May 31, 2023 · 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.

Nov 28, 2022 · 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

其次，我们深入理解下vae的原理：vae是一种无监督的生成模型，其理论基础是建立在高斯混合模型之上。 由VAE的模型结构，我们可以看到噪声编码 z 是由一个标准正态分布所产生的向量，我们对这个分布随机采样 m 个点，其中 m 服从多项式分布 P(x) 。

“vae的潜在空间能够捕捉到图像的主要特征，从而生成具有相似结构的全新图像。 下面是一些应用场景： 人物动作生成 ：在CVPR'24的一篇论文中，提出了一个框架，能够生成人物动作，精细到手部运动。

Mar 6, 2024 · I think anime folks use a different VAE. This has been kind of simplified and complicated at the same time by the fact that most community models have been baking the VAE model into the checkpoint, so you only really need to worry about what VAE to pick for unbaked models and those will usually be 6+ months old at this point.

Jan 16, 2023 · Basically, yes, that's exactly what it does. Eyes and hands in particular are drawn better when the VAE is present. You also have to make sure it is selected by the application you are using. On Automatic1111 WebUI there is a setting where you can select the VAE you want in the settings tabs,

以minist手写图像VAE示例为例进行说明，图像大小为28*28=784维度，经过隐藏层后输出为隐变量z（一个长度为n，n可以为4，8，16...的向量)，VAE假设z的每一个维度相互独立，其各自服从均值为 \mu_i,方差为 \sigma_i^2 的正态分布，示例代码如下（keras，这里学习的是 log\sigma^2,具体完整代码请参考引文）：

The "VAE" in the context of latent diffusion isn't really a VAE. It's more like a glorified downsample-upsample model. It's more like a glorified downsample-upsample model. If you look at the "latents" of a stable diffusion model, they're just downsampled images, rather than being some sort of high-level random variables.

Aug 18, 2023 · Tiled VAE performs tiling while encoding and decoding the latent image (that is, before and after generating the image). Tiled diffusion performs tiling while denoising the latent image (that is, while generating the image). ControlNet Tile can be used to steer tiled diffusion so it doesn't generate the same subject multiple times.

Dec 19, 2022 · For image generation, the VAE (Variational Autoencoder) is what turns the latents into a full image. After Stable Diffusion is done with the initial image generation steps, the result is a tiny data structure called a latent, the VAE takes that latent and transforms it into the 512X512 image that we see.