Historical context & research account
Latent diffusion operates in the compressed space of a pretrained autoencoder and accepts conditions such as text through cross-attention. It reduces computational costs and provides the direct technical foundation for Stable Diffusion, helping make an open text-to-image ecosystem practicable on consumer graphics hardware.
Dates & version record
Date displayed for this node：2021-12 / 2022-06 · Date recorded in the research master：2021-12 / 2022-06
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Original sources & further reading
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Latent Diffusion Models
https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html
Current cited URL
Latent Diffusion Models preprint
https://arxiv.org/abs/2112.10752
URL cited in a research master or supplement; it may have moved or expired
Provenance, translation & verification
Archive node #080 · Historical node
Research masters & supplement references · 3
AI Art Genealogy Research Master · p. 24 · 080
Date as recorded: 2021-12 / 2022-06
Latent Diffusion Models ↗
Latent Diffusion Models
GenAI Visual Release Chronology Research Master · p. 3 · R010
Date as recorded: 2021-12
Latent Diffusion Models ↗
Latent Diffusion Models preprint
GenAI Visual Release Chronology Research Master · p. 3 · R012
Date as recorded: 2021-12/2022-06
Latent Diffusion Models ↗
Rombach et al. — Latent Diffusion Models
Archive account adapted from the masters, not a full translation of the linked work. AI-assisted translation; human review pending.
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Cite this node
ANTI-AI ARCHIVE. Latent Diffusion Models. Art-history node #080. https://salondesrefuses.cn/en/art-history/080
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