Introduction
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.
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Source · Latent Diffusion Models ↗RESEARCH ACCOUNT
Running diffusion in a compressed representation rather than pixel space.
ANTI-AI ARCHIVE · Revised 2026-10-02
Method and contribution
Latent diffusion first establishes an image representation through an autoencoder, then learns denoising in that space to reduce computation while retaining detail. Cross-attention introduces text or other conditions. The paper describes a framework; Stable Diffusion’s public release and licensing are later, separate events.
Section sources: Latent Diffusion Models
Art-historical interpretation
Compression shapes what visual information is readily retained, while conditioning defines where users can intervene. Separating these layers helps explain detail and limits of control without attributing every variation to the prompt.
Section sources: Latent Diffusion Models
Sources for this account
Author or conference abstract and version record read; full proofs and experiments were not independently reproduced.
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Continue with a comparative question
Compare the methods, forms of control or presentation contexts of this entry and Denoising Diffusion Probabilistic Models.
Compare the methods, forms of control or presentation contexts of this entry and Stable Diffusion public model release.
Dates & version record
Date displayed for this node: 2021-12 / 2022-06 · Historical date recorded for the source: 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
Timeline entry #129 · Historical node
Research materials & 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 based on the listed research materials, not a full translation of the linked work. AI-assisted translation; independent human review pending.
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Cite this node
ANTI-AI ARCHIVE. Latent Diffusion Models. Art-history node #129. https://salondesrefuses.cn/en/art-history/080
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