  Although two-stage Vector Quantized (VQ) generative models allow for
synthesizing high-fidelity and high-resolution images, their quantization
operator encodes similar patches within an image into the same index, resulting
in a repeated artifact for similar adjacent regions using existing decoder
architectures. To address this issue, we propose to incorporate the spatially
conditional normalization to modulate the quantized vectors so as to insert
spatially variant information to the embedded index maps, encouraging the
decoder to generate more photorealistic images. Moreover, we use multichannel
quantization to increase the recombination capability of the discrete codes
without increasing the cost of model and codebook. Additionally, to generate
discrete tokens at the second stage, we adopt a Masked Generative Image
Transformer (MaskGIT) to learn an underlying prior distribution in the
compressed latent space, which is much faster than the conventional
autoregressive model. Experiments on two benchmark datasets demonstrate that
our proposed modulated VQGAN is able to greatly improve the reconstructed image
quality as well as provide high-fidelity image generation.
