# Ltx 2.3 Quality · Extend Video · LoRA API

> 向前或向後延長影片，最多套用 3 個自訂 LoRA

影片延長功能，同時接受必要的 `loras` 清單，最多包含 3 個自訂 LoRA，每個最大 3 GB。`prompt` 描述片段如何延續，`extend_direction` 選擇 `forward` 或 `backward`。`num_context_frames`（9 至 121）控制與來源的重疊，`num_frames`（9 至 481）控制片段大小，而 `end_image_url` 可設定結尾關鍵影格。

- Model id: `ltx-2.3-quality/extend-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/extend-video/lora`
- Quote: `POST https://api.doitong.ru/v1/quote/ltx-2.3-quality/extend-video/lora`
- Docs page: https://doitong.com/zh-TW/developers/models/ltx-2.3-quality/extend-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/extend-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 |  |  | 指定影片所需延續內容的提示詞。 |
| `video_url` | `video` | string (video URL) | yes |  |  | 指向要延長之影片的 URL。 |
| `extend_direction` |  | enum (string) | no | `forward` | `forward`, `backward` | 影片延長模式：'backward' 建立新的開頭，'forward' 則在影片結尾後延續。 |
| `num_frames` |  | integer | no | `121` | 9 – 481 | 延長影格數，受解析度乘以影格數的穩定生成預算限制：24 fps 下，480p 級約 20 秒、720p 級約 11 秒、1080p 級約 5 秒。超出預算的請求會返回 422；請降低 `num_frames` 或選擇較小解析度。數量包含與來源重疊的 `num_context_frames`，因此增加的時長約為 (`num_frames` - `num_context_frames`) / `frames_per_second` 秒。 |
| `num_context_frames` |  | integer | no | `25` | 9 – 121 | 要求用於延長上下文／重疊的來源影格數，受來源包含的影格數限制。數量會向下取整，以符合 LTX 的 8k+1 網格。 |
| `end_image_url` | `end_image` | string (image URL) \| null | no |  |  | 選用的關鍵影格圖像；向後延長時設定新的第一影格，向前延長時設定最後影格。 |
| `end_image_strength` |  | number | no | `1` | 0 – 1 | 選用的結束關鍵影格條件強度：降低可給模型更多自由，1.0 則強制精確匹配。 |
| `resolution` |  | object {width, height} \| enum (string) | no | `auto` | `auto`, `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | 生成影片尺寸，對齊至 32px 增量；例如 1920x1080 會變為 1888x1056，來源會調整至此尺寸以產生拼接輸出。較低解析度可支援較長的穩定延長，高解析度則較短。選擇 'auto' 會在 LTX 限制允許的範圍內保留來源尺寸和長寬比。 |
| `match_input_fps` |  | boolean | no | `true` |  | 啟用時使輸出影格率匹配來源，覆寫 `frames_per_second`。來源 FPS 會四捨五入為整數，並限制在支援範圍內。 |
| `frames_per_second` |  | number | no | `24` | 1 – 60 | 輸出影片影格率，以每秒影格數表示。 |
| `video_strength` |  | number | no | `1` | 0 – 1 | 來源上下文條件強度：降低可提供模型更多自由。設定為 1.0 時，重疊影格會鎖定至來源，使轉場無縫。 |
| `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` |  | 延長音訊切換：關閉時新增片段保持靜音，但保留來源影片的音訊軌；開啟時為延長部分生成音訊，並在接合處與來源音訊交叉淡化。 |
| `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`, `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/extend-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/extend-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/extend-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/extend-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/extend-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/extend-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/extend-video/lora
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/ltx-2.3-quality/extend-video/lora