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@@ -31,7 +31,9 @@ We introduce `MuseTalk`, a **real-time high quality** lip-syncing model (30fps+
## Model ## Model
![Model Structure](assets/figs/musetalk_arc.jpg) ![Model Structure](assets/figs/musetalk_arc.jpg)
MuseTalk was trained in latent spaces, where the images were encoded by a freezed VAE. The audio was encoded by a freezed `whisper-tiny` model. The architecture of the generation network was borrowed from the UNet of the `stable-diffusion-v1-4`, where the audio embeddings were fused to the image embeddings by cross-attention. MuseTalk was trained in latent spaces, where the images were encoded by a freezed VAE. The audio was encoded by a freezed `whisper-tiny` model. The architecture of the generation network was borrowed from the UNet of the `stable-diffusion-v1-4`, where the audio embeddings were fused to the image embeddings by cross-attention.
Note that although we use a very similar architecture as Stable Diffusion, MuseTalk is distinct in that it is `Not` a diffusion model. Instead, MuseTalk operates by inpainting in the latent space with `a single step`.
## Cases ## Cases
### MuseV + MuseTalk make human photos alive！ ### MuseV + MuseTalk make human photos alive！