# LTX Video-0.9.7 13B Distilled · Multiconditioning API

> Video pārveide video ar attēlu un video nosacījumiem, LoRA atbalstu un divu posmu uzlabošanu ar izvēles detaļu posmu

Ģenerē video no obligāta `prompt`, izmantojot nosacījumu ievades laukos `images` un `videos`. Pielāgotas LoRA var nodot laukā `loras`. Izvade ir 480p vai 720p, `num_frames` — no 9 līdz 1441, bet `frame_rate` — no 1 līdz 60. Atsevišķi soļu skaiti pirmajam un otrajam posmam, izvēles detaļu posms un krāsu vadīklas, piemēram, `temporal_adain_factor`, ļauj precīzi pielāgot rezultātu.

- Model id: `ltx-video-13b-distilled/multiconditioning`
- Made by: Lightricks
- 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.ru/v1/run/ltx-video-13b-distilled/multiconditioning`
- Quote: `POST https://api.doitong.ru/v1/quote/ltx-video-13b-distilled/multiconditioning`
- Docs page: https://doitong.com/lv/developers/models/ltx-video-13b-distilled/multiconditioning

## 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.

The full example input below costs $0.202 = 202 API credits (5.0417 s output video × $0.04).

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/ltx-video-13b-distilled/multiconditioning` 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 |  |  | Teksta norādījumi, kas vada ģenerēšanu. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Negatīvie teksta norādījumi ģenerēšanai. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | Ģenerēšanas laikā izmantotie LoRA svari. |
| `loras[].path` |  | string | yes |  |  | LoRA svaru atrašanās vieta, norādīta kā URL vai ceļš. |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA svara nosaukums, lai atlasītu vienu no vairākiem svariem HuggingFace repozitorijā; nepieciešams tikai repozitorijiem ar vairākiem svariem. Šis iestatījums neietekmē rezultātu, ja `path` neidentificē HuggingFace repozitoriju. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Reizinātājs, kas mērogo LoRA svaru tā ielādes laikā. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | Izvades video izšķirtspēja. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `9:16`, `1:1`, `16:9`, `auto` | Video malu attiecība. |
| `seed` |  | integer \| null | no |  |  | Sākumvērtība, kas kontrolē nejaušību ģenerēšanas laikā. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | Kopējais video kadru skaits. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Secināšanas soļu skaits pirmajā posmā. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Secināšanas soļu skaits otrajā posmā. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | Otrajā posmā izlaižamo sākotnējo secināšanas soļu skaits, novirzot šo posmu uz smalku detaļu apstrādi, nevis plašām izmaiņām. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | Video kadru ātrums. |
| `expand_prompt` |  | boolean | no | `false` |  | Uzvednes paplašināšana ar valodas modeli. |
| `reverse_video` |  | boolean | no | `false` |  | Video atskaņošanas secības apvēršana. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Drošības pārbaudes slēdzis, kura izslēgtais stāvoklis tiek ņemts vērā tikai autorizētiem kontiem. Visiem pieprasījumiem bez šādas autorizācijas tiek veiktas drošības pārbaudes. |
| `enable_detail_pass` |  | boolean | no | `false` |  | Neobligāts otrais pilnveidošanas posms, kas pēc iespējošanas uzlabo video detalizāciju. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | Adaptīvās instanču normalizācijas (AdaIN) stiprums, kas līdzsvaro krāsu saskaņotību pie augstākām vērtībām ar lielāku krāsu variāciju pie zemākām vērtībām. Tā darbojas uz ģenerētajiem fragmentiem, izņemot pirmo, normalizējot krāsu sadalījumu visā video, lai palīdzētu novērst pakāpenisku piesātinājuma vai kontrasta pieaugumu. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | Toņu kartēšanas saspiešanas līmenis diapazonā no saspiešanas neesamības pie 0.0 līdz maksimālai saspiešanai pie 1.0. Šāda video dinamiskā diapazona saspiešana uzlabo vizuālo kvalitāti. |
| `constant_rate_factor` |  | integer | no | `29` | 0 – 51 | Ievades saspiešanai izmantotais nemainīgās kvalitātes koeficients (CRF), kas var uzlabot kustības kvalitāti, tuvinot iesniegto multividi modeļa apmācības datiem. |
| `images` |  | array of object {image_url, start_frame_num, strength} | no | `[]` |  | Nosacījumu attēli, kas norādīti ar URL. |
| `images[].image_url` |  | string (image URL) | yes |  |  | Nosacījuma attēls, kas norādīts ar URL. |
| `images[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Attēla nosacījuma sākuma pozīcija, kas norādīta kā kadra numurs, kurš dalās ar 8. |
| `images[].strength` |  | number | no | `1` | 0 – 1 | Nosacījumu ietekmes intensitāte: 0.0 atspējo nosacījumu ietekmi, savukārt 1.0 to piemēro pilnībā. |
| `videos` |  | array of object {video_url, conditioning_type, preprocess, start_frame_num, strength, limit_num_frames, max_num_frames, resample_fps, target_fps, reverse_video} | no | `[]` |  | Video, kas nodrošināti kā nosacījumi. |
| `videos[].video_url` |  | string (video URL) | yes |  |  | Nosacījumus nosakošs video, kas norādīts ar URL. |
| `videos[].conditioning_type` |  | enum (string) \| null | no | `rgb` | `rgb`, `depth`, `pose`, `canny` | Video nosacījumu kategorija, ko izmanto priekšapstrādes konveijera izvēlei, ja priekšapstrāde ir iespējota, un konteksta LoRA svaru pareizai piemērošanai. |
| `videos[].preprocess` |  | boolean | no | `false` |  | Iespējo priekšapstrādi atbilstoši video nosacījumu tipam. RGB nosacījumu gadījumā iespējošana neko nemaina. |
| `videos[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Nosacījumu piemērošanas sākuma pozīcija video, norādīta kā kadra numurs, kas dalās ar 8. |
| `videos[].strength` |  | number | no | `1` | 0 – 1 | Nosacījumu ietekmes intensitāte: 0.0 atspējo nosacījumu ietekmi, savukārt 1.0 to piemēro pilnībā. |
| `videos[].limit_num_frames` |  | boolean | no | `false` |  | Video kadru skaita ierobežošanas slēdzis: ja ieslēgts, `max_num_frames` nosaka maksimālo no video izmantoto kadru skaitu. |
| `videos[].max_num_frames` |  | integer | no | `1441` | 1 – 1,441 | Maksimālais izmantoto video kadru skaits; None atlasa visu video, nevis ierobežotu kadru skaitu. |
| `videos[].resample_fps` |  | boolean | no | `false` |  | Video kadru ātruma pārveidošanas slēdzis: ja ieslēgts, video tiek pārveidots atbilstoši `target_fps` norādītajam kadru ātrumam. |
| `videos[].target_fps` |  | integer | no | `24` | 1 – 60 | Pārveidošanas kadru ātrums FPS vienībās; piemērojams tikai tad, ja ir iespējots `resample_fps`. |
| `videos[].reverse_video` |  | boolean | no | `false` |  | Video secības apvēršana uzdevumiem, kuros nosacījumiem jāseko kadriem apgrieztā secībā. |

## 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/ltx-video-13b-distilled/multiconditioning?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/ltx-video-13b-distilled/multiconditioning?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/ltx-video-13b-distilled/multiconditioning", 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": "ltx-video-13b-distilled/multiconditioning",
      "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": "ltx-video-13b-distilled/multiconditioning",
  "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": 202,
    "usd": "0.202",
    "charged": 202,
    "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 vibrant, abstract composition featuring a person with outstretched arms, rendered in a kaleidoscope of colors against a deep, dark background. The figure is composed of intricate, swirling patterns reminiscent of a mosaic, with hues of orange, yellow, blue, and green that evoke the style of artists such as Wassily Kandinsky or Bridget Riley. The camera zooms into the face striking portrait of a man, reimagined through the lens of old-school video-game graphics. The subject's face is rendered in a kaleidoscope of colors, with bold blues and reds set against a vibrant yellow backdrop. His dark hair is pulled back, framing his profile in a dramatic pose.",
    "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} | Ģenerēšanas rezultātā iegūtais video fails. |
| `prompt` | string | Uzvedne, kas tika izmantota izvades ģenerēšanai. |
| `seed` | integer | Ģenerēšanas laikā lietotā sēklas vērtība. |

## Related models

- [LTX Video-0.9.7 13B Distilled](https://doitong.com/developers/models/ltx-video-13b-distilled.md) (`ltx-video-13b-distilled`): from $0.04 / second of output video
- [LTX Video-0.9.7 13B Distilled · Image to Video](https://doitong.com/developers/models/ltx-video-13b-distilled/image-to-video.md) (`ltx-video-13b-distilled/image-to-video`): from $0.04 / second of output video
- [LTX Video-0.9.7 13B Distilled · Extend](https://doitong.com/developers/models/ltx-video-13b-distilled/extend.md) (`ltx-video-13b-distilled/extend`): $0.04 / video
- [Ltx 2.3](https://doitong.com/developers/models/ltx-2.3/reframe.md) (`ltx-2.3/reframe`): from $0.10 / second of input video
- [LTX 2.3 22B](https://doitong.com/developers/models/ltx-2.3-22b/reference-video-to-video/lora.md) (`ltx-2.3-22b/reference-video-to-video/lora`): $0.00181 / megapixel of output video
- [Ltx 2.3 Quality · Clean Plate](https://doitong.com/developers/models/ltx-2.3-quality/clean-plate.md) (`ltx-2.3-quality/clean-plate`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Colorization](https://doitong.com/developers/models/ltx-2.3-quality/colorization.md) (`ltx-2.3-quality/colorization`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Cross Eyed](https://doitong.com/developers/models/ltx-2.3-quality/cross-eyed.md) (`ltx-2.3-quality/cross-eyed`): $0.00241 / megapixel of output video

## Links

- HTML page: https://doitong.com/developers/models/ltx-video-13b-distilled/multiconditioning
- 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=ltx-video-13b-distilled/multiconditioning
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/ltx-video-13b-distilled/multiconditioning