# LTX-2.3 22B · Text to Video · LoRA API

> Test għal vidjo li japplika l-LoRAs tiegħek permezz tal-input meħtieġ `loras`

Jieħu `prompt` u lista meħtieġa `loras` ta’ LoRAs personalizzati. `video_size` joffri daqsijiet kwadri, ritratt u pajsaġġ issettjati minn qabel, `num_frames` huwa minn 9 sa 481, u `fps` minn 1 sa 60. Huma disponibbli wkoll ġenerazzjoni tal-awdjo, camera LoRA u skala distill LoRA tal-ewwel pass.

- Model id: `ltx-2.3-22b/text-to-video/lora`
- Made by: Lightricks
- Category: Text to Video
- Output: video
- Price per megapixel of your output video: $0.00181 / megapixel of output video (cheapest run $0.002 (2 API credits))
- Run: `POST https://api.doitong.ru/v1/run/ltx-2.3-22b/text-to-video/lora`
- Quote: `POST https://api.doitong.ru/v1/quote/ltx-2.3-22b/text-to-video/lora`
- Docs page: https://doitong.com/mt/developers/models/ltx-2.3-22b/text-to-video/lora

## Pricing

Price: **$0.00181 / megapixel of output video**. Price per megapixel 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 $0.203 = 203 API credits (112 MP output video × $0.001805).

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/ltx-2.3-22b/text-to-video/lora` 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 |  |  | Prompt użat bħala bażi għall-ġenerazzjoni tal-vidjo. |
| `num_frames` |  | integer | no | `121` | 9 – 481 | Għadd ta' frejms għall-output iġġenerat. |
| `video_size` |  | object {width, height} \| enum (string) | no | `landscape_16_9` | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | Dimensjonijiet tal-vidjo ġġenerat. |
| `generate_audio` |  | boolean | no | `true` |  | Jikkontrolla jekk il-vidjo jinkludix awdjo ġġenerat. |
| `use_multiscale` |  | boolean | no | `true` |  | Toggle ta' ġenerazzjoni fuq skali multipli għal koerenza u dettall aħjar. B'True, vidjo preliminari iżgħar jiggwida t-tieni ġenerazzjoni li d-daqs tagħha jilħaq jew jaqbeż id-dimensjonijiet mitluba. |
| `fps` |  | number | no | `24` | 1 – 60 | Rata tal-frejms tal-vidjo tal-output, imkejla fi frejms kull sekonda. |
| `num_inference_steps` |  | integer | no | `40` | 8 – 50 | Għadd tal-passi tal-inference mwettqa. |
| `video_cfg_scale` |  | number | no | `3` | 1 – 20 | Setting tal-gwida tal-vidjo: żidu għal konsistenza u fokus akbar fil-kontenut iġġenerat. Juża classifier-free guidance (CFG). |
| `video_stg_scale` |  | number | no | `0` | 0 – 20 | Kontroll tal-konsistenza u l-fokus tal-vidjo permezz tal-iskala Spatiotemporal Guidance (STG); żid is-setting biex issaħħaħ iż-żewġ kwalitajiet. |
| `video_rescaling_scale` |  | number | no | `0.7` | 0 – 1 | Aġġustament tar-riscalar tal-vidjo biex jibbilanċja classifier-free ma' spatiotemporal guidance bħala proporzjon. |
| `video_modality_scale` |  | number | no | `3` | 0 – 10 | Aġġustament tal-modalità tal-vidjo biex jibbilanċja l-kontribuzzjonijiet tal-vidjo u l-awdjo bħala proporzjon. |
| `audio_cfg_scale` |  | number | no | `7` | 1 – 20 | Setting tal-gwida tal-awdjo: żidu għal konsistenza u fokus akbar fil-kontenut iġġenerat. Juża classifier-free guidance (CFG). |
| `audio_stg_scale` |  | number | no | `0` | 0 – 20 | Kontroll tal-konsistenza u l-fokus tal-awdjo permezz tal-iskala Spatiotemporal Guidance (STG); żid is-setting biex issaħħaħ iż-żewġ kwalitajiet. |
| `audio_rescaling_scale` |  | number | no | `0.7` | 0 – 1 | Aġġustament tar-riscalar tal-awdjo biex jibbilanċja classifier-free ma' spatiotemporal guidance bħala proporzjon. |
| `audio_modality_scale` |  | number | no | `3` | 0 – 10 | Aġġustament tal-modalità tal-awdjo biex jibbilanċja l-kontribuzzjonijiet tal-vidjo u l-awdjo bħala proporzjon. |
| `gradient_estimation_gamma` |  | number | no | `2` | 0 – 10 | Setting tal-istima tal-gradjent tad-denoising: 0 jitfi l-istima; inkella, il-valur jispeċifika gamma. |
| `use_restart_sampling` |  | boolean | no | `false` |  | Toggle ta' kampjunar mill-ġdid għall-kwalità potenzjalment aħjar tal-vidjo: l-attivazzjoni żżid ftit storbju f'kull pass tad-denoising. |
| `scheduler` |  | enum (string) | no | `ltx2` | `ltx2`, `linear_quadratic`, `beta` | Għażla tal-iskedulatur. |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular`, `high`, `full` | Livell ta’ aċċelerazzjoni applikat għall-ipproċessar. |
| `camera_lora` |  | enum (string) | no | `none` | `dolly_in`, `dolly_out`, `dolly_left`, `dolly_right`, `jib_up`, `jib_down`, `static`, `none` | Għażla tal-Camera LoRA għall-kontroll aktar preċiż tal-moviment fil-vidjo iġġenerat milli bi prompts tal-moviment tal-kamera biss. |
| `camera_lora_scale` |  | number | no | `1` | 0 – 1 | Saħħa tal-Camera LoRA għall-kontroll aktar preċiż tal-moviment fil-vidjo iġġenerat milli bi prompts tal-moviment tal-kamera biss. |
| `negative_prompt` |  | string | no | `news broadcast, 3d animation, computer graphics, pc game, console game, video game, cartoon, childish, watermark, logo, text, on screen text, subtitles, titles, signature, slowmo, static` |  | Prompt negattiv użat waqt il-ġenerazzjoni tal-vidjo. |
| `seed` |  | integer \| null | no |  |  | Valur seed li jibda l-ġenerazzjoni ta' numri bl-addoċċ. |
| `enable_prompt_expansion` |  | boolean | no | `true` |  | Jikkontrolla jekk l-espansjoni tal-prompt hijiex attivata. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Jikkontrolla jekk il-verifika tas-sikurezza hijiex attivata. |
| `video_output_type` |  | enum (string) | no | `X264 (.mp4)` | `X264 (.mp4)`, `VP9 (.webm)`, `PRORES4444 (.mov)`, `GIF (.gif)` | Tip tal-output għall-vidjo li jirriżulta. |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | Livell tal-kwalità għall-vidjo li jirriżulta. |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | Modalità tal-kitba għall-vidjo li jirriżulta. |
| `loras` |  | array of object {path, weight_name, scale} | yes |  |  | Għażla tal-LoRA għall-ġenerazzjoni. |
| `loras[].path` |  | string | yes |  |  | Post tal-piżijiet tal-LoRA, mogħti bħala URL jew ID ta' repożitorju HuggingFace fil-format owner/repo. |
| `loras[].weight_name` |  | string \| null | no |  |  | Isem tal-piż tal-LoRA għall-għażla bejn piżijiet multipli f'repożitorju HuggingFace; meħtieġ biss għal repożitorji b'piżijiet multipli. Dan is-setting m'għandu l-ebda effett sakemm `path` ma jidentifikax repożitorju HuggingFace. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Fattur ta’ skalar applikat għal-LoRA, minn 0.0 sa 4.0. |
| `distill_lora_first_pass_scale` |  | number | no | `0.2` | 0 – 1 | Ikkontrolla l-iskalar tad-distill LoRA tal-ewwel pass, billi tuża 0 biex titfih. |
| `distill_lora_second_pass_scale` |  | number | no | `0.5` | 0 – 1 | Ikkontrolla l-iskalar tad-distill LoRA għal kull pass wara l-ewwel. |

## Example input

Required parameters only:

```json
{
  "loras": [
    {
      "path": "https://example.com/your-file.safetensors"
    }
  ],
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "loras": [
    {
      "path": "https://example.com/your-file.safetensors"
    }
  ],
  "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.ru/v1/run/ltx-2.3-22b/text-to-video/lora?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "loras": [
      {
        "path": "https://example.com/your-file.safetensors"
      }
    ],
    "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.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/ltx-2.3-22b/text-to-video/lora?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "loras": [
      {
        "path": "https://example.com/your-file.safetensors"
      }
    ],
    "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.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}

payload = {
    "loras": [
        {
            "path": "https://example.com/your-file.safetensors",
        },
    ],
    "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-2.3-22b/text-to-video/lora", 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": "ltx-2.3-22b/text-to-video/lora",
      "input": {
        "loras": [
          {
            "path": "https://example.com/your-file.safetensors"
          }
        ],
        "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-2.3-22b/text-to-video/lora",
  "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": 203,
    "usd": "0.203",
    "charged": 203,
    "refunded": 0
  },
  "input": {
    "loras": [
      {
        "path": "https://example.com/your-file.safetensors"
      }
    ],
    "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,
      "width": 1024,
      "height": 768,
      "fps": 1.0,
      "duration": 5,
      "num_frames": 1
    },
    "seed": 42,
    "prompt": "A cowboy walking through a dusty town at high noon, camera following from behind, cinematic depth, realistic lighting, western mood, 4K film grain."
  },
  "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.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, width, height, fps, duration, num_frames} | Vidjo prodott mill-mudell. |
| `seed` | integer | Valur taż-żerriegħa użat biex jiġi inizjalizzat il-ġeneratur tan-numri każwali. |
| `prompt` | string | Prompt użat biex jiggwida l-ġenerazzjoni. |

## Related models

- [LTX-2.3 22B · Text to Video](https://doitong.com/developers/models/ltx-2.3-22b/text-to-video.md) (`ltx-2.3-22b/text-to-video`): $0.00161 / megapixel of output video
- [LTX-2.3 22B · Image to Video](https://doitong.com/developers/models/ltx-2.3-22b/image-to-video.md) (`ltx-2.3-22b/image-to-video`): $0.00161 / megapixel of output video
- [LTX-2.3 22B · Image to Video · LoRA](https://doitong.com/developers/models/ltx-2.3-22b/image-to-video/lora.md) (`ltx-2.3-22b/image-to-video/lora`): $0.00181 / megapixel of output video
- [LTX 2.3 22B](https://doitong.com/developers/models/ltx-2.3-22b/reference-video-to-video/lora.md) (`ltx-2.3-22b/reference-video-to-video/lora`): $0.00181 / megapixel of output video
- [LTX-2.3 22B · Extend Video](https://doitong.com/developers/models/ltx-2.3-22b/extend-video.md) (`ltx-2.3-22b/extend-video`): $0.00161 / megapixel of output video
- [LTX-2.3 22B · Extend Video · LoRA](https://doitong.com/developers/models/ltx-2.3-22b/extend-video/lora.md) (`ltx-2.3-22b/extend-video/lora`): $0.00181 / megapixel of output video
- [LTX-2.3 22B · Reference Video to Video](https://doitong.com/developers/models/ltx-2.3-22b/reference-video-to-video.md) (`ltx-2.3-22b/reference-video-to-video`): $0.00161 / megapixel of output video
- [LTX-2.3 22B · Video to Video](https://doitong.com/developers/models/ltx-2.3-22b/video-to-video.md) (`ltx-2.3-22b/video-to-video`): $0.00161 / megapixel of output video

## Links

- HTML page: https://doitong.com/developers/models/ltx-2.3-22b/text-to-video/lora
- 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-2.3-22b/text-to-video/lora
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/ltx-2.3-22b/text-to-video/lora