VAE (latent autoencoder)
Compresses images into a small latent space where diffusion actually happens, then decodes the result back to pixels.
Introduced inDec 2013Auto-Encoding Variational Bayes
Latent diffusion models never diffuse pixels directly. A variational autoencoder first compresses the image ~8× per side into a compact latent tensor, the diffusion process denoises in that space, and the VAE decoder renders the final latent back into pixels. This is the trick that made high-resolution diffusion affordable, and it is why generation graphs end with a VAE decode stage.
Adoption over time
Share of new models that have included a VAE over time.
See it in real models
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Comfy-Org/MiniMax-H3↓ 17.3M · ♡ 2kOpen in visualizer
MiniMaxAI/MiniMax-H3image-text-to-video · ↓ 4.0M · ♡ 4kOpen in visualizer
Comfy-Org/Krea-2↓ 2.2M · ♡ 465Open in visualizer
microsoft/TRELLIS.2-4Bimage-to-3d · ↓ 1.6M · ♡ 1kOpen in visualizer
stabilityai/stable-diffusion-xl-base-1.0text-to-image · ↓ 1.6M · ♡ 8kOpen in visualizer
Lightricks/LTX-2.3image-to-video · ↓ 1.6M · ♡ 2kOpen in visualizer
stable-diffusion-v1-5/stable-diffusion-v1-5text-to-image · ↓ 1.4M · ♡ 1kOpen in visualizer
stabilityai/sdxl-turbotext-to-image · ↓ 1.1M · ♡ 3kOpen in visualizer
Tongyi-MAI/Z-Image-Turbotext-to-image · ↓ 794k · ♡ 5kOpen in visualizer
nvidia/Cosmos3-Edge↓ 789k · ♡ 170Open in visualizer
microsoft/VibeVoice-ASRautomatic-speech-recognition · ↓ 697k · ♡ 1kOpen in visualizer
black-forest-labs/FLUX.2-devimage-to-image · ↓ 655k · ♡ 2kOpen in visualizer
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