# LTX-2.3 22B Video to Video Trainer API

> Trener en LTX-2.3 22B-LoRA for videottransformasjon eller referansevideobetinget generering

Trener en LoRA på LTX-2.3 22B-modellen for videottransformasjon ved hjelp av et zip-arkiv med videoer eller bilder (mp4, mov, avi, mkv, png, jpg, jpeg); minst omtrent 10 filer anbefales. `number_of_steps` går fra 1 til 20000 (standard 2000), og `frame_rate` er som standard 25. `first_frame_conditioning_p` er som standard 0.1. Validering godtar opptil 2 ledetekster med referansevideoer.

- 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/no/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 til et ZIP-arkiv for et datasett som utelukkende inneholder bilder eller utelukkende inneholder videoer; datasett med blandede medier støttes ikke. Sikt mot 10 filer eller flere, helst en større samling. Godkjente filtyper er .png, .jpg, .jpeg for bilder og .mp4, .mov, .avi, .mkv for videoer. Valgfrie tekstfiler med bildetekster kan følge mediene, og hver bildetekstfil må ha samme navn som den tilhørende mediefilen. |
| `rank` |  | enum (integer) | no | `32` | `8`, `16`, `32`, `64`, `128` | LoRA-tilpasningsrangering, som bytter økt minnebruk mot større kapasitet når innstillingen økes. |
| `number_of_steps` |  | integer | no | `2000` | 1 – 20,000 | Totalt antall treningstrinn. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Læringsrate for optimalisering, som balanserer en mulig hastighetsøkning i treningen mot risikoen for overtilpasning når den økes. |
| `number_of_frames` |  | integer | no | `89` | 9 – 121 | Antall bilder i hvert treningseksempel. Når antallet deles på 8, må resten være 1; eksempler er 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97. |
| `frame_rate` |  | integer | no | `25` | 8 – 60 | Målfrekvens for video, målt i bilder per sekund. |
| `resolution` |  | enum (string) | no | `medium` | `low`, `medium`, `high` | Treningsoppløsning. Høyere oppløsning øker minnekravene. |
| `aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Treningssideforhold. |
| `trigger_phrase` |  | string | no | `` |  | LoRA-stilens aktiveringsfrase, satt inn først i bildetekstene under trening. |
| `auto_scale_input` |  | boolean | no | `false` |  | Automatisk videojustering for å samsvare med forespurt FPS og antall bilder når aktivert; bildedatasett forblir uendret. |
| `split_input_into_scenes` |  | boolean | no | `true` |  | Scenedeling for videoer som overskrider en varighetsterskel, aktivert når verdien er true. |
| `split_input_duration_threshold` |  | number | no | `30` | 1 – 60 | Utløser for scenedeling uttrykt som en varighet i sekunder: bare videoer som er lengre enn terskelen, deles. |
| `debug_dataset` |  | boolean | no | `false` |  | Inspeksjonsmodus for forbehandling, for kontroll av videoer, bilder og bildetekster før en full treningskjøring. Når den aktiveres, returnerer treneren det behandlede treningsdatasettet i et nedlastbart arkiv for manuell gjennomgang. |
| `first_frame_conditioning_p` |  | number | no | `0.1` | 0 – 1 | Prioriter video-til-video-transformasjon ved å redusere hvor ofte treningen bruker betinging på første bilde; denne verdien angir sannsynligheten. |
| `validation` |  | array of object {prompt, reference_video_url} | no | `[]` | ≤ 2 items | Valideringsliste for inndata med prompter koblet til referansevideoer. |
| `validation[].prompt` |  | string | yes |  |  | Prompt brukt under validering. |
| `validation[].reference_video_url` |  | string (video URL) | yes |  | ≥ 1 characters | Lever videoen som skal transformeres under IC-LoRA-validering via URL-en. |
| `validation_negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Negativ prompt brukt under validering. |
| `validation_number_of_frames` |  | integer | no | `89` | 9 – 121 | Antall bilder i hver valideringsvideo. |
| `validation_frame_rate` |  | integer | no | `25` | 8 – 60 | Målfrekvens for valideringsvideo, målt i bilder per sekund. |
| `validation_resolution` |  | enum (string) | no | `high` | `low`, `medium`, `high` | Oppløsning brukt under validering. |
| `validation_aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Sideforhold brukt under validering. |
| `stg_scale` |  | number | no | `1` | 0 – 3 | Spatio-Temporal Guidance (STG)-skala, der 1.0 anbefales og 0.0 slår av 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 til tilgjengelige valideringsvideoer, vist sammen med referansevideoene deres. |
| `lora_file` | object {url, content_type, file_name, file_size} | URL til den trente IC-LoRA-vektfilen i .safetensors-format. |
| `config_file` | object {url, content_type, file_name, file_size} | Konfigurasjon for oppsett av inferensendepunkt. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Nedlastbart feilsøkingsarkiv, bare tilgjengelig for inndata som aktiverer `debug_dataset`; innholdet fra treningsdataene er forhåndsbehandlet og inkluderer videoer i dekodet form. |

## 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