AI BriefingVQ-Diffusion: A Discrete Latent Space Model
Key point
This covers the technical features and implementation of VQ-Diffusion, a conditional latent diffusion model that operates in a discrete latent space.
Details
VQ-Diffusion is a conditional latent diffusion model jointly developed by the University of Science and Technology of China and Microsoft. Unlike existing continuous diffusion models, it performs noising and denoising processes in a Discrete latent space.
The core architecture and operating principles are as follows:
- Use of VQ-VAE: Images are encoded via VQ-VAE (or VQGAN) into discrete tokens contained in a fixed-size Codebook, reducing dimensionality.
- Forward Process: Data is transformed through a process in which latent tokens either remain unchanged, are resampled into a different vector, or are masked.
- Reverse Process: Using a CLIP text encoder and a decoder transformer, the model predicts the probability distribution of unmasked latent tokens under a given prompt condition.
This model can be conveniently used via Hugging Face's Diffusers library, and it demonstrates the potential of discrete diffusion models compared to autoregressive (AR) models.
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