VQ-Diffusion
Vector Quantized Diffusion (VQ-Diffusion) is a conditional latent diffusion model developed by the University of Science and Technology of China and Microsoft. Unlike most commonly studied diffusion models, VQ-Diffusion's noising and denoising processes operate on a quantized latent space, i.e., the latent space is composed of a discrete set of vectors. Discrete diffusion models are less explored than their continuous counterparts and offer an interesting point of comparison with autoregressive (AR) models.
Hugging Face model card
Hugging Face Spaces
Original Implementation
Paper
Demo
🧨 Diffusers lets you run VQ-Diffusion with just a few lines of code.
Vector Quantized Diffusion (VQ-Diffusion) is a conditional latent diffusion model developed by the University of Science and Technology of China and Microsoft. Unlike most commonly studied diffusion models, VQ-Diffusion's noising and denoising processes operate on a quantized latent space, i.e., the latent space is composed of a discrete set of vectors. Discrete diffusion models are less explored than their continuous counterparts and offer an interesting point of comparison with autoregressive (AR) models.
Hugging Face model card
Hugging Face Spaces
Original Implementation
Paper
Demo
🧨 Diffusers lets you run VQ-Diffusion with just a few lines of code.