# LTX-Video 13B 0.9.8 Distilled · Multiconditioning API

> Penjanaan 13B distilled 0.9.8 yang dikondisikan pada beberapa imej dan video serentak, serta prompt

Membina video daripada `prompt` yang diperlukan bersama senarai `images` dan `videos` pengkondisian, supaya beberapa rujukan boleh membentuk satu hasil. Output ialah 480p atau 720p dengan `aspect_ratio` auto, 9:16, 1:1 atau 16:9. `num_frames` berjulat daripada 9 hingga 1441, dan `loras` menambah pemberat tersuai.

- Model id: `ltxv-13b-098-distilled/multiconditioning`
- Made by: Lightricks
- Category: Video to Video
- Output: video
- Price per second of your output video: from $0.02 / second of output video (cheapest run $0.02 (20 API credits))
- Run: `POST https://api.doitong.ru/v1/run/ltxv-13b-098-distilled/multiconditioning`
- Quote: `POST https://api.doitong.ru/v1/quote/ltxv-13b-098-distilled/multiconditioning`
- Docs page: https://doitong.com/ms/developers/models/ltxv-13b-098-distilled/multiconditioning

## Pricing

Price: **from $0.02 / 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 $0.101 = 101 API credits (5.0417 s output video × $0.02).

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/ltxv-13b-098-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 |  |  | Arahan teks yang memandu penjanaan. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Arahan teks negatif untuk penjanaan. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | Pemberat LoRA yang digunakan semasa penjanaan. |
| `loras[].path` |  | string | yes |  |  | Lokasi pemberat LoRA, diberikan sebagai URL atau laluan. |
| `loras[].weight_name` |  | string \| null | no |  |  | Nama pemberat LoRA untuk memilih antara berbilang pemberat dalam repositori HuggingFace; hanya repositori yang mengandungi berbilang pemberat memerlukannya. Tetapan ini tidak memberi kesan melainkan `path` mengenal pasti repositori HuggingFace. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Pengganda yang menskalakan pemberat LoRA semasa dimuatkan. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | Resolusi untuk video output. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `9:16`, `1:1`, `16:9`, `auto` | Nisbah aspek video. |
| `seed` |  | integer \| null | no |  |  | Seed yang mengawal rawak semasa penjanaan. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | Jumlah kiraan bingkai untuk video. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Kiraan langkah inferens untuk laluan pertama. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Kiraan langkah inferens untuk laluan kedua. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | Kiraan awal langkah inferens yang dikecualikan semasa laluan kedua, mengalihkannya kepada kerja perincian halus dan bukannya perubahan berskala besar. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | Kadar bingkai untuk video. |
| `expand_prompt` |  | boolean | no | `false` |  | Pengembangan prompt melalui model bahasa. |
| `reverse_video` |  | boolean | no | `false` |  | Pembalikan susunan main balik video. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Suis pemeriksaan keselamatan yang tetapan matinya hanya dihormati untuk akaun yang diberi kuasa. Semua permintaan tanpa kebenaran itu menjalani pemeriksaan keselamatan. |
| `enable_detail_pass` |  | boolean | no | `false` |  | Laluan penambahbaikan kedua pilihan yang meningkatkan perincian video apabila didayakan. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | Kekuatan normalisasi tika adaptif (AdaIN), mengimbangi konsistensi warna pada tetapan lebih tinggi dengan variasi warna lebih besar pada tetapan lebih rendah. Ia beroperasi pada ketulan yang dijana kecuali yang pertama, menormalkan taburan warna sepanjang video untuk membantu mengurangkan peningkatan tepu atau kontras secara beransur-ansur. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | Tahap mampatan pemetaan tona, daripada tiada mampatan pada 0.0 hingga mampatan maksimum pada 1.0. Memampatkan julat dinamik video dengan cara ini meningkatkan kualiti visual. |
| `constant_rate_factor` |  | integer | no | `29` | 0 – 51 | Faktor kadar malar (CRF) yang digunakan untuk pemampatan input, yang mungkin meningkatkan kualiti gerakan dengan mendekatkan media yang dibekalkan kepada data latihan model. |
| `images` |  | array of object {image_url, start_frame_num, strength} | no | `[]` |  | Imej pengkondisian yang dibekalkan melalui URL. |
| `images[].image_url` |  | string (image URL) | yes |  |  | Imej pengkondisian yang dibekalkan melalui URL. |
| `images[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Kedudukan mula pengkondisian imej, dinyatakan menggunakan nombor bingkai yang boleh dibahagi dengan 8. |
| `images[].strength` |  | number | no | `1` | 0 – 1 | Keamatan pengkondisian: 0.0 melumpuhkan pengkondisian, manakala 1.0 menggunakannya sepenuhnya. |
| `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 yang dibekalkan untuk pengkondisian. |
| `videos[].video_url` |  | string (video URL) | yes |  |  | Video pengkondisian yang dibekalkan melalui URL. |
| `videos[].conditioning_type` |  | enum (string) \| null | no | `rgb` | `rgb`, `depth`, `pose`, `canny` | Kategori pengkondisian video yang digunakan untuk memilih aliran prapemprosesan apabila prapemprosesan didayakan, serta menggunakan pemberat LoRA dalam konteks dengan betul. |
| `videos[].preprocess` |  | boolean | no | `false` |  | Mendayakan prapemprosesan agar sepadan dengan jenis pengkondisian video. Untuk pengkondisian RGB, mendayakannya tidak membuat sebarang perubahan. |
| `videos[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Kedudukan mula pengkondisian dalam video, dinyatakan sebagai nombor bingkai yang boleh dibahagi dengan 8. |
| `videos[].strength` |  | number | no | `1` | 0 – 1 | Keamatan pengkondisian: 0.0 melumpuhkan pengkondisian, manakala 1.0 menggunakannya sepenuhnya. |
| `videos[].limit_num_frames` |  | boolean | no | `false` |  | Suis had bingkai video: apabila didayakan, `max_num_frames` menjadi had bingkai yang diambil daripada video. |
| `videos[].max_num_frames` |  | integer | no | `1441` | 1 – 1,441 | Had penggunaan bingkai video, dengan None memilih keseluruhan video dan bukannya kiraan bingkai terhad. |
| `videos[].resample_fps` |  | boolean | no | `false` |  | Suis penukaran kadar bingkai video: apabila didayakan, video diambil sampel semula pada kadar yang dinyatakan oleh `target_fps`. |
| `videos[].target_fps` |  | integer | no | `24` | 1 – 60 | Kadar bingkai pensampelan semula dalam FPS, hanya terpakai apabila `resample_fps` didayakan. |
| `videos[].reverse_video` |  | boolean | no | `false` |  | Pembalikan susunan video untuk tugasan yang memerlukan pengkondisian mengikuti bingkai secara terbalik. |

## 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/ltxv-13b-098-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/ltxv-13b-098-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/ltxv-13b-098-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": "ltxv-13b-098-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": "ltxv-13b-098-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": 101,
    "usd": "0.101",
    "charged": 101,
    "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} | Fail video yang dihasilkan oleh penjanaan. |
| `prompt` | string | Prompt yang digunakan untuk menjana output. |
| `seed` | integer | Nilai seed yang digunakan semasa penjanaan. |

## Related models

- [LTX-Video 13B 0.9.8 Distilled](https://doitong.com/developers/models/ltxv-13b-098-distilled.md) (`ltxv-13b-098-distilled`): from $0.02 / second of output video
- [LTX-Video 13B 0.9.8 Distilled · Image to Video](https://doitong.com/developers/models/ltxv-13b-098-distilled/image-to-video.md) (`ltxv-13b-098-distilled/image-to-video`): from $0.02 / second of output video
- [LTX-Video 13B 0.9.8 Distilled · Extend](https://doitong.com/developers/models/ltxv-13b-098-distilled/extend.md) (`ltxv-13b-098-distilled/extend`): from $0.02 / second of output 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/ltxv-13b-098-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=ltxv-13b-098-distilled/multiconditioning
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/ltxv-13b-098-distilled/multiconditioning