# LTX-2.3 22B Video to Video Trainer API

> Træner en LTX-2.3 22B-LoRA til videotransformation eller referencevideobetinget generering

Træner en LoRA på LTX-2.3 22B-modellen til videotransformation ved hjælp af et zip-arkiv med videoer eller billeder (mp4, mov, avi, mkv, png, jpg, jpeg); mindst cirka 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 accepterer op til 2 prompts med referencevideoer.

- 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.ru/v1/run/ltx23-v2v-trainer`
- Quote: `POST https://api.doitong.ru/v1/quote/ltx23-v2v-trainer`
- Docs page: https://doitong.com/da/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.ru/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 med et datasæt, der udelukkende indeholder billeder eller udelukkende videoer; datasæt med blandede medier understøttes ikke. Sigt efter 10 filer eller flere, helst en større samling. Accepterede filtypenavne er .png, .jpg, .jpeg for billeder og .mp4, .mov, .avi, .mkv for videoer. Valgfrie billedteksts-filer kan ledsage medierne, hvor hver billedtekstfil navngives, så den matcher den tilknyttede mediefil. |
| `rank` |  | enum (integer) | no | `32` | `8`, `16`, `32`, `64`, `128` | LoRA-tilpasningsrang, der bytter øget hukommelsesforbrug for større kapacitet, når indstillingen øges. |
| `number_of_steps` |  | integer | no | `2000` | 1 – 20,000 | Samlet antal træningstrin. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Optimeringens læringsrate, der afvejer en mulig hastighedsforøgelse i træningen mod risikoen for overtilpasning, når den øges. |
| `number_of_frames` |  | integer | no | `89` | 9 – 121 | Antal frames i hver træningsprøve. Når antallet divideres med 8, skal 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 | Målvideoframefrekvens målt i frames per sekund. |
| `resolution` |  | enum (string) | no | `medium` | `low`, `medium`, `high` | Træningsopløsning. En højere opløsning øger hukommelseskravene. |
| `aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Træningens størrelsesforhold. |
| `trigger_phrase` |  | string | no | `` |  | Aktiveringsudtryk for LoRA-stilen, indsat i begyndelsen af beskrivelserne under træning. |
| `auto_scale_input` |  | boolean | no | `false` |  | Automatisk videojustering, så den matcher den anmodede FPS og frameantal, når den aktiveres; billeddatasæt forbliver uændrede. |
| `split_input_into_scenes` |  | boolean | no | `true` |  | Scenedeling for videoer, der overskrider en varighedstærskel, aktiveret når værdien er true. |
| `split_input_duration_threshold` |  | number | no | `30` | 1 – 60 | Trigger for scenedeling angivet som en varighed i sekunder: kun videoer, der er længere end tærsklen, opdeles. |
| `debug_dataset` |  | boolean | no | `false` |  | Inspektionstilstand for præprocessering, der bruges til at kontrollere videoer, billeder og beskrivelser før en fuld træningskørsel. Aktivering får træneren til at returnere det behandlede træningsdatasæt i et arkiv, der kan downloades til manuel gennemgang. |
| `first_frame_conditioning_p` |  | number | no | `0.1` | 0 – 1 | Frem video-til-video-transformation ved at reducere, hvor ofte træningen bruger betingelse på første frame; denne værdi angiver sandsynligheden. |
| `validation` |  | array of object {prompt, reference_video_url} | no | `[]` | ≤ 2 items | Valideringsinputliste med prompts parret med referencevideoer. |
| `validation[].prompt` |  | string | yes |  |  | Prompt, der bruges under validering. |
| `validation[].reference_video_url` |  | string (video URL) | yes |  | ≥ 1 characters | Lever videoen, der skal transformeres under IC-LoRA-validering, via dens URL. |
| `validation_negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Negativ prompt, der anvendes under validering. |
| `validation_number_of_frames` |  | integer | no | `89` | 9 – 121 | Frameantal for hver valideringsvideo. |
| `validation_frame_rate` |  | integer | no | `25` | 8 – 60 | Målvideoframefrekvens under validering målt i frames per sekund. |
| `validation_resolution` |  | enum (string) | no | `high` | `low`, `medium`, `high` | Opløsning, der bruges under validering. |
| `validation_aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Størrelsesforhold, der bruges under validering. |
| `stg_scale` |  | number | no | `1` | 0 – 3 | Skala for Spatio-Temporal Guidance (STG), hvor 1.0 anbefales, og 0.0 deaktiverer 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.ru/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.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/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.ru/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.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": "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.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} \| null | URL til eventuelle tilgængelige valideringsvideoer, vist sammen med deres referencevideoer. |
| `lora_file` | object {url, content_type, file_name, file_size} | URL til den trænede IC-LoRA-vægtfil i .safetensors-format. |
| `config_file` | object {url, content_type, file_name, file_size} | Konfiguration til opsætning af inferens-endpointet. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Downloadbart fejlfindingsarkiv, kun tilgængeligt for input, der aktiverer `debug_dataset`; dets træningsdata er forbehandlet og indeholder videoer i afkodet 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.ru/v1/models/ltx23-v2v-trainer