# LTX-2.3 22B Video to Video Trainer API

> Melatih LoRA LTX-2.3 22B untuk transformasi video atau generasi berkondisi video dari video referensi

Melatih LoRA pada model LTX-2.3 22B untuk transformasi video, menggunakan zip berisi video atau gambar (mp4, mov, avi, mkv, png, jpg, jpeg) dengan rekomendasi minimal sekitar 10 file. `number_of_steps` berkisar dari 1 hingga 20000 (default 2000), dan `frame_rate` default-nya 25. `first_frame_conditioning_p` default-nya 0.1. Validasi menerima hingga 2 prompt dengan video referensi.

- Model id: `ltx23-v2v-trainer`
- Made by: Lightricks
- Category: Training
- Output: file
- Price: $0.0135 / step (cheapest run $27.00 (27000 API credits))
- Run: `POST https://api.doitong.com/v1/run/ltx23-v2v-trainer`
- Quote: `POST https://api.doitong.com/v1/quote/ltx23-v2v-trainer`
- Docs page: https://doitong.com/id/developers/models/ltx23-v2v-trainer

## Pricing

Price: **$0.0135 / step**. Billed per training step (`number_of_steps`).

The full example input below costs $27.00 = 27000 API credits (2000 steps × 13.5 API credits).

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/ltx23-v2v-trainer` 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 |
|---|---|---|---|---|---|---|
| `training_data_url` |  | string | yes |  |  | URL arsip ZIP untuk dataset yang hanya berisi gambar atau hanya berisi video; dataset media campuran tidak didukung. Usahakan 10 file atau lebih, sebaiknya koleksi yang lebih besar. Ekstensi yang diterima adalah .png, .jpg, .jpeg untuk gambar dan .mp4, .mov, .avi, .mkv untuk video. File teks caption opsional dapat menyertai media, dengan setiap file caption diberi nama yang sesuai dengan file media terkait. |
| `rank` |  | enum (integer) | no | `32` | `8`, `16`, `32`, `64`, `128` | Rank adaptasi LoRA, yang menukar peningkatan konsumsi memori dengan kapasitas yang lebih besar seiring naiknya pengaturan. |
| `number_of_steps` |  | integer | no | `2000` | 1 – 20,000 | Jumlah total langkah pelatihan. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Laju pembelajaran pengoptimalan, menyeimbangkan kemungkinan percepatan pelatihan dengan risiko overfitting saat dinaikkan. |
| `number_of_frames` |  | integer | no | `89` | 9 – 121 | Jumlah bingkai untuk setiap sampel pelatihan. Membagi jumlah tersebut dengan 8 harus menyisakan sisa 1; contohnya 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97. |
| `frame_rate` |  | integer | no | `25` | 8 – 60 | Laju bingkai video target, diukur dalam bingkai per detik. |
| `resolution` |  | enum (string) | no | `medium` | `low`, `medium`, `high` | Resolusi pelatihan. Meningkatkan resolusi akan meningkatkan kebutuhan memori. |
| `aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Rasio aspek pelatihan. |
| `trigger_phrase` |  | string | no | `` |  | Frasa aktivasi gaya LoRA, disisipkan di awal caption selama pelatihan. |
| `auto_scale_input` |  | boolean | no | `false` |  | Penyesuaian video otomatis agar sesuai dengan FPS dan jumlah bingkai yang diminta saat diaktifkan; dataset gambar tidak berubah. |
| `split_input_into_scenes` |  | boolean | no | `true` |  | Pemisahan adegan untuk video yang melebihi ambang durasi, diaktifkan saat true. |
| `split_input_duration_threshold` |  | number | no | `30` | 1 – 60 | Pemicu pemisahan adegan yang dinyatakan sebagai durasi dalam detik: hanya video yang lebih panjang dari ambang batas yang dipisahkan. |
| `debug_dataset` |  | boolean | no | `false` |  | Mode pemeriksaan prapemrosesan untuk memeriksa video, gambar, dan caption sebelum proses pelatihan penuh. Mengaktifkannya membuat trainer mengembalikan dataset pelatihan yang telah diproses dalam arsip yang dapat diunduh untuk ditinjau secara manual. |
| `first_frame_conditioning_p` |  | number | no | `0.1` | 0 – 1 | Utamakan transformasi video-to-video dengan mengurangi frekuensi pelatihan yang menggunakan pengondisian bingkai pertama; nilai ini menetapkan probabilitas tersebut. |
| `validation` |  | array of object {prompt, reference_video_url} | no | `[]` | ≤ 2 items | Daftar input validasi yang berisi prompt berpasangan dengan video referensi. |
| `validation[].prompt` |  | string | yes |  |  | Prompt yang digunakan selama validasi. |
| `validation[].reference_video_url` |  | string (video URL) | yes |  | ≥ 1 characters | Sediakan video yang akan ditransformasi selama validasi IC-LoRA melalui URL-nya. |
| `validation_negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Prompt negatif yang diterapkan selama validasi. |
| `validation_number_of_frames` |  | integer | no | `89` | 9 – 121 | Jumlah bingkai untuk setiap video validasi. |
| `validation_frame_rate` |  | integer | no | `25` | 8 – 60 | Laju bingkai video validasi target, diukur dalam bingkai per detik. |
| `validation_resolution` |  | enum (string) | no | `high` | `low`, `medium`, `high` | Resolusi yang digunakan selama validasi. |
| `validation_aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Rasio aspek yang digunakan selama validasi. |
| `stg_scale` |  | number | no | `1` | 0 – 3 | Skala Spatio-Temporal Guidance (STG), dengan 1.0 direkomendasikan dan 0.0 menonaktifkan STG. |

## Example input

Required parameters only:

```json
{
  "training_data_url": "https://example.com/your-file.zip"
}
```

A fuller example:

```json
{
  "training_data_url": "https://example.com/your-file.zip"
}
```

## 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/ltx23-v2v-trainer?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "training_data_url": "https://example.com/your-file.zip"
  }'

# 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/ltx23-v2v-trainer?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "training_data_url": "https://example.com/your-file.zip"
  }),
});
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 = {
    "training_data_url": "https://example.com/your-file.zip",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx23-v2v-trainer", 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": "ltx23-v2v-trainer",
      "input": {
        "training_data_url": "https://example.com/your-file.zip"
      },
      "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": "ltx23-v2v-trainer",
  "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": 27000,
    "usd": "27.00",
    "charged": 27000,
    "refunded": 0
  },
  "input": {
    "training_data_url": "https://example.com/your-file.zip"
  },
  "output": {
    "video": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "lora_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "config_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "debug_dataset": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    }
  },
  "outputs": [
    {
      "type": "file",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "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} \| null | URL video validasi yang tersedia, ditampilkan bersama video referensinya. |
| `lora_file` | object {url, content_type, file_name, file_size} | URL file bobot IC-LoRA terlatih dalam format .safetensors. |
| `config_file` | object {url, content_type, file_name, file_size} | Konfigurasi untuk penyiapan endpoint inferensi. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Arsip debug yang dapat diunduh, hanya tersedia untuk input yang mengaktifkan `debug_dataset`; isinya data pelatihan yang telah diproses sebelumnya dan mencakup video dalam bentuk terdekode. |

## Related models

- [LTX 2.3 Trainer (V2) - Audio Inpainting](https://doitong.com/developers/models/ltx23-trainer-v2/audio-inpaint.md) (`ltx23-trainer-v2/audio-inpaint`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Audio+Video Reference IC-LoRA](https://doitong.com/developers/models/ltx23-trainer-v2/ic-lora/av2av.md) (`ltx23-trainer-v2/ic-lora/av2av`): $0.0068 / step
- [LTX 2.3 Trainer (V2) - Audio+Video Reference Transformation](https://doitong.com/developers/models/ltx23-trainer-v2/av2av.md) (`ltx23-trainer-v2/av2av`): $0.0068 / step
- [LTX 2.3 Trainer (V2) - Audio-to-Audio](https://doitong.com/developers/models/ltx23-trainer-v2/a2a.md) (`ltx23-trainer-v2/a2a`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Audio-to-Audio IC-LoRA](https://doitong.com/developers/models/ltx23-trainer-v2/ic-lora/a2a.md) (`ltx23-trainer-v2/ic-lora/a2a`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Audio-to-Video](https://doitong.com/developers/models/ltx23-trainer-v2/a2v.md) (`ltx23-trainer-v2/a2v`): $0.006 / step
- [LTX 2.3 Trainer (V2) - Backward Audio Extension](https://doitong.com/developers/models/ltx23-trainer-v2/audio-extend-suffix.md) (`ltx23-trainer-v2/audio-extend-suffix`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Backward Video Extension](https://doitong.com/developers/models/ltx23-trainer-v2/extend-suffix.md) (`ltx23-trainer-v2/extend-suffix`): $0.0061 / step

## Links

- HTML page: https://doitong.com/developers/models/ltx23-v2v-trainer
- 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=ltx23-v2v-trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/ltx23-v2v-trainer