Why creators choose LatentSync

LatentSync represents a shift in how lip sync works. Instead of frame-by-frame processing, it understands the entire video structure and generates mouth movements that stay consistent across temporal boundaries. That consistency matters when audio and video have complex relationships.

Latent diffusion advantage

Working in latent space rather than pixel space means LatentSync has a more abstract understanding of what makes a mouth movement credible. It's not trying to predict exact pixel colors; it's learning the underlying structure of how mouths move in response to sound.

Audio-visual correlation learning

LatentSync includes explicit SyncNet supervision to ensure the model learns real audio-visual relationships. It isn't just guessing; it's trained to maximize actual sync accuracy between audio and mouth movement.

Temporal consistency

Latent diffusion models naturally handle temporal relationships better than frame-by-frame approaches. Mouth movements stay consistent across frames because the model understands motion as a continuous structure, not a series of isolated predictions.

Broad source compatibility

LatentSync generalizes well across different types of video: live action, AI-generated content, animation, and mixed media. The latent-space approach isn't dependent on training data bias toward one format.

How to use LatentSync in Craze

LatentSync is a transform model: provide video and audio, and Craze handles the synchronization.

1. Upload your video and the audio track you want to sync. The audio can be a voiceover, a dub, or new dialogue.
2. Ask for LatentSync by name, or describe your sync goal and let Craze show compatible models with pricing before you choose.
3. Craze processes the video against the audio and returns the synced result.
4. Review in the editor. If adjustments are needed, regenerate the section or try a different lip sync model.

LatentSync works well on footage where the speaker's mouth is visible. Crowded scenes or footage where faces are small may need Craze's agent to break them into closer shots first.

The latent diffusion approach has a distinct advantage in temporal consistency. Because the model understands the entire temporal structure rather than predicting frame by frame, mouth movements don't flicker or lose coherence across frame boundaries. That's especially noticeable in long speeches or dialogue sequences where consistency matters most.

LatentSync pricing

LatentSync is priced by the length of your source video at $0.006 per second of source video. A 30-second clip costs less than a 2-minute clip. Pricing scales linearly with duration, and you can calculate exact costs on this page.

Video quality, resolution, and frame rate don't affect the price. Only the duration of your source video matters. Since LatentSync works in latent space, it delivers results without requiring the pixel-level processing overhead of frame-by-frame approaches.

LatentSync vs. other lip sync models

Sync Lipsync 3 and Sync Lipsync 2 Pro use frame-by-frame or global analysis on pixel space, while LatentSync works in latent space with diffusion models. Kling LipSync offers multilingual support with native Kling 3.0 integration. Choose LatentSync if you prefer the diffusion approach or need efficiency for batch processing.

Who LatentSync is for

Researchers and technical creators

LatentSync's paper and code are available publicly. If you're interested in the research behind modern lip sync, LatentSync is worth exploring on its own merits.

Efficient rendering workflows

Because LatentSync works in latent space rather than pixel space, it can be computationally efficient for batch processing or long-form content. If you're syncing many videos, the approach has advantages.

High-fidelity dialogue replacement

LatentSync's explicit audio-visual correlation learning makes it strong for precise dialogue replacement where sync accuracy is critical.

AI video content

LatentSync generalizes well to AI-generated video because it doesn't rely on live-action training biases. If you're mixing AI and real footage, LatentSync handles both consistently.

Batch processing and long-form content

Because LatentSync works in latent space, it can be computationally more efficient than pixel-space approaches. If you need to sync many videos or long-form content, the technical foundation of LatentSync makes it practical for high-volume production.

Technical and research applications

LatentSync's paper and code are publicly available. If you're experimenting with diffusion-based synthesis or want to understand the research behind modern lip sync, LatentSync offers a transparent, open-source approach.