第一阶段是Image Quantization,借助ViT,将256x256的图片编码成32x32的离散latent codes,codebook size是8192,这里为了提升训练效果,用到了logit-laplace loss, l2 loss, adversarial loss and perceptual loss等loss。第二阶段是Vector-quantized Image Modeling,用第一阶段的模型得到的32x32共1024个tokens,让Transformer ...
再学习 prior. 学习 codebook 的部分与 VQ-VAE 大同小异,不同之处在于:加了一个 Patch Discriminator 做对抗训练,以及把重构损失的 L2 loss 换成了 perceptual loss. 实验证明 VQ-VAE 的重构非常模糊,而 VQGAN 能保留很多细节。
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 ...
each of which encompasses an 8x8 patch of the input image. Using these tokens, we train a decoder-only Transformer to predict a sequence of image tokens autoregressively. This two-stage model, VIM, is able to perform unconditioned image generation by simply sampling token-by-token from ...
开通百度智能云千帆大模型平台服务自动获取1000000+免费tokens 立即体验 随着人工智能技术的飞速发展,文本到图像合成已成为一个备受瞩目的研究领域。在这一领域中,Vector Quantized Diffusion Model(VQDM)凭借其出色的性能和广泛的应用前景,成为了研究者们关注的焦点。本文将带您了解VQDM的原理、特点以及在实际应用中的优...
[2024.07.01] compute the prior loss only on the masked locations, instead of the entire tokens. Fidelity Enhancer for Vector Quantized Time Series Generator (FE-VQTSG) [3] (not published yet) It is a U-Net-based mapping model that transforms a synthetic time series generated by a VQ-base...
介绍了一个patch aggregation 的方式, 使用离散的image patches 来提升 global 语义表示。 为何要强调high-level的语义信息 之前使用MIM的方法大体可以分为三种 low-level 的image raw pixels 手工的featuers, 比如HOG features visual tokens 但是在language 里面用mask的方式进行训练的时候, masked words 都是high-le...