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Aliyun Wan I2v

Community
cinience
aliyun-wan-i2v

Use when generating videos from images with DashScope Wan 2.7 image-to-video model (wan2.7-i2v). Use when implementing first-frame video generation, first+last frame interpolation, video continuation, or audio-driven video synthesis via the video-synthesis async API.

Overview

Publishercinience
Repositoryalicloud-skills
Skill namealiyun-wan-i2v
Stars
397
Forks
34
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cinience on GitHub. Read the source before you install it.

Installation

Install the Aliyun Wan I2v AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-i2v
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cinience/alicloud-skills.git /tmp/alicloud-skills
mkdir -p .claude/skills
cp -r /tmp/alicloud-skills/skills/ai/video/aliyun-wan-i2v .claude/skills/aliyun-wan-i2v
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aliyun Wan I2v in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aliyun Wan I2v on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aliyun Wan I2v is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wan 2.7 Image-to-Video

Validation

bash
mkdir -p output/aliyun-wan-i2v
python -m py_compile skills/ai/video/aliyun-wan-i2v/scripts/generate_i2v.py && echo "py_compile_ok" > output/aliyun-wan-i2v/validate.txt

Pass criteria: command exits 0 and output/aliyun-wan-i2v/validate.txt is generated.

Output And Evidence

  • Save task IDs, polling responses, and final video URLs to output/aliyun-wan-i2v/.
  • 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-i2v — supports first-frame, first+last frame, video continuation, and audio-driven generation

Capabilities

CapabilityDescriptionRequired media types
First-frame videoGenerate video from a single imagefirst_frame
First+last frameInterpolate video between two imagesfirst_frame + last_frame
Video continuationExtend an existing video clipfirst_clip
Audio-drivenDrive video with audio (lip-sync, rhythm)first_frame + driving_audio

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 video content
  • negative_prompt (string, optional) — up to 500 characters
  • media (array, required) — media objects with type and url fields:
    • type: first_frame | last_frame | driving_audio | first_clip
    • url: public URL (HTTP/HTTPS) or OSS temporary URL
  • resolution (string, optional) — 720P or 1080P (default: 1080P)
  • duration (integer, optional) — video length in seconds, range [2, 15] (default: 5)
  • 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

Images (first_frame, last_frame):

  • Formats: JPEG, JPG, PNG (no transparency), BMP, WEBP
  • Resolution: [240, 8000] pixels per side
  • Aspect ratio: 1:8 to 8:1
  • Max size: 20MB

Audio (driving_audio):

  • Formats: wav, mp3
  • Duration: 2-30s
  • Max size: 15MB
  • Auto-truncated to duration value if longer

Video (first_clip):

  • Formats: mp4, mov
  • Duration: 2-10s
  • Resolution: [240, 4096] pixels per side
  • Aspect ratio: 1:8 to 8:1
  • Max size: 100MB

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) — generated video URL
  • output.orig_prompt (string) — original prompt
  • output.actual_prompt (string) — rewritten prompt (if prompt_extend enabled)
  • 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_i2v_task(req: dict) -> str:
    """Create an image-to-video task and return task_id."""
    payload = {
        "model": "wan2.7-i2v",
        "input": {
            "prompt": req.get("prompt", ""),
            "media": req["media"],
        },
        "parameters": {
            "resolution": req.get("resolution", "1080P"),
            "duration": req.get("duration", 5),
            "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("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)

Media combination examples

python
# First-frame only
media = [{"type": "first_frame", "url": "https://example.com/image.jpg"}]

# First + last frame interpolation
media = [
    {"type": "first_frame", "url": "https://example.com/start.jpg"},
    {"type": "last_frame", "url": "https://example.com/end.jpg"},
]

# Audio-driven from first frame
media = [
    {"type": "first_frame", "url": "https://example.com/face.jpg"},
    {"type": "driving_audio", "url": "https://example.com/speech.mp3"},
]

# Video continuation
media = [{"type": "first_clip", "url": "https://example.com/clip.mp4"}]

Error handling

ErrorLikely causeAction
401/403Missing or invalid DASHSCOPE_API_KEYCheck env var or credentials file
400 InvalidParameterUnsupported resolution, bad duration, missing mediaValidate parameters
"does not support synchronous calls"Missing X-DashScope-Async: enable headerAdd required header
429Rate limit or quotaRetry with backoff

Output location

  • Default output: output/aliyun-wan-i2v/videos/
  • Override base dir with OUTPUT_DIR.

Anti-patterns

  • Do not use model names other than wan2.7-i2v.
  • Do not call this API synchronously — async header is required.
  • Do not pass duplicate media types (e.g., two first_frame entries).
  • Video URLs expire after 24 hours; download and persist immediately.
  • Do not use this API for video editing — use aliyun-wan-videoedit instead.

Workflow

  1. Confirm user intent: first-frame, first+last frame, video continuation, or audio-driven.
  2. Prepare media array with correct types and valid URLs.
  3. Create async task and poll for results.
  4. Download and save generated video before URL expiration.

References

  • See references/api_reference.md for full HTTP API details.
  • See references/sources.md for source links.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aliyun Wan I2v AI skill do?

Use when generating videos from images with DashScope Wan 2.7 image-to-video model (wan2.7-i2v). Use when implementing first-frame video generation, first+last frame interpolation, video continuation, or audio-driven video synthesis via the video-synthesis async API.

Why use Aliyun Wan I2v on TypingMind?

Because you install it once and use it with any model. Aliyun Wan I2v is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aliyun Wan I2v in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-i2v. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aliyun Wan I2v?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aliyun Wan I2v?

As many as you like. As long as a model supports skills, you can use Aliyun Wan I2v with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aliyun Wan I2v AI skill free?

Yes. It is published on GitHub by cinience under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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