Aliyun Liveportrait

Name: Aliyun Liveportrait
Author: cinience

cinience/alicloud-skills

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

Generate lightweight talking-head portrait videos with Alibaba Cloud Model Studio LivePortrait from a portrait image and speech audio.

About

Uses Model Studio LivePortrait to animate a detected portrait image with speech audio into a talking-head video. A developer uses it for longer or simpler presenter-style portrait animation.

Run liveportrait-detect first, then liveportrait to generate
China mainland (Beijing) only; inputs must be public URLs

Aliyun Liveportrait by the numbers

53 all-time installs (skills.sh)
Ranked #879 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-liveportrait

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Installs	53
repo stars	★ 396
Last updated	July 18, 2026
Repository	cinience/alicloud-skills ↗

What it does

Generate lightweight talking-head portrait videos with Alibaba Cloud Model Studio LivePortrait from a portrait image and speech audio.

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio LivePortrait

Validation

mkdir -p output/aliyun-liveportrait
python -m py_compile skills/ai/video/aliyun-liveportrait/scripts/prepare_liveportrait_request.py && echo "py_compile_ok" > output/aliyun-liveportrait/validate.txt

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

Output And Evidence

Save normalized request payloads, template choice, and task polling snapshots under output/aliyun-liveportrait/.
Record the exact portrait/audio URLs and motion-strength related parameters.

Use LivePortrait when the job is lightweight portrait animation with speech audio, especially for longer clips or simpler presenter-style motion.

Critical model names

Use these exact model strings:

liveportrait-detect
liveportrait

Selection guidance:

Run liveportrait-detect first to verify the portrait image.
Use liveportrait for the actual video generation task.

Prerequisites

China mainland (Beijing) only.
Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.
Input image and audio must be public HTTP/HTTPS URLs.

Normalized interface (video.liveportrait)

Detect Request

model (string, optional): default liveportrait-detect
image_url (string, required)

Generate Request

model (string, optional): default liveportrait
image_url (string, required)
audio_url (string, required)
template_id (string, optional): normal, calm, or active
eye_move_freq (number, optional): 0 to 1
video_fps (int, optional): 15 to 30
mouth_move_strength (number, optional): 0 to 1.5
paste_back (bool, optional)
head_move_strength (number, optional): 0 to 1

Response

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

Quick start

python skills/ai/video/aliyun-liveportrait/scripts/prepare_liveportrait_request.py \
  --image-url "https://example.com/portrait.png" \
  --audio-url "https://example.com/speech.mp3" \
  --template-id calm \
  --video-fps 24 \
  --paste-back

Operational guidance

Use a clear, front-facing portrait with low occlusion.
Keep the audio clean and voice-dominant.
paste_back=false outputs only the generated face region; keep it true for standard talking-head output.
LivePortrait is a better fit than EMO when you need longer, simpler presenter-style clips.

Output location

Default output: output/aliyun-liveportrait/request.json
Override base dir with OUTPUT_DIR.

References

references/sources.md

interface:
  display_name: "Alibaba Cloud AI Video LivePortrait"
  short_description: "Lightweight portrait video generation with LivePortrait"
  default_prompt: "Use $aliyun-liveportrait to complete this ai/video LivePortrait task on Alibaba Cloud."

#!/usr/bin/env python3
"""Prepare normalized requests for LivePortrait detect/generate flows."""

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("--template-id", choices=["normal", "calm", "active"])
    parser.add_argument("--eye-move-freq", type=float)
    parser.add_argument("--video-fps", type=int)
    parser.add_argument("--mouth-move-strength", type=float)
    parser.add_argument("--paste-back", action="store_true")
    parser.add_argument("--head-move-strength", type=float)
    parser.add_argument("--detect-only", action="store_true")
    parser.add_argument("--output", default="output/aliyun-liveportrait/request.json")
    args = parser.parse_args()

    if args.detect_only:
        payload = {
            "model": "liveportrait-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: dict[str, object] = {
            "model": "liveportrait",
            "input": {
                "image_url": args.image_url,
                "audio_url": args.audio_url,
            },
            "parameters": {},
        }
        if args.template_id:
            payload["parameters"]["template_id"] = args.template_id
        if args.eye_move_freq is not None:
            payload["parameters"]["eye_move_freq"] = args.eye_move_freq
        if args.video_fps is not None:
            payload["parameters"]["video_fps"] = args.video_fps
        if args.mouth_move_strength is not None:
            payload["parameters"]["mouth_move_strength"] = args.mouth_move_strength
        if args.paste_back:
            payload["parameters"]["paste_back"] = True
        if args.head_move_strength is not None:
            payload["parameters"]["head_move_strength"] = args.head_move_strength

    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()