Aliyun Wan Digital Human

Name: Aliyun Wan Digital Human
Author: cinience

cinience/alicloud-skills

84 installs
396 repo stars
Updated July 18, 2026
cinience/alicloud-skills

Generates talking, singing, or presentation avatar videos from a single character image and audio using Alibaba Cloud Model Studio wan2.2-s2v.

About

This skill validates a character image then generates image-plus-audio driven speaking, singing, or presenting videos via Model Studio wan2.2-s2v. A developer uses it to make narrated avatar clips, singing portraits, or broadcast-style talking heads.

Two models: wan2.2-s2v-detect to validate, wan2.2-s2v to generate
Scenario options talk/sing/perform at 480P or 720P, Beijing region only

Aliyun Wan Digital Human by the numbers

84 all-time installs (skills.sh)
Ranked #811 of 1,335 Generative Media skills by installs in the Skillselion catalog
Data as of Aug 5, 2026 (Skillselion catalog sync)

npx skills add https://github.com/cinience/alicloud-skills --skill aliyun-wan-digital-human

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Installs	84
repo stars	★ 396
Last updated	July 18, 2026
Repository	cinience/alicloud-skills ↗

What it does

Generates talking, singing, or presentation avatar videos from a single character image and audio using Alibaba Cloud Model Studio wan2.2-s2v.

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio Digital Human

Validation

mkdir -p output/aliyun-wan-digital-human
python -m py_compile skills/ai/video/aliyun-wan-digital-human/scripts/prepare_digital_human_request.py && echo "py_compile_ok" > output/aliyun-wan-digital-human/validate.txt

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

Output And Evidence

Save normalized request payloads, chosen resolution, and task polling snapshots under output/aliyun-wan-digital-human/.
Record image/audio URLs and whether the input image passed detection.

Use this skill for image + audio driven speaking, singing, or presenting characters.

Critical model names

Use these exact model strings:

wan2.2-s2v-detect
wan2.2-s2v

Selection guidance:

Run wan2.2-s2v-detect first to validate the image.
Use wan2.2-s2v for the actual video generation job.

Prerequisites

China mainland (Beijing) only.
Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.
Input audio should contain clear speech or singing, and input image should depict a clear subject.

Normalized interface (video.digital_human)

Detect Request

model (string, optional): default wan2.2-s2v-detect
image_url (string, required)

Generate Request

model (string, optional): default wan2.2-s2v
image_url (string, required)
audio_url (string, required)
resolution (string, optional): 480P or 720P
scenario (string, optional): talk, sing, or perform

Response

task_id (string)
task_status (string)
video_url (string, when finished)

Quick start

python skills/ai/video/aliyun-wan-digital-human/scripts/prepare_digital_human_request.py \
  --image-url "https://example.com/anchor.png" \
  --audio-url "https://example.com/voice.mp3" \
  --resolution 720P \
  --scenario talk

Operational guidance

Use a portrait, half-body, or full-body image with a clear face and stable framing.
Match audio length to the desired output duration; the output follows the audio length up to the model limit.
Keep image and audio as public HTTP/HTTPS URLs.
If the image fails detection, do not proceed directly to video generation.

Output location

Default output: output/aliyun-wan-digital-human/request.json
Override base dir with OUTPUT_DIR.

References

references/sources.md

interface:
  display_name: "Alibaba Cloud AI Video Digital Human"
  short_description: "Talking or singing avatar videos from image + audio"
  default_prompt: "Use $aliyun-wan-digital-human to complete this ai/video digital-human task on Alibaba Cloud."

#!/usr/bin/env python3
"""Prepare a normalized request for Model Studio digital human generation."""

from __future__ import annotations

import argparse
import json
from pathlib import Path


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--image-url", required=True)
    parser.add_argument("--audio-url")
    parser.add_argument("--resolution", choices=["480P", "720P"])
    parser.add_argument("--scenario", choices=["talk", "sing", "perform"])
    parser.add_argument("--detect-only", action="store_true")
    parser.add_argument("--output", default="output/aliyun-wan-digital-human/request.json")
    args = parser.parse_args()

    if args.detect_only:
        payload = {
            "model": "wan2.2-s2v-detect",
            "input": {
                "image_url": args.image_url,
            },
        }
    else:
        if not args.audio_url:
            raise SystemExit("--audio-url is required unless --detect-only is set")
        payload = {
            "model": "wan2.2-s2v",
            "input": {
                "image_url": args.image_url,
                "audio_url": args.audio_url,
            },
            "parameters": {},
        }
        if args.resolution:
            payload["parameters"]["resolution"] = args.resolution
        if args.scenario:
            payload["parameters"]["scenario"] = args.scenario

    output = Path(args.output)
    output.parent.mkdir(parents=True, exist_ok=True)
    output.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps({"ok": True, "request_path": str(output)}, ensure_ascii=False))


if __name__ == "__main__":
    main()