# Ltx 2.3 Quality · Reference Video to Video · LoRA API

> 如同基礎版本的參考影片生成，但會套用最多 3 個自訂 LoRA

根據必要的 `prompt` 與用於提供動態和結構的參考 `video_url` 生成影片，並另外需要 `loras`，最多 3 個 LoRA 檔案，每個最大 3 GB。可選的首幀、中間與末幀關鍵影格圖片（`image_url`、`mid_image_url`、`end_image_url`）會固定外觀。`video_strength` 與 `strength` 範圍為 0 至 1，`num_frames` 範圍為 9 至 481。

- Model id: `ltx-2.3-quality/reference-video-to-video/lora`
- Made by: Lightricks
- Category: Video to Video
- Output: video
- Price per megapixel of your output video: $0.00271 / megapixel of output video (cheapest run $0.003 (3 API credits))
- Run: `POST https://api.doitong.ru/v1/run/ltx-2.3-quality/reference-video-to-video/lora`
- Quote: `POST https://api.doitong.ru/v1/quote/ltx-2.3-quality/reference-video-to-video/lora`
- Docs page: https://doitong.com/zh-TW/developers/models/ltx-2.3-quality/reference-video-to-video/lora

## Pricing

Price: **$0.00271 / 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 $0.304 = 304 API credits (112 MP output video × $0.002708).

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/reference-video-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

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | 用於引導生成的提示詞。 |
| `video_url` | `video` | string (video URL) | yes |  |  | 指向用於引導動態與結構之參考影片的 URL。 |
| `control_video_url` |  | string (video URL) \| null | no |  |  | 選用的預先計算控制影片；與 `skip_control_preprocess` 一起提供時，會取代內建控制估算。重新取樣以符合輸出尺寸與影格數後，會成為直接控制訊號。例如，影片可以包含已算好的深度、邊緣或姿勢合成結果。 |
| `skip_control_preprocess` |  | boolean | no | `false` |  | 使用 `control_video_url` 的直接控制模式，不在內部估算深度、邊緣或姿勢；未提供該 URL 時會忽略此設定。此模式的輸出尺寸遵循明確指定的尺寸並以 64 對齊，以保持 RGB 與控制影片一致；`auto` 則在 LTX 限制內採用來源尺寸與長寬比。 |
| `preserve_original_video` |  | boolean | no | `false` |  | 原始影片保留模式，僅適用於 `skip_control_preprocess`、`video_url` 與 `control_video_url`。預設停用時會維持標準控制處理；啟用後會從 `video_url` 開始生成，同時保留 `control_video_url` 的結構引導。透過 `strength` 設定去噪程度，以控制保留量；較低的值會保留更多原始像素。 |
| `image_url` | `image` | string (image URL) \| null | no |  |  | 作為第一個影格關鍵影格的選用圖像錨定風格或角色身分。 |
| `mid_image_url` |  | string (image URL) \| null | no |  |  | 片段中點附近的選用圖像錨定外觀，用於補充初始的 `image_url` 關鍵影格。可協助外觀跟隨鏡頭移動而非逐漸偏移；需要 `image_url`。 |
| `end_image_url` | `end_image` | string (image URL) \| null | no |  |  | 片段最終影格的選用圖像錨定外觀。當鏡頭移動結束於不同於起始圖像的位置時很有用；需要 `image_url`。 |
| `video_strength` |  | number | no | `0.6` | 0 – 1 | 此條件強度控制可從參考影片改變動態與結構的自由度：較小的值允許更大的變化。 |
| `strength` |  | number | no | `1` | 0 – 1 | 參考影片轉影片取樣的去噪控制，其效果取決於 `preserve_original_video`。模式關閉時只會縮短去噪排程；啟用時則控制來源像素的轉換，1.0 會全部重新生成，較低的值會保留更多內容（例如 0.5 約保留 50%）。 |
| `num_frames` |  | integer | no | `121` | 9 – 481 | 輸出影格數，受以解析度乘以影格數計算的 IC-LoRA 穩定性預算限制。422 回應表示超出預算：請降低解析度或 `num_frames`。此預算約允許 720p 等級尺寸下的 150 個影格，較小尺寸則可更多。 |
| `resolution` |  | object {width, height} \| enum (string) | no | `auto` | `auto`, `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | 輸出影片尺寸，也會影響穩定時長：較小尺寸允許更多影格，較大尺寸則降低上限。內建控制估算會使尺寸接近官方控制流程；透過 `skip_control_preprocess` 與 `control_video_url` 的直接控制則遵循明確尺寸並以 64px 對齊。直接控制模式中，`auto` 會在 LTX 限制內沿用來源尺寸與長寬比。 |
| `frames_per_second` |  | number | no | `24` | 1 – 60 | 輸出影片影格率，以每秒影格數表示。 |
| `num_inference_steps` |  | integer | no | `15` | 8 – 30 | 設定要執行的推論步驟數，最多 30；維持不變時使用 15。 |
| `guidance_scale` |  | number | no | `1` | 1 – 20 | 使用此尺度調整無分類器引導；預設調校同時以高品質和快速生成為目標。 |
| `generate_audio` |  | boolean | no | `true` |  | 返回 MP4 的音訊軌切換：關閉會產生僅含影片的檔案，開啟則包含音訊。 |
| `negative_prompt` |  | string | no | `color distortion, overexposure, static, blurry details, subtitles, style, artwork, painting, frame, still, dim overall tone, worst quality, low quality, JPEG compression artifacts, ugly, mutilated, extra fingers, poorly drawn hands, poorly drawn face, deformed, disfigured, malformed limbs, fused fingers, motionless frame, cluttered background, three legs, crowded background, walking backwards` |  | 指定生成應避免內容的負面提示詞。 |
| `seed` |  | integer \| null | no |  |  | 使生成結果可重現的種子。設為 None 會隨機選擇種子。 |
| `enable_prompt_expansion` |  | boolean | no | `true` |  | 控制是否啟用提示詞擴展。 |
| `enable_safety_checker` |  | boolean | no | `true` |  | 控制是否啟用安全性檢查。 |
| `video_quality` |  | enum (string) | no | `high` | `low`, `medium`, `high`, `maximum` | 套用至輸出影片的品質預設。 |
| `video_write_mode` |  | enum (string) | no | `balanced` | `fast`, `balanced`, `small` | 結果影片的寫入模式。 |
| `loras` |  | array of object {path, scale, transformer} | yes |  | ≤ 3 items | LTX-2.3 的 LoRA 附加項，每個上限為 3 GB，最多允許 3 個項目。只有在 registry 的 SSRF 安全下載器擷取各路徑後才會載入。 |
| `loras[].path` |  | string | yes |  | ≤ 2048 characters | 不超過 3 GB 的 LoRA .safetensors 檔案直接 HTTP(S) URL。載入前會使用 SSRF 安全下載器擷取。 |
| `loras[].scale` |  | number | no | `1` | 0 – 2 | 在 0.0–2.0 範圍內，此強度越高，LoRA 對生成影片的影響越大。 |
| `loras[].transformer` |  | enum (string) | no | `both` | `high`, `low`, `both` | Transformer 選擇旗標：使用 LTX-2.3 的幾乎所有情況都適合 'both'，因為模型只有一個 transformer。此旗標用於支援未來包含多個 transformer 的架構。 |

File parameters (`video`, `control_video_url`, `image`, `mid_image_url`, `end_image`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## 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",
  "video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
```

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",
  "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
```

## 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/reference-video-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",
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
  }'

# 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/reference-video-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",
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
  }),
});
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",
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
}

# 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/reference-video-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-quality/reference-video-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",
        "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
      },
      "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/reference-video-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": 304,
    "usd": "0.304",
    "charged": 304,
    "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",
    "video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
  },
  "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
    },
    "seed": 42,
    "prompt": "string"
  },
  "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} | 模型產生的影片。 |
| `seed` | integer | 生成期間實際套用的種子。 |
| `prompt` | string | 生成提示詞，包括使用前套用的任何擴寫內容。 |

## 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/reference-video-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-quality/reference-video-to-video/lora
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/ltx-2.3-quality/reference-video-to-video/lora