atlascloud/infinitetalk

ljud-till-video

InfiniteTalk turns a reference portrait and audio into a realistic talking-head video with lip-sync, supporting up to 10-minute audio in 480p or 720p.

Inmatning

Laddar parameterkonfiguration...

Utmatning

Vilande

Dina genererade videor visas här

Konfigurera parametrar och klicka på Kör för att börja generera

Varje körning kostar $0.03. För $10 kan du köra cirka 333 gånger.

Du kan fortsätta med:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parametrar

Kodexempel
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "atlascloud/infinitetalk",
    "prompt": "A beautiful sunset over the ocean with gentle waves",
    "width": 512,
    "height": 512,
    "duration": 3,
    "fps": 24,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Installera

Installera det nödvändiga paketet för ditt programmeringsspråk.

bash

pip install requests

Autentisering

Alla API-förfrågningar kräver autentisering via en API key. Du kan hämta din API key från Atlas Cloud-instrumentpanelen.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Håll din API key säker

Exponera aldrig din API key i klientkod eller publika arkiv. Använd miljövariabler eller en backend-proxy istället.

Skicka en förfrågan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Skicka en förfrågan

Skicka en asynkron genereringsförfrågan. API:et returnerar ett prediction ID som du kan använda för att kontrollera statusen och hämta resultatet.

POST/api/v1/model/generateVideo

Förfrågningsinnehåll

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "atlascloud/infinitetalk",
    "input": {
        "prompt": "A beautiful sunset over the ocean with gentle waves"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Svar

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Kontrollera status

Polla prediction-endpointen för att kontrollera den aktuella statusen för din förfrågan.

GET/api/v1/model/prediction/{prediction_id}

Polling-exempel

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Statusvärden

processingFörfrågan bearbetas fortfarande.

completedGenereringen är klar. Utdata är tillgängliga.

succeededGenereringen lyckades. Utdata är tillgängliga.

failedGenereringen misslyckades. Kontrollera error-fältet.

Slutfört svar

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Ladda upp filer

Ladda upp filer till Atlas Cloud-lagring och få en URL som du kan använda i dina API-förfrågningar. Använd multipart/form-data för uppladdning.

POST/api/v1/model/uploadMedia

Uppladdningsexempel

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Svar

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

Följande parametrar accepteras i förfrågningsinnehållet.

Totalt: 0Obligatorisk: 0Valfri: 0

Inga parametrar tillgängliga.

Exempel på förfrågningsinnehåll

json

{
  "model": "atlascloud/infinitetalk"
}

Output Schema

API:et returnerar ett prediction-svar med de genererade utdata-URL:erna.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for video generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Exempelsvar

json

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrerar 300+ AI-modeller direkt i din AI-kodassistent. Ett kommando för att installera, sedan använd naturligt språk för att generera bilder, videor och chatta med LLM.

Stödda klienter

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ stödda klienter

Installera

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Konfigurera API Key

Hämta din API key från Atlas Cloud-instrumentpanelen och ställ in den som en miljövariabel.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Funktioner

När det är installerat kan du använda naturligt språk i din AI-assistent för att komma åt alla Atlas Cloud-modeller.

BildgenereringGenerera bilder med modeller som Nano Banana 2, Z-Image och fler.

VideoskapandeSkapa videor från text eller bilder med Kling, Vidu, Veo m.fl.

LLM-chattChatta med Qwen, DeepSeek och andra stora språkmodeller.

MediauppladdningLadda upp lokala filer för bildredigering och bild-till-video-arbetsflöden.

Läs mer

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server ansluter din IDE med 300+ AI-modeller via Model Context Protocol. Fungerar med alla MCP-kompatibla klienter.

Stödda klienter

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ stödda klienter

Installera

bash

npx -y atlascloud-mcp

Konfiguration

Lägg till följande konfiguration i din IDE:s MCP-inställningsfil.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Tillgängliga verktyg

atlas_generate_imageGenerera bilder från textpromptar.

atlas_generate_videoSkapa videor från text eller bilder.

atlas_chatChatta med stora språkmodeller.

atlas_list_modelsBläddra bland 300+ tillgängliga AI-modeller.

atlas_quick_generateInnehållsskapande i ett steg med automatiskt modellval.

atlas_upload_mediaLadda upp lokala filer för API-arbetsflöden.

Läs mer

github.com/AtlasCloudAI/mcp-server

API Schema

Schema ej tillgängligt

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InfiniteTalk: Audio-Driven Talking Video Generation

1. Introduction

InfiniteTalk is an audio-driven video generation model developed by AtlasCloud that transforms a single portrait image into a realistic talking-head video synchronized to any speech audio input. Built on a modified Wan2.1 I2V-14B diffusion transformer backbone with a dedicated audio cross-attention module, InfiniteTalk achieves phoneme-level lip synchronization while preserving the subject's identity, hairstyle, clothing, and background throughout the entire video.

InfiniteTalk's core innovation lies in its triple cross-attention architecture: each transformer block processes visual self-attention, text prompt cross-attention, and frame-level audio cross-attention in sequence, enabling precise per-frame audio-visual alignment. Combined with a streaming inference pipeline that processes video in overlapping segments, InfiniteTalk supports continuous video generation of up to 10 minutes from a single request — far exceeding the typical 5–15 second limit of conventional image-to-video models. The model also supports dual-person mode, animating two speakers simultaneously within the same frame using separate audio tracks and bounding box annotations.

2. Key Features & Innovations

Triple Cross-Attention Audio Conditioning: Unlike text-only conditioned video models, InfiniteTalk injects audio embeddings at every transformer block via a dedicated cross-attention layer. Audio features are extracted frame-by-frame using a Wav2Vec2 encoder, providing per-frame speech signal anchoring that drives natural mouth movements, facial micro-expressions, and head motion synchronized to the audio input.

Streaming Long-Form Video Generation: InfiniteTalk's streaming mode processes audio in overlapping clip segments with configurable motion frame overlap, automatically concatenating segments into seamless long-form video. This enables generation of minutes-long talking videos without quality degradation or identity drift — a capability not available in standard image-to-video pipelines limited to single-shot outputs.

High-Fidelity Identity Preservation: The model maintains consistent facial identity, hairstyle, clothing texture, and background composition across the entire generated video. The audio conditioning signal provides strong per-frame constraints that prevent the identity drift commonly observed in long unconditional video generation.

Dual-Person Conversation Mode: InfiniteTalk supports animating two speakers in a single scene by accepting separate audio tracks and bounding box coordinates for each person. This enables realistic conversation scenarios, interview formats, and dialogue-driven content without requiring separate generation passes or post-production compositing.

Flexible Input Modalities: The model accepts either a static portrait image or a reference video as the visual source, combined with audio in WAV or MP3 format. Text prompts provide additional guidance for expression style, posture, and behavioral nuance, giving creators fine-grained control over the generated output.

Conditional VSR Upscaling: When generating at 720p resolution with audio duration under 60 seconds, InfiniteTalk automatically routes output through a FlashVSR super-resolution pipeline, delivering enhanced visual clarity without additional user configuration or cost management.

3. Model Architecture & Technical Details

InfiniteTalk is built on the Wan2.1 I2V-14B foundation model (14 billion parameters, 480p native resolution) with custom InfiniteTalk adapter weights that introduce the audio cross-attention pathway. The audio encoder uses a Chinese-Wav2Vec2-Base model that extracts frame-aligned speech embeddings at 25 fps video rate, creating a one-to-one correspondence between audio features and generated video frames.

The inference pipeline operates in two modes. In clip mode, the model generates a single video segment of up to 81 frames (approximately 3.2 seconds at 25 fps), suitable for short-form content. In streaming mode, the model iteratively generates overlapping clips with a configurable motion frame overlap (default: 9 frames), seamlessly blending segments to produce arbitrarily long video bounded only by the input audio duration and a configurable maximum frame limit.

The diffusion process uses a configurable number of denoising steps (default: 40, tunable from 1–100) with TeaCache acceleration for improved throughput. On NVIDIA H200 hardware, each 81-frame clip requires approximately 3.5 minutes of processing time, yielding a generation-to-output ratio of roughly 10–30× depending on resolution and hardware load.

For 720p output, the system employs a two-stage pipeline: base generation at 480p followed by conditional FlashVSR 4× upscaling (target: 921,600 pixels at 25 fps), applied automatically when audio duration is 60 seconds or less.

4. Performance Highlights

InfiniteTalk addresses a specific niche — audio-driven talking-head video — that differs from general-purpose text-to-video or image-to-video models. Its performance should be evaluated primarily on lip-sync accuracy, identity consistency, and long-form stability rather than visual diversity or cinematic motion range.

Capability	InfiniteTalk	General I2V Models	Dedicated Lip-Sync Tools
Lip-sync accuracy	Phoneme-level, multi-language	N/A (no audio input)	Word-level, often English-only
Maximum duration	Up to 10 minutes (streaming)	5–15 seconds typical	30–60 seconds typical
Identity preservation	High (audio-anchored per-frame)	Moderate (drift in longer clips)	Moderate
Dual-person support	Native	Not available	Rare
Resolution	480p native, 720p with VSR	Up to 1080p	Varies
Audio input	Any language WAV/MP3	N/A	Usually English TTS

InfiniteTalk achieves strong lip-sync fidelity across Chinese, English, Japanese, and other languages tested, owing to the language-agnostic Wav2Vec2 audio feature extraction. Identity drift is minimal even in 5+ minute generations due to the per-frame audio conditioning anchor.

5. Intended Use & Applications

Digital Avatar & Virtual Presenter: Create realistic talking-head videos for virtual hosts, AI assistants, and digital spokespersons using a single photo and recorded or synthesized speech audio.

Video Dubbing & Localization: Generate lip-synced video from translated audio tracks, enabling cost-effective multilingual content adaptation without re-filming or manual lip-sync editing.

Online Education & Training: Produce instructor-led video content at scale from lecture audio recordings and a single instructor photograph, reducing video production costs for e-learning platforms.

Podcast & Interview Visualization: Transform audio-only podcast or interview recordings into engaging video content with realistic speaker animations, suitable for social media distribution.

Customer Service & Chatbot Video: Generate personalized video responses driven by TTS audio output, enabling human-like video communication in automated customer interaction flows.

Social Media Content at Scale: Rapidly produce talking-head content for influencer accounts, news summaries, or commentary formats using text-to-speech pipelines combined with InfiniteTalk video generation.