aliyun-liveportrait
Use when generating lightweight talking-head portrait videos with Alibaba Cloud Model Studio LivePortrait (`liveportrait`) from a detected portrait image and speech audio. Use when you need long-form or simple broadcast-style portrait animation beyond the typical short expressive models.
What this skill does
Category: provider # Model Studio LivePortrait ## Validation ```bash 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 ```bash 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`
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