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

> Wan 2.1 视频生成器，根据给定的首图和末图生成中间运动

创建从 `start_image_url` 运动到 `end_image_url` 的片段，并由 `prompt` 引导。分辨率为 480p 或 720p，画幅比例可为 auto、16:9、9:16 或 1:1。`num_frames` 为 81-100，`frames_per_second` 为 5-24。支持 `negative_prompt`、`seed`、引导和步数控制，以及通过 `loras` 提供 LoRA 权重。

- 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/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 |  |  | 指导视频生成的文本指令。 |
| `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` |  | 生成视频时应用的负面提示词。 |
| `start_image_url` | `image` | string (image URL) | yes |  |  | 通过 URL 提供起始图像；当图像宽高比不同时，需要围绕中心调整大小并裁剪以匹配所选宽高比。 |
| `end_image_url` | `end_image` | string (image URL) | yes |  |  | 通过 URL 提供结束图像；当图像宽高比不同时，需要围绕中心调整大小并裁剪以匹配所选宽高比。 |
| `num_frames` |  | integer \| null | no | `81` | 81 – 100 | 生成所用的帧数，范围为 81 至 100（含边界）。 |
| `frames_per_second` |  | integer \| null | no | `16` | 5 – 24 | 生成视频的帧率，单位为帧/秒，范围为 5 到 24。 |
| `seed` |  | integer \| null | no |  |  | 使生成结果可复现的种子。设置为 None 时会随机选择种子。 |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | 输出视频分辨率，可为 480p 或 720p。 |
| `num_inference_steps` |  | integer | no | `30` | 2 – 40 | 采样步数；步数越多，处理时间越长，质量越高。 |
| `guide_scale` |  | number | no | `5` | 1 – 10 | 无分类器引导强度；比例越高，越倾向于匹配提示词，但可能损失质量。 |
| `shift` |  | number | no | `5` | 1 – 10 | 生成视频时应用的偏移设置。 |
| `enable_safety_checker` |  | boolean | no | `false` |  | 安全检查开关，关闭此设置需要账户授权。没有该授权时，无论设置如何，每个请求都会经过检查；true 启用检查器。 |
| `enable_prompt_expansion` |  | boolean | no | `false` |  | 控制是否启用提示词扩展。 |
| `acceleration` |  | enum (string) | no | `regular` | `none`, `regular` | 加速设置，建议选择 'regular'。提高加速程度会牺牲质量以缩短生成时间。 |
| `aspect_ratio` |  | enum (string) | no | `auto` | `auto`, `16:9`, `9:16`, `1:1` | 生成视频的比例；设为 'auto' 时根据输入图像自动选择。 |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | 推理期间应用的 LoRA 权重。 |
| `loras[].path` |  | string | yes |  |  | LoRA 权重的位置，以 URL 或路径指定。 |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA 权重名称；仅当 Hugging Face 仓库包含多个 safetensors 文件时必填。仅当 `path` 标识此类仓库时使用此名称。 |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | LoRA 权重合并到基础模型前应用的乘数。 |

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} | 生成得到的视频文件。 |
| `seed` | integer | 生成期间应用的种子值。 |

## 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