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The vae in the context of latent diffusion isn't really a vae 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。 I mean that's kind of what a vae is to begin with
The encoder downsamples, or compresses, to a bottleneck layer, and the decoder upsamples, or decompresses, back to image space. This is the new 1.5 model with updated vae, but you can actually update the vae of all your previous diffusion ckpt models in a non destructive manner, for this check this post out (especially the update at the end to use 1 file for all models) edit 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 1,002 被浏览
A vae is a variational autoencoder
An autoencoder is a model (or part of a model) that is trained to produce its input as output 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. 模仿学习的思想很直观 (intuitive)。我们在前面所介绍的Model-free, Model-based强化学习方法都是 从零开始 (from scratch) 探索并学习一个使累计回报最大的策略 (policy) 。 Imitation Learning的想法是，借助人类给出的示范 (demonstration)，可以快速地达到这个目的。这个示范是多组trajectory轨迹数据 ， 每条轨迹包含. If it had run out of memory earlier in the workflow it might have also recommended the vae encode tiled node.
KL散度积分形式怎么变成期望形式？ 最近在看VAE的推导，由于数学基础薄弱，卡在了其中一步。 如图所示，KL散度的积分形式是怎么变成期望形式的： [图片] 我尝试从最基础的连续型随机变量的… 显示全部 关注者 6 被浏览 Is there any difference between the two or any functional benefit in a1111 of doing it one way or the other? SD原文3.1节中同时提供了VAE和VQ-VAE两种方案，VAE效果更好所以被大家一直沿用） 之所以效果这么好，主要还是因为diffusion model强大。 强大到用diffusion model去拟合的隐空间分布，能够逼近VAE或者VQ-VAE用encoder编码RGB图片得到的latent feature分布。
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