--- id: cinience/alicloud-skills/aliyun-wan-videoedit version: "e15a5780" license: MIT install: manual updated: 2026-07-18 --- # aliyun-wan-videoedit — This skill harnesses Alibaba's Wan 2.7 video editing model to transform video appearance through style transfer (clay, anime, etc.) or content-aware edits guided by text prompts and optional reference images. It handles async task creation, polling, and media validation across multiple aspect ratios and resolutions. Publisher: cinience · Stars: 397 · Updated: 2026-07-18 Install (manual): `git clone https://github.com/cinience/alicloud-skills` ## SKILL.md # Wan 2.7 Video Editing ## Validation ```bash mkdir -p output/aliyun-wan-videoedit python -m py_compile skills/ai/video/aliyun-wan-videoedit/scripts/edit_video.py && echo "py_compile_ok" > output/aliyun-wan-videoedit/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-wan-videoedit/validate.txt` is generated. ## Output And Evidence - Save task IDs, polling responses, and final video URLs to `output/aliyun-wan-videoedit/`. - Keep at least one end-to-end run log for troubleshooting. ## Prerequisites - Install SDK (recommended in a venv): ```bash python3 -m venv .venv . .venv/bin/activate python -m pip install dashscope ``` - Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`. ## Critical model names - `wan2.7-videoedit` — supports style transfer and instruction-based video editing ## Capabilities | Capability | Description | Required media | |---|---|---| | Style transfer | Convert video to a different visual style (clay, anime, etc.) | `video` only | | Instruction editing | Edit video content with text instructions and optional reference images | `video` + optional `reference_image` (up to 3) | ## API endpoint (async only) ``` POST https://dashscope.aliyuncs.com/api/v1/services/aigc/video-generation/video-synthesis ``` Required headers: - `Authorization: Bearer $DASHSCOPE_API_KEY` - `Content-Type: application/json` - `X-DashScope-Async: enable` Singapore endpoint: replace `dashscope.aliyuncs.com` with `dashscope-intl.aliyuncs.com`. ## Normalized interface ### Request - `prompt` (string, optional) — up to 5000 characters, describes desired editing - `negative_prompt` (string, optional) — up to 500 characters - `media` (array, required) — media objects with `type` and `url` fields: - `type`: `video` (required, exactly 1) | `reference_image` (optional, up to 3) - `url`: public URL (HTTP/HTTPS) or OSS temporary URL - `resolution` (string, optional) — `720P` or `1080P` (default: `1080P`) - `ratio` (string, optional) — output aspect ratio: `16:9`, `9:16`, `1:1`, `4:3`, `3:4`. If omitted, follows input video ratio. - `duration` (integer, optional) — truncate input video to this length in seconds, range [2, 10]. Default `0` (use input video duration). - `audio_setting` (string, optional) — `auto` (default, AI decides) or `origin` (keep original audio) - `prompt_extend` (boolean, optional) — AI prompt rewriting (default: true) - `watermark` (boolean, optional) — add "AI generated" watermark (default: false) - `seed` (integer, optional) — range [0, 2147483647] ### Media input limits **Video** (type=video): - Formats: mp4, mov - Duration: 2-10s - Resolution: [240, 4096] pixels per side - Aspect ratio: 1:8 to 8:1 - Max size: 100MB **Reference images** (type=reference_image): - Formats: JPEG, JPG, PNG (no transparency), BMP, WEBP - Resolution: [240, 8000] pixels per side - Aspect ratio: 1:8 to 8:1 - Max size: 20MB - Maximum 3 reference images ### Resolution output table | Resolution | Ratio | Output (W*H) | |---|---|---| | 720P | 16:9 | 1280*720 | | 720P | 9:16 | 720*1280 | | 720P | 1:1 | 960*960 | | 720P | 4:3 | 1104*832 | | 720P | 3:4 | 832*1104 | | 1080P | 16:9 | 1920*1080 | | 1080P | 9:16 | 1080*1920 | | 1080P | 1:1 | 1440*1440 | | 1080P | 4:3 | 1648*1248 | | 1080P | 3:4 | 1248*1648 | ### Response (task creation) - `output.task_id` (string) — use for polling, valid 24 hours - `output.task_status` (string) — PENDING | RUNNING | SUCCEEDED | FAILED | CANCELED - `request_id` (string) ### Response (task result) - `output.video_url` (string) — edited video URL - `usage.video_count` (integer) - `usage.video_duration` (integer) — duration in seconds ## Quick start (Python + HTTP) ```python import os import json import time import requests API_KEY = os.getenv("DASHSCOPE_API_KEY") BASE_URL = "https://dashscope.aliyuncs.com/api/v1" def create_videoedit_task(req: dict) -> str: """Create a video editing task and return task_id.""" payload = { "model": "wan2.7-videoedit", "input": { "prompt": req.get("prompt", ""), "media": req["media"], }, "parameters": { "resolution": req.get("resolution", "1080P"), "prompt_extend": req.get("prompt_extend", True), "watermark": req.get("watermark", False), }, } if req.get("negative_prompt"): payload["input"]["negative_prompt"] = req["negative_prompt"] if req.get("ratio"): payload["parameters"]["ratio"] = req["ratio"] if req.get("duration"): payload["parameters"]["duration"] = req["duration"] if req.get("audio_setting"): payload["parameters"]["audio_setting"] = req["audio_setting"] if req.get("seed") is not None: payload["parameters"]["seed"] = req["seed"] resp = requests.post( f"{BASE_URL}/services/aigc/video-generation/video-synthesis", headers={ "Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json", "X-DashScope-Async": "enable", }, json=payload, ) resp.raise_for_status() data = resp.json() return data["output"]["task_id"] def poll_task(task_id: str, interval: int = 15) -> dict: """Poll until task completes. Returns final response.""" while True: resp = requests.get( f"{BASE_URL}/tasks/{task_id}", headers={"Authorization": f"Bearer {API_KEY}"}, ) resp.raise_for_status() data = resp.json() status = data["output"]["task_status"] if status in ("SUCCEEDED", "FAILED", "CANCELED"): return data time.sleep(interval) ``` ## Usage examples ```python # Style transfer — convert to clay style media = [{"type": "video", "url": "https://example.com/input.mp4"}] task_id = create_videoedit_task({ "prompt": "将整个画面转换为黏土风格", "media": media, "resolution": "720P", }) # Instruction editing with reference image media = [ {"type": "video", "url": "https://example.com/input.mp4"}, {"type": "reference_image", "url": "https://example.com/hat.jpg"}, ] task_id = create_videoedit_task({ "prompt": "为人物换上酷闪的衣服，再戴参考图里的帽子", "media": media, "audio_setting": "origin", }) ``` ## Error handling | Error | Likely cause | Action | |---|---|---| | 401/403 | Missing or invalid `DASHSCOPE_API_KEY` | Check env var or credentials file | | 400 `InvalidParameter` | Bad resolution, missing video, too many reference images | Validate parameters | | "does not support synchronous calls" | Missing `X-DashScope-Async: enable` header | Add required header | | 429 | Rate limit or quota | Retry with backoff | ## Output location - Default output: `output/aliyun-wan-videoedit/videos/` - Override base dir with `OUTPUT_DIR`. ## Anti-patterns - Do not use model names other than `wan2.7-videoedit`. - Do not call this API synchronously — async header is required. - Do not pass more than 1 video or more than 3 reference images. - Video URLs expire after 24 hours; download and persist immediately. - Do not use this API for video generation — use `aliyun-wan-i2v` instead. ## Workflow 1) Confirm user intent: style transfer or instruction-based editing. 2) Prepare media array with video (required) and optional reference images. 3) Create async task and poll for results. 4) Download and save edited video before URL expiration. ## References - See `references/api_reference.md` for full HTTP API details. - See `references/sources.md` for source links. 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