ai-avatar-video

Erstelle KI-Avatar- und Talking-Head-Videos über die inference.sh CLI. Empfohlen: P-Video-Avatar (schnellste, günstigste, integrierte TTS). Auch: OmniHuman, Fabric, PixVerse. Audio: Inworld TTS-2 (100+ Sprachen, Emotionssteuerung für Charaktere), ElevenLabs, Kokoro. Fähigkeiten: audiogesteuerte Avatare, Text-zu-Avatar, Lipsync-Videos, Talking-Head-Generierung, virtuelle Präsentatoren, UGC-Inhalte. Verwenden für: KI-Präsentatoren, Erklärvideos, virtuelle Influencer, Synchronisation, Marketingvideos, UGC-Anzeigen, Gaming-Avatare,...

npx skills add https://github.com/qu-skills/skills --skill ai-avatar-video

Install the belt CLI skill: npx skills add belt-sh/cli

AI Avatar & Talking Head Videos

Create AI avatars and talking head videos via inference.sh CLI.

AI Avatar & Talking Head Videos

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

# Recommended: P-Video-Avatar (fastest, cheapest, built-in TTS)
belt app run pruna/p-video-avatar --input '{
  "image": "https://portrait.jpg",
  "voice_script": "Hello, welcome to our product demo!",
  "voice": "Zephyr (Female)"
}'

Available Models

Start with P-Video-Avatar — it's 18x faster and 6x cheaper than alternatives, with built-in TTS, dynamic backgrounds, and 1080p support.

ModelApp IDBest ForBuilt-in TTS
P-Video-Avatarpruna/p-video-avatarBest overall: speed, cost, quality, controlYes (30 voices, 10 languages)
OmniHuman 1.5bytedance/omnihuman-1-5Multi-character, audio-drivenNo
Fabric 1.0falai/fabric-1-0Image talks with lipsyncYes
PixVerse Lipsyncfalai/pixverse-lipsyncHighly realistic lipsyncNo

Cost & Speed Comparison

ModelSpeed (per sec of video)Cost per second
P-Video-Avatar~1.83s/s$0.025
OmniHuman 1.5~28s/s (15x slower)$0.16 (6.4x more)
Fabric 1.0~34s/s (18x slower)$0.14 (5.6x more)

Examples

P-Video-Avatar (Recommended)

Generate avatar from portrait + text script with built-in TTS:

belt app run pruna/p-video-avatar --input '{
  "image": "https://portrait.jpg",
  "voice_script": "Welcome to our product walkthrough. Today I will show you three key features.",
  "voice": "Puck (Male)",
  "voice_language": "English (US)",
  "resolution": "720p"
}'

With custom style control:

belt app run pruna/p-video-avatar --input '{
  "image": "https://portrait.jpg",
  "voice_script": "This is exciting news!",
  "voice": "Aoede (Female)",
  "voice_prompt": "Enthusiastic and energetic tone",
  "video_prompt": "The person is presenting on stage with dramatic lighting",
  "resolution": "1080p"
}'

With audio file instead of TTS:

belt app run pruna/p-video-avatar --input '{
  "image": "https://portrait.jpg",
  "audio": "https://speech.mp3"
}'

Full Workflow: Generate Portrait + Avatar

Use Pruna P-Image to generate the portrait, then create the avatar:

# 1. Generate a portrait image
belt app run pruna/p-image --input '{
  "prompt": "professional headshot portrait of a young woman, neutral background, looking at camera, studio lighting, photorealistic",
  "aspect_ratio": "9:16"
}'

# 2. Create avatar video with built-in TTS
belt app run pruna/p-video-avatar --input '{
  "image": "<image-url-from-step-1>",
  "voice_script": "Hi there! Let me walk you through our latest features.",
  "voice": "Zephyr (Female)"
}'

OmniHuman 1.5 (Multi-Character)

belt app run bytedance/omnihuman-1-5 --input '{
  "image_url": "https://portrait.jpg",
  "audio_url": "https://speech.mp3"
}'

Supports specifying which character to drive in multi-person images.

Fabric 1.0 (Image Talks)

belt app run falai/fabric-1-0 --input '{
  "image_url": "https://face.jpg",
  "audio_url": "https://audio.mp3"
}'

PixVerse Lipsync

belt app run falai/pixverse-lipsync --input '{
  "image_url": "https://portrait.jpg",
  "audio_url": "https://speech.mp3"
}'

Full Workflow: TTS + Avatar (Non-TTS Models)

For models without built-in TTS (OmniHuman, PixVerse), generate speech first:

# 1. Generate speech — Inworld TTS-2 for expressive character voices
belt app run inworld/text-to-speech-2 --input '{
  "text": "[friendly] Welcome to our product demo! [excited] Let me show you three features that will change how you work.",
  "voice_id": "Sarah",
  "delivery_mode": "CREATIVE"
}' > speech.json

# 2. Create avatar video with the speech
belt app run bytedance/omnihuman-1-5 --input '{
  "image_url": "https://presenter-photo.jpg",
  "audio_url": "<audio-url-from-step-1>"
}'

Tip: For most use cases, P-Video-Avatar with built-in TTS is simpler — no separate audio step needed. Use this workflow only when you specifically need OmniHuman (multi-character) or PixVerse (realistic lipsync).

Full Workflow: Dub Video in Another Language

# 1. Transcribe original video
belt app run infsh/fast-whisper-large-v3 --input '{"audio_url": "https://video.mp4"}' > transcript.json

# 2. Translate text (manually or with an LLM)

# 3. Generate speech in new language
belt app run infsh/kokoro-tts --input '{"text": "<translated-text>"}' > new_speech.json

# 4. Lipsync the original video with new audio
belt app run infsh/latentsync-1-6 --input '{
  "video_url": "https://original-video.mp4",
  "audio_url": "<new-audio-url>"
}'

Avatar UGC Generation

Create UGC-style content with P-Video-Avatar — built-in TTS, no separate audio step needed:

# 1. Generate a relatable UGC-style portrait
belt app run pruna/p-image --input '{
  "prompt": "casual selfie-style photo of a young woman in a cozy room, natural lighting, looking at camera, warm smile, authentic feel",
  "aspect_ratio": "9:16"
}'

# 2. Create UGC avatar video with built-in TTS
belt app run pruna/p-video-avatar --input '{
  "image": "<image-url-from-step-1>",
  "voice_script": "Okay so I just tried this product and honestly? It is a game changer. I was not expecting to love it this much but here we are!",
  "voice": "Zephyr (Female)",
  "voice_prompt": "Excited, casual, authentic tone like talking to a friend",
  "video_prompt": "The person is talking casually to camera in their room, natural gestures",
  "resolution": "1080p"
}'

Why P-Video-Avatar for UGC

  • All-in-one — built-in TTS means no separate audio generation step
  • 30 voices, 10 languages — match your target audience
  • Voice + video prompts — control tone, emotion, body language, and background independently
  • 18x faster, 6x cheaper — produce UGC at scale vs. Fabric/OmniHuman/HeyGen
  • 1080p support — platform-ready vertical video from a single portrait image

Batch UGC: Same Product, Multiple Presenters

# Generate 3 different presenters
for voice in "Zephyr (Female)" "Puck (Male)" "Aoede (Female)"; do
  belt app run pruna/p-video-avatar --input "{
    \"image\": \"https://portrait.jpg\",
    \"voice_script\": \"This changed my morning routine completely. Five minutes and I am done.\",
    \"voice\": \"$voice\",
    \"voice_prompt\": \"Casual, authentic, like a real testimonial\",
    \"video_prompt\": \"Person talking to camera in a bright kitchen\",
    \"resolution\": \"1080p\"
  }"
done

Use Cases

  • UGC & Marketing: Product demos, UGC-style ads with AI presenters
  • Education: Course videos, explainers
  • Localization: Dub content across 10 languages from one image
  • Social Media: Consistent virtual influencer content
  • Corporate: Training videos, announcements
  • Gaming: Character avatars, NPC dialogue

Tips

  • Use high-quality portrait photos (front-facing, good lighting)
  • Audio should be clear with minimal background noise
  • P-Video-Avatar supports built-in TTS — no need for a separate speech generation step
  • P-Video-Avatar output aspect ratio matches the input image
  • Generate portraits with pruna/p-image using 9:16 aspect ratio for vertical videos
  • OmniHuman 1.5 supports multiple people in one image
  • LatentSync is best for syncing existing videos to new audio

Related Skills

# Dedicated P-Video-Avatar skill
npx skills add inference-sh/skills@p-video-avatar

# Full platform skill (all apps)
npx skills add inference-sh/skills@infsh-cli

# Text-to-speech (generate audio for non-TTS avatar models)
npx skills add inference-sh/skills@text-to-speech

# Speech-to-text (transcribe for dubbing)
npx skills add inference-sh/skills@speech-to-text

# Video generation
npx skills add inference-sh/skills@ai-video-generation

# Image generation (create avatar images)
npx skills add inference-sh/skills@ai-image-generation

Browse all video apps: belt app store --category video

Documentation

Mehr Skills von qu-skills

ai-video-generation
qu-skills
Erstelle KI-Videos mit Google Veo, Seedance 2.0, HappyHorse, Wan, Grok und 40+ Modellen über die inference.sh CLI. Modelle: Veo 3.1, Veo 3, Seedance 2.0, HappyHorse 1.0, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Fähigkeiten: Text-zu-Video, Bild-zu-Video, Referenz-zu-Video, Videobearbeitung, Lippen-Sync, Avatar-Animation, Video-Upscaling, Foley-Sound. Verwendung für: Social-Media-Videos, Marketinginhalte, Erklärvideos, Produktdemos, KI-Avatare. Auslöser: Videoerstellung, KI-Video,...
videocreativemedia
remotion-render
qu-skills
Videos aus React/Remotion-Komponentencode über inference.sh rendern. TSX-Code übergeben, MP4 erhalten. Unterstützt alle Remotion-APIs: useCurrentFrame, useVideoConfig, spring, interpolate, AbsoluteFill, Sequence. Konfigurierbare Auflösung, FPS, Dauer, Codec. Verwendung für: programmatische Videogenerierung, animierte Grafiken, Bewegungsdesign, datengesteuerte Videos, React-Animationen zu Video. Auslöser: remotion, Video aus Code rendern, tsx zu Video, React-Video, programmatisches Video, Remotion-Render, Code zu Video, animiert...
developmentvideocreative
ai-image-generation
qu-skills
Generieren Sie KI-Bilder mit GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve und über 50 Modellen über die inference.sh CLI. Modelle: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Fähigkeiten: Text-zu-Bild, Bild-zu-Bild, Inpainting, LoRA, Bildbearbeitung, Hochskalierung, Textrendering. Verwendung für: KI-Kunst, Produkt-Mockups, Konzeptkunst, Social-Media-Grafiken, Marketing-Visuals, Illustrationen. Auslöser: flux, Bildgenerierung, KI-Bild, Text zu...
creativemediaimage
twitter-automation
qu-skills
Automatisiere Twitter/X mit Posting, Engagement und Benutzerverwaltung über die inference.sh CLI. Apps: x/post-tweet, x/post-create (mit Medien), x/post-like, x/post-retweet, x/dm-send, x/user-follow. Fähigkeiten: Tweets posten, Inhalte planen, Beiträge liken, retweeten, DMs senden, Benutzern folgen, Profile abrufen. Verwendung für: Social-Media-Automatisierung, Inhaltsplanung, Engagement-Bots, Zielgruppenwachstum, X API. Auslöser: twitter api, x api, tweet automation, post to twitter, twitter bot, social media automation, x...
api
agent-browser
qu-skills
Browser-Automatisierung für KI-Agenten über inference.sh. Navigieren Sie auf Webseiten, interagieren Sie mit Elementen über @e-Referenzen, machen Sie Screenshots, nehmen Sie Video auf. Fähigkeiten: Web Scraping, Formularausfüllen, Klicken, Tippen, Drag & Drop, Datei-Upload, JavaScript-Ausführung. Verwendung für: Web-Automatisierung, Datenextraktion, Testen, Agenten-Browsing, Recherche. Auslöser: Browser, Web-Automatisierung, Scrapen, Navigieren, Klicken, Formular ausfüllen, Screenshot, Web durchsuchen, Playwright, Headless-Browser, Web-Agent, Internet surfen, Video aufnehmen
browser-automationweb-scrapingtesting
web-search
qu-skills
Websuche und Inhaltsextraktion mit Tavily und Exa über die inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Fähigkeiten: KI-gestützte Suche, Inhaltsextraktion, direkte Antworten, Recherche. Verwendung für: Recherche, RAG-Pipelines, Faktenprüfung, Inhaltsaggregation, Agenten. Auslöser: Websuche, tavily, exa, search api, Inhaltsextraktion, Recherche, Internetsuche, KI-Suche, Suchassistent, Web Scraping, rag, Perplexity-Alternative
researchweb-scrapingapi
agent-tools
qu-skills
Führe 250+ KI-Apps über die inference.sh CLI aus – Bildgenerierung, Videocrstellung, LLMs, Suche, 3D, Twitter-Automatisierung. Modelle: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter und viele mehr. Verwenden beim Ausführen von KI-Apps, Generieren von Bildern/Videos, Aufrufen von LLMs, Websuche oder Automatisieren von Twitter. Auslöser: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok
developmentapicreative
python-executor
qu-skills
We need to translate the given English text into German, preserving the name "python-executor" only if it appears in the source text. The source text does not contain "python-executor" explicitly; it only appears in the instruction as the name to preserve, but not in the <text> block. So we should not include it. The translation should be accurate, keep technical terms, URLs, numbers, and product names. The text describes executing Python code in a sandboxed environment via inference.sh, lists pre-installed libraries, use cases, and triggers. Translate naturally into German. Key points: "safe sandboxed environment" -> "sicheren Sandkasten-Umgebung" or "sicheren isolierten Umgebung"? "Sandboxed" is often "Sandkasten" or "isoliert". "Pre-installed" -> "Vorinstalliert". "100+ more libraries" -> "über 100 weitere Bibliotheken". "Use for" -> "Verwendung für". "Triggers" -> "Auslöser" or "Trigger
developmentdata-analysisweb-scraping