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  • Stable Diffusion BASICS - A guide to VAE : r StableDiffusion
    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
  • Whats a VAE? : r StableDiffusion - Reddit
    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
  • VAE（变分自动编码器）优势在哪里？ - 知乎
    若只是为了提取中间隐层信息（作降维用），VAE（变分自动编码器）相比AE（自编码器），DAE（降噪自动编码…
  • 各种生成模型vae gan diffusion有什么独特之处？分别擅长在什么方面使用？ - 知乎
    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。
  • Explanation of vae-ft-mse-840000-ema : r StableDiffusion - Reddit
    This vae was trained longer, thats pretty much it MSE is just one of the losses used in training (Mean Squared Error) If you want to learn more you can look up "variational autoencoders" and find loads of information
  • VAE、GAN 这种生成模型和 transformer 有什么区别？
    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 168
  • [D] Is VAE still worth it? : r MachineLearning - Reddit
    The "VAE" in the context of latent diffusion isn't really a VAE It's more like a glorified downsample-upsample model 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
  • AE，VAE，VQVAE DVAE为什么是离散-连续-离散？ - 知乎
    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。
  • r StableDiffusion on Reddit: Comparison of different VAEs on different . . .
    A VAE renders the image, the last step after all the AI magic I think of them as final-step photoshop filters, because there are subtle differences in how they present the image vs other VAEs
  • [D] Current best practices for VAEs : r MachineLearning - Reddit
    The notion of SOTA best-practices is metric-dependent For likelihood maximization, use an M-sample K-importance-weighted IAF PixelVAE (because why not) Maybe even throw in a little bit of hybrid training if you're doing semi-supervised conditional density estimation (shameless plug) For disentangled representations, use TC-VAE (or beta-VAE, or infoGAN), depending on your choice of





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