# LTX Video-0.9.7 13B Distilled API

> Text-to-video dengan model distilled 13B lama 0.9.7, yang mendukung LoRA dan jumlah bingkai panjang

Membuat video dari `prompt` wajib menggunakan model terdistilasi 0.9.7. Resolusi berupa 480p atau 720p, rasio aspek 9:16, 1:1, atau 16:9, dan `num_frames` dapat bernilai 9 hingga 1441 pada 1 hingga 60 fps. Masing-masing dari dua pass sampling memerlukan 2 hingga 12 langkah. `loras` opsional, pass detail tambahan, dan perluasan prompt tersedia.

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

## 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.com/v1/quote/ltx-video-13b-distilled` 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 | `16:9` | `9:16`, `1:1`, `16:9` | Rasio aspek video hasil. |
| `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. |

## 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/ltx-video-13b-distilled?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/ltx-video-13b-distilled?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/ltx-video-13b-distilled", 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": "ltx-video-13b-distilled",
      "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",
  "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 cinematic fast-tracking shot follows a vintage, teal camper van as it descends a winding mountain trail. The van, slightly weathered but well-maintained, is the central focus, its retro design emphasized by the motion blur. Medium shot reveals the dusty, ochre trail, edged with vibrant green pine trees. Close-up on the van's tires shows the gravel spraying, highlighting the speed and rugged terrain. Sunlight filters through the trees, casting dappled shadows on the van and the trail. The background is a hazy, majestic mountain range bathed in warm, golden light. The overall mood is adventurous and exhilarating. High resolution 4k movie scene.",
    "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.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} | 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-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 Video-0.9.7 13B Distilled · Multiconditioning](https://doitong.com/developers/models/ltx-video-13b-distilled/multiconditioning.md) (`ltx-video-13b-distilled/multiconditioning`): from $0.04 / second of output video
- [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
- [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
- [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/ltx-video-13b-distilled
- 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
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/ltx-video-13b-distilled