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Latent diffusion model

Deep generative model

The latent diffusion model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.

Nº Q130641540 ★

Commune · Littérature

Latent diffusion model

Deep generative model

Texte en anglais

The latent diffusion model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.

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Texte en anglais Pas encore d'article dans ta langue : extrait en anglais.

The latent diffusion model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images. The LDM is an improvement on standard DM by performing diffusion modeling in a latent space, and by allowing self-attention and cross-attention conditioning. LDMs are widely used in practical diffusion models. For instance, Stable Diffusion versions 1.1 to 2.1 were based on the LDM architecture.

Texte : Wikipédia en anglais, CC BY-SA 4.0. ·

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