But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at 256x256 resolution, we achieve Inception Score (IS) of 175.1 and Fr'echet Inception Distance (FID) of 4.17, a dramatic improvement over ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a package. It uses exponential moving averages to update the dictionary. VQ has been successfully used by Deepmind and OpenAI for high quality generation of images (VQ-VAE-2) and music (Jukebox).Semantic image synthesis enables control over unconditional image generation by allowing guidance on what is being generated. We conditionally synthesize the latent space from a vector quantized model (VQ-model) pre-trained to autoencode images. Instead of training an autoregressive Transformer on separately learned conditioning latents and ...Overview of the proposed ViT-VQGAN (left) and VIM (right), which, when working together, is capable of both image generation and image understanding…Vector-Quantized Image Modeling with ViT-VQGAN One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end. VQGAN is an improved version of this that introduces an ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...此篇 ViT-VQGAN 為 VQ-GAN 的改良版本，沒看過的人可以看 The AI Epiphany 介紹的 VQ-GAN 和 VQ-VAE，這種類型的方法主要是要得到一個好的 quantizer，而 VQ-VAE 是透過 CNN-based 的 auto-encoder 把 latent space 變成類似像 dictionary 的 codebook (discrete…We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning.Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.Posted by Jiahui Yu, Senior Research Scientist, and Jing Yu Koh, Research Software Engineer, Google Research In recent years, natural language processing models have dramatically improved their ability to learn general-purpose representations, which has resulted in significant performance gains for a wide range of natural language generation and natural language understanding tasks. In large ...Oct 9, 2021 · The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at 256x256 resolution, we achieve Inception Score (IS) of 175.1 and Fr'echet Inception Distance (FID) of 4.17, a dramatic improvement over ... Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at 256 × 256 resolution, we achieve Inception Score (IS) of 175.1 and Fréchet Inception Distance (FID) of 4.17, a dramatic improvement ...此篇 ViT-VQGAN 為 VQ-GAN 的改良版本，沒看過的人可以看 The AI Epiphany 介紹的 VQ-GAN 和 VQ-VAE，這種類型的方法主要是要得到一個好的 quantizer，而 VQ-VAE 是透過 CNN-based 的 auto-encoder 把 latent space 變成類似像 dictionary 的 codebook (discrete…Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). Vector-quantized Image Modeling with Improved VQGAN Yu, Jiahui ; Li, Xin ; Koh, Jing Yu ; Zhang, Han ; Pang, Ruoming ; Qin, James ; Ku, Alexander ; Xu, Yuanzhong But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Vector-quantized Image Modeling with Improved VQGAN. Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks.But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...arXiv.org e-Print archiveBut while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...DALL-E 2 - Pytorch. Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch.. Yannic Kilcher summary | AssemblyAI explainer. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP.Our experiments show that causal decoder-only models trained on an autoregressive language modeling objective exhibit the strongest zero-shot generalization after purely unsupervised pretraining. However, models with non-causal visibility on their input trained with a masked language modeling objective followed by multitask finetuning perform ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at 256x256 resolution, we achieve Inception Score (IS) of 175.1 and Fr'echet Inception Distance (FID) of 4.17, a dramatic improvement over ...Abstract and Figures. Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Vision transformers (ViTs) have gained popularity recently. Even without customized image operators such as convolutions, ViTs can yield competitive performance when properly trained on massive data. However, the computational overhead of ViTs remains prohibitive, due to stacking multi-head self-attention modules and else. Compared to the vast literature and prevailing success in compressing ...and Yonghui Wu. Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627, 2021.3 [10]Chuanxia Zheng, Long Tung Vuong, Jianfei Cai, and Dinh Phung. Movq: Modulating quantized vectors for high-fidelity image generation.arXiv preprint arXiv:2209.09002, 2022.3Posted by Jiahui Yu, Senior Research Scientist, and Jing Yu Koh, Research Software Engineer, Google Research In recent years, natural language processing models have dramatically improved their ability to learn general-purpose representations, which has resulted in significant performance gains for a wide range of natural language generation and natural language understanding tasks. In large ...Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from ...Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The...Sep 19, 2022 · Vector-quantized Image Modeling with Improved VQGAN. 2 code implementations • ICLR 2022 Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a package. It uses exponential moving averages to update the dictionary. VQ has been successfully used by Deepmind and OpenAI for high quality generation of images (VQ-VAE-2) and music (Jukebox).But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Vector-Quantized Image Modeling with ViT-VQGAN One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...The Vector-Quantized (VQ) codebook is first introduced in VQVAE , which aims to learn discrete priors to encode images. The following work VQGAN proposes a perceptual codebook by further using perceptual loss and adversarial training objectives . We briefly describe the VQGAN model with its codebook in this section, and more details can be ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...The concept is build upon two stages. The first stage learns in an autoencoder-like fashion by encoding images into a low-dimensional latent space, then applying vector quantization by making use of a codebook. Afterwards, the quantized latent vectors are projected back to the original image space by using a decoder.But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Described as “a bunch of Python that can take words and make pictures based on trained data sets," VQGANs (Vector Quantized Generative Adversarial Networks) pit neural networks against one another to synthesize “plausible” images. Much coverage has been on the unsettling applications of GANs, but they also have benign uses. Hands-on access through a simplified front-end helps us develop ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...此篇 ViT-VQGAN 為 VQ-GAN 的改良版本，沒看過的人可以看 The AI Epiphany 介紹的 VQ-GAN 和 VQ-VAE，這種類型的方法主要是要得到一個好的 quantizer，而 VQ-VAE 是透過 CNN-based 的 auto-encoder 把 latent space 變成類似像 dictionary 的 codebook (discrete…Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from ...Vector-quantized image modeling with improved vqgan J Yu, X Li, JY Koh, H Zhang, R Pang, J Qin, A Ku, Y Xu, J Baldridge, Y Wu The Tenth International Conference on Learning Representations , 2021Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...We describe multiple improvements to the image quantizer and show that training a stronger image quantizer is a key component for improving both image generation and image understanding. Vector-Quantized Image Modeling with ViT-VQGAN One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational ...Abstract and Figures. Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar ...Autoregressive Image Generation using Residual Quantization ...Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.We propose Vector-quantized Image Modeling (VIM), which pretrains a Transformer to predict image tokens autoregressively, where discrete image tokens are produced from improved ViT-VQGAN image quantizers. With our proposed improvements on image quantization, we demonstrate superior results on both image generation and understanding.Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis. Vector -Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in ...Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.Vision transformers (ViTs) have gained popularity recently. Even without customized image operators such as convolutions, ViTs can yield competitive performance when properly trained on massive data. However, the computational overhead of ViTs remains prohibitive, due to stacking multi-head self-attention modules and else. Compared to the vast literature and prevailing success in compressing ...Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.“Vector-Quantized Image Modeling with Improved VQGAN” proposes a two-stage model that reinvents classic image quantization methods to produce better picture generation and image understanding tasks. The first step is to encode an image into discrete latent codes of lesser dimensions using an image quantization model called VQGAN.Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The...A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a package. It uses exponential moving averages to update the dictionary. VQ has been successfully used by Deepmind and OpenAI for high quality generation of images (VQ-VAE-2) and music (Jukebox). Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including ...This strategy can naturally tap into the rich body of prior work on large language models, which have seen continued advances in capabilities and performance through scaling data and model sizes. Our approach is simple: First, Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens.Vector-Quantized Image Modeling with ViT-VQGAN. One recent, commonly used model that quantizes images into integer tokens is the Vector-quantized Variational AutoEncoder (VQVAE), a CNN-based auto-encoder whose latent space is a matrix of discrete learnable variables, trained end-to-end.Vector-quantized Image Modeling with Improved VQGAN Yu, Jiahui ; Li, Xin ; Koh, Jing Yu ; Zhang, Han ; Pang, Ruoming ; Qin, James ; Ku, Alexander ; Xu, Yuanzhong But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Vision transformers (ViTs) have gained popularity recently. Even without customized image operators such as convolutions, ViTs can yield competitive performance when properly trained on massive data. However, the computational overhead of ViTs remains prohibitive, due to stacking multi-head self-attention modules and else. Compared to the vast literature and prevailing success in compressing ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Described as “a bunch of Python that can take words and make pictures based on trained data sets," VQGANs (Vector Quantized Generative Adversarial Networks) pit neural networks against one another to synthesize “plausible” images. Much coverage has been on the unsettling applications of GANs, but they also have benign uses. Hands-on access through a simplified front-end helps us develop ...Vector-quantized Image Modeling with Improved VQGAN Yu, Jiahui ; Li, Xin ; Koh, Jing Yu ; Zhang, Han ; Pang, Ruoming ; Qin, James ; Ku, Alexander ; Xu, YuanzhongThe Vector-Quantized (VQ) codebook is first introduced in VQVAE , which aims to learn discrete priors to encode images. The following work VQGAN proposes a perceptual codebook by further using perceptual loss and adversarial training objectives . We briefly describe the VQGAN model with its codebook in this section, and more details can be ...Vector-quantized image modeling with improved VQGAN, Oct 2021. This paper uses a 2-stage approach. This first stage uses a Vision transformer-based VQGAN for discrete codebook learning. The second state is an autoregressive transformer whose input is represented by stage 1 encoding.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dustin Brunner. Do Deep Generative Models Know What They Don’t Know? by Rongxing Liu. May 31st: Vector-quantized Image Modeling with Improved VQGAN by TBD; Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality by Dion Hopkinson-Sibley. Kinney
openreview.net あくまで個人的なメモVQGANの改善とベクトル量子化を使った画像生成モデル・画像分類モデルの改善。VQVAEはCNNベースのAE、VQGANはそこにadversarial lossを導入した。 これらはCNNのauto encoder(AE)の学習(ステージ1)とencodeしたlatent variablesの密度をCNN(or Transformer)で学習する(ステージ2)という2つ ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Vector-Quantized Image Modeling with Improved VQGAN may 17, 2022 ... Image-Text Pre-training with Contrastive Captioners ... Vector-Quantized Image Modeling with ...Image encoders compress an image into smaller dimensions, sometimes even quantized into a discrete space (such as the VQGAN from taming-transformers used in Craiyon). In this article, we try to reproduce the results from ViT-VQGAN (" Vector-quantized Image Modeling with Improved VQGAN ") and experiment with further adaptations.DALL-E 2 - Pytorch. Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch.. Yannic Kilcher summary | AssemblyAI explainer. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP.Current image-to-image translation methods formulate the task with conditional generation models, leading to learning only the recolorization or regional changes as being constrained by the rich structural information provided by the conditional contexts. In this work, we propose introducing the vector quantization technique into the image-to-image translation framework. The vector quantized ...The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including ...But while such models have achieved strong performance for image generation, few studies have evaluated the learned representation for downstream discriminative tasks (such as image classification). In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization ...Vector-Quantized Image Modeling with Improved VQGAN maj 17, 2022 ... Image-Text Pre-training with Contrastive Captioners ... Vector-Quantized Image Modeling with ...The release weight of ViT-VQGAN small which is trained on ImageNet at here; 16/08. First release weight of ViT-VQGAN base which is trained on ImageNet at here; Add an colab notebook at here; About The Project. This is an unofficial implementation of both ViT-VQGAN and RQ-VAE in Pytorch. ViT-VQGAN is a simple ViT-based Vector Quantized ....