WebVAEs have been traditionally hard to train at high resolutions and unstable when going deep with many layers. In addition, VAE samples are often more blurry and less crisp than … Web论文名字叫做 NVAE: A Deep Hierarchical Variational Autoencoder，顾名思义是做VAE的改进工作的，提出了一个叫NVAE的新模型。 说实话，笔者点进去的时候是不抱什么希望的，因为笔者也算是对VAE有一定的了解， …

Generating Diverse High-Fidelity Images with VQ-VAE-2

WebHierarchical VQ-VAE. Latent variables are split into L L layers. Each layer has a codebook consisting of Ki K i embedding vectors ei,j ∈RD e i, j ∈ R D i, j =1,2,…,Ki j = 1, … WebBased on the hierarchical VQ-VAE, we propose a two-stage model for multiple-solution inpainting. The first stage is known as diverse structure generator, where sampling from … gram shortened

扩散模型（Diffusion Model，DDPM，GLIDE，DALLE2，Stable ...

WebThe proposed model is inspired by the hierarchical vector quantized variational auto-encoder (VQ-VAE), whose hierarchical architecture isentangles structural and textural information. In addition, the vector quantization in VQVAE enables autoregressive modeling of the discrete distribution over the structural information. Web2 de mar. de 2024 · With VQ-VAE we compress high-resolution videos into a hierarchical set of multi-scale discrete latent variables. Compared to pixels, this compressed latent space has dramatically reduced dimensionality, allowing us to apply scalable autoregressive generative models to predict video. In contrast to previous work that has largely … Web9 de ago. de 2024 · We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, each layer in HR ... chinatown chicago attractions