1. Upload photo
2. Choose Model
3. Add Script
20 credits
Billing unit10 credits / 5s
Billing units2
Estimated length8s
Est. total20 credits
Uses real audio duration when available.
87 / 1000
Avg render time
7 min
Languages supported
50+
Creators onboarded
3,200+
Trusted by teams
StudioBlendAudioNovaCourseWaveMintlyVisionSpark
Overview
Audio‑conditioned latent diffusion model for lip sync, designed for high‑fidelity results and strong temporal consistency over time.
Highlights
- End‑to‑end audio‑conditioned latent diffusion.
- Temporal consistency enhancements with TREPA.
- Language‑agnostic lip sync.
- Optimized for 512×512 outputs.
Quick Specifications
Primary useHigh‑fidelity video‑to‑video lip sync
InputsSource video + target audio
OutputSynced video
Best strengthTemporal consistency on longer clips
Best for
Longer clipsConsistency‑critical work
Inputs & Outputs
Inputs
VideoAudio
Outputs
Video
Long‑form segment
Stable mouth motion across a longer scene.
Original
Synced
Capabilities
Diffusion‑based sync
- End‑to‑end audio‑conditioned latent diffusion.
- Strong temporal stability for longer sequences.
Language‑agnostic output
- Designed to generalize across languages.
- Robust to diverse speech patterns.
Use Cases
Podcast videos
Maintain sync over time.
Training lessons
Consistency across segments.
Series content
Keep identity stable.
Applications
Podcasts
Keep long‑form talk segments aligned.
Training content
Maintain consistency across sections.
Series videos
Stable identity over time.
Best Practices
- 1Use steady, well‑lit footage for the cleanest temporal consistency.
- 2Keep the face centered to minimize occlusion artifacts.
- 3Match audio cadence to the original pacing.
Frequently Asked Questions
How does it keep frames consistent?
It uses temporal representation alignment (TREPA) to stabilize results across frames.
Is it language‑specific?
No. LatentSync is designed to be language‑agnostic.
What resolution is it optimized for?
The model targets 512×512 output resolution.