the abstract of the paper is: We present the vector quantized diffusion (VQ-Diffusion)  model for text-to-image generation. This method is based  on a vector quantized variational autoencoder (VQ-VAE)  whose latent space is modeled by a conditional variant  of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method  is well-suited for text-to-image generation tasks because  it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-andreplace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our  experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with  similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can  handle more complex scenes and improve the synthesized  image quality by a large margin. Finally, we show that the  image generation computation in our method can be made  highly efficient by reparameterization. With traditional AR  methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite  time consuming even for normal size images. The VQDiffusion allows us to achieve a better trade-off between  quality and speed. Our experiments indicate that the VQDiffusion model with the reparameterization is fifteen times  faster than traditional AR methods while achieving a better image quality. The code and models are available at  https://github.com/NAME_1/VQ-Diffusion. Based on the summary of this post, can you tell me what the code link for this model is?  I want the entire output in JSON format.
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