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为什么vae效果不好，但vae+diffusion效果就好了？ vae本身生成图像模糊，说明encoder、decoder以及中间的隐层表示没有学到本质的东西。 SD在训练时又把VAE冻结了，为什么在隐层用diffu… 显示全部 关注者 989 被浏览 VAE：使用变分推断 [1] 将标准正态分布变换到目标分布的概率图模型。 相较于GAN易于训练，但也有后验崩塌问题，同时由于ELBO只是目标分布对数似然的一个下界，因此VAE的生成结果通常会比较模糊。 A vae is a variational autoencoder

All you need to know about Variational AutoEncoder | BayesLabs blog

An autoencoder is a model (or part of a model) that is trained to produce its input as output If it had run out of memory earlier in the workflow it might have also recommended the vae encode tiled node. By giving the model less information to represent the data than the input contains, it's forced to learn about the input distribution and compress the information.

Trueyou seem to have some misconceptions when it comes to vae

The vae is what gets you from latent space to pixelated images and vice versa There's hence no such thing as no vae as you wouldn't have an image It hence would have used a default vae, in most cases that would be the one used for sd 1.5 A vae is hence also definitely not a network extension file

Still one of the best ways. VAE生成例子 MNIST是个手写数字数据集，相信大家耳熟能详，就用这个数据集来解释VAE，网上代码很多，tensorflow的官方教程也包含了一个，这里就不再详细展开。 VAE、GAN 这种生成模型和 transformer 有什么区别？ 想问问为什么在分子生成中大部分选择使用VAE、GAN这种生成模型，而不使用transformer进行生成，这两种直接有什么本质的区别吗？ transfo… 显示全部 关注者 171 3. Vector Quantized Variational Autoencoder (VQVAE) / Discrete VAE (DVAE)： - VQVAE是对传统VAE的改进，它引入了离散的潜在表示。 VQVAE使用了一种称为向量量化（Vector Quantization）的技术，将连续的潜在表示映射到一个离散的表示空间中。

最后说一下比较有意思的事： VAE、GAN、Flow (NICE)三种模型都是2013-2014年提出来的（VAE是13年放到arXiv上的，后来中了NIPS；GAN也同时中了NIPS，而NICE最早是14年的一个ICLR workshop）。 最后的发展情况是：GAN最火，VAE次之，Flow模型似乎总是要火不火。

Vae不止是在Diss曲目上有所建树，其它主题的rap歌曲也是获得了认可的。 2007年2月17日，Vae发布了《朝舞》，这首歌邀请到了乱感觉的队友大V李毅杰合作，而另一位合作嘉宾则是天王星的呆宝静。 能和呆宝静合作，也说明了Vae的rap水平必然是在平均水准之上的。