# LTX-Video 13B 0.9.8 Distilled · Multiconditioning API

> Generasi 13B distilled 0.9.8 yang dikondisikan pada beberapa gambar dan video sekaligus, serta prompt

Membangun video dari `prompt` yang wajib diisi bersama daftar `images` dan `videos` sebagai kondisi, sehingga beberapa referensi dapat membentuk satu hasil. Output berukuran 480p atau 720p dengan `aspect_ratio` auto, 9:16, 1:1, atau 16:9. `num_frames` berkisar dari 9 hingga 1441, dan `loras` menambahkan bobot kustom.

- 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/id/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 |  |  | Instruksi teks yang memandu pembuatan. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Instruksi teks negatif untuk pembuatan. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | Bobot LoRA yang diterapkan selama pembuatan. |
| `loras[].path` |  | string | yes |  |  | Lokasi bobot LoRA, diberikan sebagai URL atau path. |
| `loras[].weight_name` |  | string \| null | no |  |  | Nama bobot LoRA untuk memilih di antara beberapa bobot dalam repositori HuggingFace; hanya repositori yang berisi beberapa bobot yang memerlukannya. Pengaturan ini tidak berpengaruh kecuali `path` mengidentifikasi repositori HuggingFace. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Pengali yang menskalakan bobot LoRA saat dimuat. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | Resolusi video output. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `9:16`, `1:1`, `16:9`, `auto` | Rasio aspek video. |
| `seed` |  | integer \| null | no |  |  | Seed yang mengontrol keacakan selama pembuatan. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | Jumlah total bingkai untuk video. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Jumlah langkah inferensi untuk tahap pertama. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Jumlah langkah inferensi untuk tahap kedua. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | Jumlah langkah inferensi awal yang dilewati selama tahap kedua, sehingga tahap tersebut lebih berfokus pada pekerjaan detail halus daripada perubahan berskala besar. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | Laju bingkai untuk video. |
| `expand_prompt` |  | boolean | no | `false` |  | Perluasan prompt melalui model bahasa. |
| `reverse_video` |  | boolean | no | `false` |  | Pembalikan urutan pemutaran video. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Sakelar pemeriksaan keamanan yang pengaturan nonaktifnya hanya dihormati untuk akun berwenang. Semua permintaan tanpa otorisasi tersebut menjalani pemeriksaan keamanan. |
| `enable_detail_pass` |  | boolean | no | `false` |  | Tahap penyempurnaan kedua secara opsional yang meningkatkan detail video saat diaktifkan. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | Kekuatan normalisasi instance adaptif (AdaIN), yang menyeimbangkan konsistensi warna pada pengaturan lebih tinggi dengan variasi warna yang lebih besar pada pengaturan lebih rendah. Proses ini diterapkan pada chunk yang dibuat kecuali chunk pertama, dengan menormalkan distribusi warna di seluruh video untuk membantu mengatasi peningkatan saturasi atau kontras secara bertahap. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | Tingkat kompresi tone mapping, mulai dari tanpa kompresi pada 0.0 hingga kompresi maksimum pada 1.0. Mengompresi rentang dinamis video dengan cara ini meningkatkan kualitas visual. |
| `constant_rate_factor` |  | integer | no | `29` | 0 – 51 | Constant rate factor (CRF) yang digunakan untuk kompresi input, yang dapat meningkatkan kualitas gerakan dengan membuat media yang diberikan lebih mendekati data pelatihan model. |
| `images` |  | array of object {image_url, start_frame_num, strength} | no | `[]` |  | Gambar conditioning yang diberikan melalui URL. |
| `images[].image_url` |  | string (image URL) | yes |  |  | Gambar conditioning yang diberikan melalui URL. |
| `images[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Posisi awal conditioning gambar, ditentukan menggunakan nomor bingkai yang habis dibagi 8. |
| `images[].strength` |  | number | no | `1` | 0 – 1 | Intensitas conditioning: 0.0 menonaktifkan conditioning, sedangkan 1.0 menerapkannya 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 diberikan untuk conditioning. |
| `videos[].video_url` |  | string (video URL) | yes |  |  | Video conditioning yang diberikan melalui URL. |
| `videos[].conditioning_type` |  | enum (string) \| null | no | `rgb` | `rgb`, `depth`, `pose`, `canny` | Kategori conditioning video yang digunakan untuk memilih pipeline prapemrosesan saat prapemrosesan diaktifkan, dan menerapkan bobot LoRA dalam konteks dengan benar. |
| `videos[].preprocess` |  | boolean | no | `false` |  | Mengaktifkan prapemrosesan agar sesuai dengan jenis conditioning video. Untuk conditioning RGB, mengaktifkannya tidak membuat perubahan. |
| `videos[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Posisi awal conditioning dalam video, ditentukan sebagai nomor bingkai yang habis dibagi 8. |
| `videos[].strength` |  | number | no | `1` | 0 – 1 | Intensitas conditioning: 0.0 menonaktifkan conditioning, sedangkan 1.0 menerapkannya sepenuhnya. |
| `videos[].limit_num_frames` |  | boolean | no | `false` |  | Sakelar batas bingkai video: mengaktifkannya menjadikan `max_num_frames` sebagai batas jumlah bingkai yang diambil dari video. |
| `videos[].max_num_frames` |  | integer | no | `1441` | 1 – 1,441 | Batas penggunaan bingkai video, dengan None memilih seluruh video alih-alih jumlah bingkai terbatas. |
| `videos[].resample_fps` |  | boolean | no | `false` |  | Sakelar konversi laju bingkai video: mengaktifkannya akan melakukan resampling video pada laju yang ditentukan oleh `target_fps`. |
| `videos[].target_fps` |  | integer | no | `24` | 1 – 60 | Laju bingkai resampling dalam FPS, hanya berlaku jika `resample_fps` diaktifkan. |
| `videos[].reverse_video` |  | boolean | no | `false` |  | Pembalikan urutan video untuk tugas yang memerlukan conditioning 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} | File video yang dihasilkan oleh pembuatan. |
| `prompt` | string | Prompt yang digunakan untuk menghasilkan output. |
| `seed` | integer | Nilai seed yang diterapkan selama pembuatan. |

## 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