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In 2021, Robin Rombach and others from Ludwig Maximilian University of Munich proposed the Latent Diffusion Model (LDM), which uses a pre-trained VAE to compress images into a low-dimensional latent space (typically 1/8 of the original resolution) before performing diffusion denoising.
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9/1/2026
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VerifiedThe Latent Diffusion Model was proposed by Rombach et al. in 2022, combining the diffusion process with the latent space of a VAE, reducing the computational dimensions from 512×512×3 to 64×64×4.75% similarVerifiedLatent Diffusion Models (LDM) reduce computational overhead by approximately 4–8 times by performing the diffusion process in a low-dimensional latent space.68% similarUnverifiedStable Diffusion通过预训练的VAE将512×512×3的图像编码为64×64×4的潜在张量，计算量降低约48倍59% similarUnverified扩散模型生成图像的宽高设置若偏离训练分辨率太远，可能导致重复构图、人物比例失调等问题59% similarVerifiedStable Diffusion compresses the diffusion process from pixel space to latent space, implemented by a Variational Autoencoder (VAE).58% similar
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