# Ltx 2.3 Quality · Text to Audio · LoRA API

> 최대 3개의 사용자 지정 LoRA를 적용한 텍스트-오디오 생성

텍스트 `prompt`에서 오디오를 생성하며, 최대 3개의 사용자 지정 LoRA로 구성된 `loras` 목록이 필요합니다. 각 LoRA는 최대 3 GB입니다. 길이는 대략 `num_frames`를 `frames_per_second`로 나눈 값이며, `num_frames`는 9~481입니다. `negative_prompt`, `seed`, 1~20 범위의 `guidance_scale`도 지원합니다.

- Model id: `ltx-2.3-quality/text-to-audio/lora`
- Made by: Lightricks
- Category: Audio
- Output: audio
- Estimated price per run: from $0.70 / run (cheapest run $0.70 (700 API credits))
- Run: `POST https://api.doitong.ru/v1/run/ltx-2.3-quality/text-to-audio/lora`
- Quote: `POST https://api.doitong.ru/v1/quote/ltx-2.3-quality/text-to-audio/lora`
- Docs page: https://doitong.com/ko/developers/models/ltx-2.3-quality/text-to-audio/lora

## Pricing

Price: **from $0.70 / run** (the lowest tier; the quote prices your exact input). Estimated from the typical cost of one run. The quote is exactly what the run charges.

Price rules (the first rule that matches the input wins; a parameter you leave out counts as its default):

- When `num_frames` > 363: base price × 4
- When `num_frames` ≤ 363: base price × 3
- When `num_frames` ≤ 242: base price × 2

The full example input below costs $0.70 = 700 API credits (1 request × 700 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/ltx-2.3-quality/text-to-audio/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 |  |  | 생성할 오디오를 설명하는 프롬프트입니다. |
| `num_frames` |  | integer | no | `121` | 9 – 481 | LTX 동영상 프레임 수로 지정하는 오디오 길이입니다. 대략적인 길이를 초 단위로 구하려면 `num_frames`를 `frames_per_second`로 나눕니다. |
| `frames_per_second` |  | number | no | `24` | 1 – 60 | 출력 오디오 길이 계산에 사용할 프레임 속도입니다. |
| `num_inference_steps` |  | integer | no | `15` | 8 – 30 | 전체 추론 단계 수입니다. |
| `guidance_scale` |  | number | no | `1` | 1 – 20 | 분류기 없는 가이던스의 강도입니다. |
| `negative_prompt` |  | string | no | `pc game, console game, video game, cartoon, childish, ugly` |  | 생성에서 피해야 할 항목을 지정하는 부정 프롬프트입니다. |
| `seed` |  | integer \| null | no |  |  | 생성을 재현 가능하게 하는 시드입니다. None으로 설정하면 무작위로 선택됩니다. |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | 프롬프트 확장 활성화 여부를 제어합니다. |
| `enable_safety_checker` |  | boolean | no | `true` |  | 안전 검사를 활성화할지 제어합니다. |
| `loras` |  | array of object {path, scale, transformer} | yes |  | ≤ 3 items | LTX-2.3용 LoRA 추가 항목입니다. 각각 3 GB로 제한되며 최대 3개까지 허용됩니다. 레지스트리의 SSRF 안전 다운로더가 각 경로를 가져온 후에만 로드됩니다. |
| `loras[].path` |  | string | yes |  | ≤ 2048 characters | 3 GB 이하의 LoRA .safetensors 파일에 대한 직접 HTTP(S) URL입니다. 로드 전에 SSRF 안전 다운로더로 가져옵니다. |
| `loras[].scale` |  | number | no | `1` | 0 – 2 | 0.0–2.0 범위에서 이 강도가 높을수록 생성 동영상에 대한 LoRA 영향이 커집니다. |
| `loras[].transformer` |  | enum (string) | no | `both` | `high`, `low`, `both` | Transformer 선택 플래그입니다. LTX-2.3은 transformer가 하나뿐이므로 거의 모든 사용자에게 'both'가 적합합니다. 여러 transformer를 사용하는 향후 아키텍처를 지원하기 위해 존재합니다. |

## 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-quality/text-to-audio/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-quality/text-to-audio/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-quality/text-to-audio/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-quality/text-to-audio/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-quality/text-to-audio/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": 700,
    "usd": "0.70",
    "charged": 700,
    "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": {
    "audio": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp3",
      "content_type": "audio/mpeg",
      "file_name": "0-output.mp3",
      "file_size": 1843200
    },
    "seed": 42,
    "prompt": "string"
  },
  "outputs": [
    {
      "type": "audio",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp3",
      "content_type": "audio/mpeg",
      "file_size": 1843200,
      "duration": 12,
      "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 |
|---|---|---|
| `audio` | object {url, content_type, file_name, file_size} | 모델이 생성한 오디오입니다. |
| `seed` | integer | 생성에 실제로 적용된 시드입니다. |
| `prompt` | string | 사용 전에 적용된 확장을 포함한 생성 프롬프트입니다. |

## Related models

- [Ltx 2.3 Quality · Text to Video](https://doitong.com/developers/models/ltx-2.3-quality/text-to-video.md) (`ltx-2.3-quality/text-to-video`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Text to Video · LoRA](https://doitong.com/developers/models/ltx-2.3-quality/text-to-video/lora.md) (`ltx-2.3-quality/text-to-video/lora`): $0.00271 / megapixel of output video
- [Ltx 2.3 Quality · Image to Video](https://doitong.com/developers/models/ltx-2.3-quality/image-to-video.md) (`ltx-2.3-quality/image-to-video`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Image to Video · LoRA](https://doitong.com/developers/models/ltx-2.3-quality/image-to-video/lora.md) (`ltx-2.3-quality/image-to-video/lora`): $0.00271 / megapixel of output video
- [Ltx 2.3 Quality · Ingredient](https://doitong.com/developers/models/ltx-2.3-quality/ingredient.md) (`ltx-2.3-quality/ingredient`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Clean Plate](https://doitong.com/developers/models/ltx-2.3-quality/clean-plate.md) (`ltx-2.3-quality/clean-plate`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Colorization](https://doitong.com/developers/models/ltx-2.3-quality/colorization.md) (`ltx-2.3-quality/colorization`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Cross Eyed](https://doitong.com/developers/models/ltx-2.3-quality/cross-eyed.md) (`ltx-2.3-quality/cross-eyed`): $0.00241 / megapixel of output video

## Links

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