# Wan-2.1 First-Last-Frame-to-Video API

> Wan 2.1 影片生成器，在指定的第一張圖片與最後一張圖片之間製作動畫

建立從 `start_image_url` 移動至 `end_image_url` 的片段，並由 `prompt` 引導。解析度為 480p 或 720p，長寬比可為 auto、16:9、9:16 或 1:1。`num_frames` 為 81-100，`frames_per_second` 為 5-24。支援 `negative_prompt`、`seed`、引導與步數控制，以及透過 `loras` 指定 LoRA 權重。

- Model id: `wan-flf2v`
- Made by: Alibaba
- Category: Image to Video
- Output: video
- Price: from $0.20 / video (cheapest run $0.20 (200 API credits))
- Run: `POST https://api.doitong.com/v1/run/wan-flf2v`
- Quote: `POST https://api.doitong.com/v1/quote/wan-flf2v`
- Docs page: https://doitong.com/zh-TW/developers/models/wan-flf2v

## Pricing

Price: **from $0.20 / video** (the lowest tier; the quote prices your exact input). Billed once per generated video.

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

- When `num_frames` > 81 and `resolution` = 720p: $0.50 (500 API credits) / video
- When `resolution` = 720p: $0.40 (400 API credits) / video
- When `num_frames` > 81: $0.25 (250 API credits) / video

The full example input below costs $0.40 = 400 API credits (1 video × 400 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/wan-flf2v` 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 |  |  | 引導影片生成的文字指示。 |
| `negative_prompt` |  | string | no | `bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards` |  | 生成影片時套用的負面提示詞。 |
| `start_image_url` | `image` | string (image URL) | yes |  |  | 透過 URL 提供起始圖像；若圖像長寬比不同，會以中心為基準調整尺寸並裁切，以符合選定的長寬比。 |
| `end_image_url` | `end_image` | string (image URL) | yes |  |  | 透過 URL 提供結束圖像；若圖像長寬比不同，會以中心為基準調整尺寸並裁切，以符合選定的長寬比。 |
| `num_frames` |  | integer \| null | no | `81` | 81 – 100 | 生成的影格數量，範圍為 81 至 100，包含邊界值。 |
| `frames_per_second` |  | integer \| null | no | `16` | 5 – 24 | 生成影片的影格速率，單位為 影格每秒，範圍為 5 到 24。 |
| `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。 |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | 輸出影片解析度，可以是 480p 或 720p。 |
| `num_inference_steps` |  | integer | no | `30` | 2 – 40 | 取樣步數；步數越多，處理時間越長但品質越好。 |
| `guide_scale` |  | number | no | `5` | 1 – 10 | 無分類器引導強度；比例越高，越貼近提示詞，但可能造成品質損失。 |
| `shift` |  | number | no | `5` | 1 – 10 | 生成影片時套用的偏移設定。 |
| `enable_safety_checker` |  | boolean | no | `false` |  | 安全檢查開關；關閉設定需要帳戶授權。沒有該授權時，每個請求都會經過檢查，不受設定影響；true 會啟用檢查器。 |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | 控制是否啟用提示詞擴展。 |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | 加速設定，建議選擇 'regular'。提高加速程度會犧牲品質，以縮短生成時間。 |
| `aspect_ratio` |  | enum (string) | no | `auto` | `auto`, `16:9`, `9:16`, `1:1` | 生成影片的比例；設為 'auto' 時會根據輸入圖像自動選擇。 |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | 推論期間套用的 LoRA 權重。 |
| `loras[].path` |  | string | yes |  |  | LoRA 權重的位置，以 URL 或路徑指定。 |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA 權重名稱；僅當 Hugging Face 儲存庫包含超過 1 個 safetensors 檔案時為必填。此名稱僅在 `path` 指定這類儲存庫時使用。 |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | 合併至基礎模型前套用至 LoRA 權重的倍數。 |

File parameters (`image`, `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
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
  "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "end_image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

A fuller example:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
  "start_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
  "end_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

## 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/wan-flf2v?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "start_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "end_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

# 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/wan-flf2v?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "start_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "end_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }),
});
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 = {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "start_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "end_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/wan-flf2v", 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": "wan-flf2v",
      "input": {
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
        "start_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
        "end_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
      },
      "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": "wan-flf2v",
  "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": 400,
    "usd": "0.40",
    "charged": 400,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "start_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
    "end_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "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
  },
  "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.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} | 生成產生的影片檔案。 |
| `seed` | integer | 生成期間套用的種子值。 |

## Related models

- [Wan-2.1 Pro Text-to-Video](https://doitong.com/developers/models/wan-pro/text-to-video.md) (`wan-pro/text-to-video`): $0.80 / run
- [Wan-2.1 Text-to-Video](https://doitong.com/developers/models/wan-t2v.md) (`wan-t2v`): from $0.20 / video
- [Wan Effects](https://doitong.com/developers/models/wan-effects.md) (`wan-effects`): $0.35 / video
- [Wan-2.1 Image-to-Video](https://doitong.com/developers/models/wan-i2v.md) (`wan-i2v`): from $0.20 / video
- [Wan-2.1 Pro Image-to-Video](https://doitong.com/developers/models/wan-pro/image-to-video.md) (`wan-pro/image-to-video`): $0.80 / video
- [Wan 3.0 Image to Video](https://doitong.com/developers/models/alibaba/wan-3.0/image-to-video.md) (`alibaba/wan-3.0/image-to-video`): from $0.05 / second
- [FLUX 3 Max Image to Video](https://doitong.com/developers/models/blackforestlabs/flux-3-max/image-to-video.md) (`blackforestlabs/flux-3-max/image-to-video`): from $0.0425 / second of output video
- [Grok Imagine Video 1.5 Image to Video](https://doitong.com/developers/models/xai/grok-imagine-video/v1.5/image-to-video.md) (`xai/grok-imagine-video/v1.5/image-to-video`): from $0.082 / second

## Links

- HTML page: https://doitong.com/developers/models/wan-flf2v
- 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=wan-flf2v
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/wan-flf2v