VQ-VAE[1]是 Google DeepMind 在 2017 年提出的一个类 VAE生成模型,相比普通的 VAE,它有两点不同: 隐空间是离散的,通过 VQ (Vector Quantization) 操作实现; 先验分布是学习出来的。 为什么要用离散的隐空间呢?首先,离散的表征更符合一些模态的自然属性,比如语言、语音,而图像也能用语言描述;其次,离散表征更...
Residual Vector Quantization(RVQ)是VQ-VAE的一个变体,在论文 2107.03312.pdf (arxiv.org)中使用,思想非常简单,我们考虑一个VQ-VAE:Encoder先从输入 x 得到 z_e ,再Quantization得到对应的 z 。但是有一个…
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 mus...
s ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQVAE) and ...
In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization techniques to yield improved performance on image generation and image understanding tasks. In the first stage, an image quantization model, calledVQGAN, encodes an...
AVQ‐VAE consists of encoder, embedding space, decoder and attention mechanism. The encoder is used to map the input image into multiple feature vectors. Vector quantisation in embedding space is used for discretisation and autoregressive modelling of feature vectors. A decoder is used to decode ...
A method is presented for computing the discrete Fourier transform (DFT) of data compressed using vector quantization (VQ). The VQ compressed data are not ... CJ Read,DM Chabries,RW Christiansen,... - IEEE 被引量: 19发表: 1989年 Online distortion simulation using generative machine learning...
For VQGAN, we use the pre-quantization layers and flatten them to obtain Lv=196 embeddings. For ConvNext, we flatten the last activation map to obtain Lv=49 embeddings. Adapter. The Adapter module performs a non-linear projection of the image embeddings into the LLM embedding space, ...
9. RQ-VAE RQ-Transformer 《Autoregressive image generation using residual quantization》 10. Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer 11. HQ-VAE《Locally Hierarchical Auto-Regressive Modeling for Image Generation》 12. Mage 《Mage: Masked generative encoder to unify ...
1. Vector Quantization的straight through是如何实现的参见encodec的代码,我们可以发现straight through是通过下面的代码实现的。 quantize = x + (quantize - x).detach() 逐步讲解每一个部分的作用: (quantize - x).detach():这个操作首先计算quantize和x的差值,然后调用.detach()方法来生成一个新的张量,这个张...