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? What is the innovation of this model? What does this model do? Please answer in 50 words or less There should be exactly 4 paragraphs separated by the markdown divider: ***.
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