēc iztveršanas samazināšanas. Mazkustīgiem materiāliem šīs vērtības samazināšana ļauj veikt lielāku iztveršanas samazināšanu un iegūt garākas secības; ļoti ātrai kustībai var būt nepieciešama lielāka vērtība, taču noklusējums ir piemērots lielākajai daļai video. Automātiskā iztveršanas samazināšana izmanto šo slieksni, lai atrastu mazāko koeficientu, kas saglabā detalizāciju. |
| `interpolator_model` |  | enum (string) | no | `film` | `rife`, `film` | Kadru interpolācijas modeļa izvēle: 'rife' vai 'film'. |
| `transparency_mode` |  | enum (string) | no | `content_aware` | `content_aware`, `white`, `black` | Sākuma un beigu kadru caurspīdīguma apstrāde, nosakot, kā tiek aizpildīti to caurspīdīgie reģioni. |
| `return_frames_zip` |  | boolean | no | `false` |  | Kontrolē, vai tiek atgriezts arī ZIP arhīvs ar katru ģenerēto kadru; aktivizēts, ja vērtība ir true. |

File parameters (`video`, `mask_video_url`, `mask_image_url`, `ref_image_urls`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## 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.com/v1/run/wan-vace-14b?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.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/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-vace-14b?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.com/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-vace-14b", 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.com/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-vace-14b",
      "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-vace-14b",
  "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": 405,
    "usd": "0.405",
    "charged": 405,
    "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,
      "width": 1024,
      "height": 768,
      "fps": 1.0,
      "duration": 5,
      "num_frames": 1
    },
    "prompt": "string",
    "seed": 42,
    "frames_zip": {
      "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
    }
  },
  "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.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size, width, height, fps, duration, num_frames} | Ģenerēšanas rezultātā iegūtais video fails. |
| `prompt` | string | Uzvedne, kas tika izmantota izvades ģenerēšanai. |
| `seed` | integer | Ģenerēšanas laikā lietotā sēklas vērtība. |
| `frames_zip` | object {url, content_type, file_name, file_size} \| null | ZIP arhīvs ar katru video kadru, kas tiek atgriezts pēc pieprasījuma. |

## Related models

- [Wan VACE 14B · Depth](https://doitong.com/developers/models/wan-vace-14b/depth.md) (`wan-vace-14b/depth`): from $0.04 / second of output video
- [Wan VACE 14B · Inpainting](https://doitong.com/developers/models/wan-vace-14b/inpainting.md) (`wan-vace-14b/inpainting`): from $0.04 / second of output video
- [Wan VACE 14B · Outpainting](https://doitong.com/developers/models/wan-vace-14b/outpainting.md) (`wan-vace-14b/outpainting`): from $0.04 / second of output video
- [Wan VACE 14B · Pose](https://doitong.com/developers/models/wan-vace-14b/pose.md) (`wan-vace-14b/pose`): from $0.04 / second of output video
- [Wan VACE 14B · Reframe](https://doitong.com/developers/models/wan-vace-14b/reframe.md) (`wan-vace-14b/reframe`): from $0.04 / second of output video
- [Happy Horse Video Edit](https://doitong.com/developers/models/alibaba/happy-horse/video-edit.md) (`alibaba/happy-horse/video-edit`): from $0.28 / second of input video
- [Wan 2.1 VACE Long Reframe](https://doitong.com/developers/models/wan-vace-apps/long-reframe.md) (`wan-vace-apps/long-reframe`): from $0.04 / 2 seconds of input video
- [Wan 2.2 VACE Fun A14B · Depth](https://doitong.com/developers/models/wan-22-vace-fun-a14b/depth.md) (`wan-22-vace-fun-a14b/depth`): from $0.05 / second of output video

## Links

- HTML page: https://doitong.com/developers/models/wan-vace-14b
- 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-vace-14b
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/wan-vace-14b