# LTX 2.3 Trainer (V2) - Spatial Outpainting API

> Træner en LoRA, der udvider en video udad omkring et fast indre rektangel, du definerer

Træner en LoRA til spatial outpainting, hvor et indre rektangel forbliver fast, og modellen genererer området omkring det. `crop_top_left` og `crop_bottom_right` er påkrævede (x, y)-hjørner for det bevarede rektangel, angivet i pixels ved den træningsopløsning, der sættes af `resolution` og `aspect_ratio`, og skal ligge inden for billedet. Valideringseksempler bruger hver en video på hele lærredet.

- Model id: `ltx23-trainer-v2/outpaint`
- Made by: Lightricks
- Category: Training
- Output: file
- Price: $0.006 / step (cheapest run $12.00 (12000 API credits))
- Run: `POST https://api.doitong.com/v1/run/ltx23-trainer-v2/outpaint`
- Quote: `POST https://api.doitong.com/v1/quote/ltx23-trainer-v2/outpaint`
- Docs page: https://doitong.com/da/developers/models/ltx23-trainer-v2/outpaint

## Pricing

Price: **$0.006 / step**. Billed per training step (`number_of_steps`).

The full example input below costs $12.00 = 12000 API credits (2000 steps × 6 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-trainer-v2/outpaint` 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 en `.zip` med træningsdata organiseret efter den valgte træningstilstand. Hver mediefil kan have en tilhørende `.txt`-beskrivelse med samme basisnavn. |
| `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` | 100 – 10,000 | Samlet antal træningstrin. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Læringsrate for optimeringen. |
| `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 | `24` | 8 – 60 | Målframerate for træningsvideoen i frames per sekund. Den oprindelige rate for LTX-2.3 er 24. |
| `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. |
| `crop_top_left` |  | array of any | yes |  | 2 – 2 items | Øverste venstre hjørne (x1, y1) af det indre rektangel, der skal bevares, fastgjort til et latent 32px-gitter inden for den træningsopløsningsbucket, der vælges af opløsning × `aspect_ratio`. Beskæring sker efter ændring af størrelsen til denne bucket, så koordinaterne bruger ikke den uploadede videos oprindelige dimensioner. |
| `crop_bottom_right` |  | array of any | yes |  | 2 – 2 items | Koordinaterne for nederste højre hjørne (x2, y2), der definerer det indre rektangel, som skal bevares; modellen genererer alt uden for det. Angivet i pixels ved træningsopløsningen skal dette hjørne være inden for træningsframen og opfylde x2 > x1 og y2 > y1. |
| `validation` |  | array of object {prompt, video_url} | no | `[]` | ≤ 2 items | Valideringsdata til outpainting, leveret som en liste over poster med en prompt og en video på fuldt lærred. |
| `validation[].prompt` |  | string | yes |  |  | Prompt, der bruges under validering. |
| `validation[].video_url` |  | string (video URL) | yes |  | ≥ 1 characters | URL til video på fuldt lærred til outpainting af det omgivende område. |
| `validation_negative_prompt` |  | string | no | `blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts.` |  | Negativ prompt anvendt under validering, som bruger en forhåndsvisningsproces i ét trin. Da processen tilnærmer destilleret produktionsinferens i to trin, adskiller dens vejledning og forhåndsvisningskvalitet sig fra de endelige resultater. |
| `validation_number_of_frames` |  | integer | no | `89` | 9 – 121 | Frameantal for hver valideringsvideo. |
| `validation_frame_rate` |  | integer | no | `24` | 8 – 60 | Målframerate for valideringsvideoer i frames per sekund; LTX-2.3 kører oprindeligt ved 24. |
| `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",
  "crop_top_left": [
    0,
    0
  ],
  "crop_bottom_right": [
    512,
    512
  ]
}
```

A fuller example:

```json
{
  "training_data_url": "https://example.com/your-file.zip",
  "crop_top_left": [
    0,
    0
  ],
  "crop_bottom_right": [
    512,
    512
  ]
}
```

## 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-trainer-v2/outpaint?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "training_data_url": "https://example.com/your-file.zip",
    "crop_top_left": [
      0,
      0
    ],
    "crop_bottom_right": [
      512,
      512
    ]
  }'

# 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-trainer-v2/outpaint?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "training_data_url": "https://example.com/your-file.zip",
    "crop_top_left": [
      0,
      0
    ],
    "crop_bottom_right": [
      512,
      512
    ]
  }),
});
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",
    "crop_top_left": [
        0,
        0,
    ],
    "crop_bottom_right": [
        512,
        512,
    ],
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx23-trainer-v2/outpaint", 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-trainer-v2/outpaint",
      "input": {
        "training_data_url": "https://example.com/your-file.zip",
        "crop_top_left": [
          0,
          0
        ],
        "crop_bottom_right": [
          512,
          512
        ]
      },
      "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-trainer-v2/outpaint",
  "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": 12000,
    "usd": "12.00",
    "charged": 12000,
    "refunded": 0
  },
  "input": {
    "training_data_url": "https://example.com/your-file.zip",
    "crop_top_left": [
      0,
      0
    ],
    "crop_bottom_right": [
      512,
      512
    ]
  },
  "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
    },
    "audio": {
      "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 | Samlet videoforhåndsvisning fra valideringen, når den er tilgængelig for endpoints, der producerer video. |
| `audio` | object {url, content_type, file_name, file_size} \| null | Samlet lyd fra valideringen, leveret som forhåndsvisning, når den er tilgængelig for disse lydruter: /audio-inpaint, /audio-extend-suffix, /audio-extend-prefix, /t2a, /a2a og /v2a. |
| `lora_file` | object {url, content_type, file_name, file_size} | URL til trænede LoRA-vægte 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 | Debug-output, der er tilgængeligt, når `debug_dataset` er aktiveret: et arkiv, der kan downloades, og som indeholder træningsdataene efter præprocessering. |

## 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-trainer-v2/outpaint
- 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-trainer-v2/outpaint
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/ltx23-trainer-v2/outpaint