AWhat changed
Ran diffusion generation in pretrained autoencoder latent spaces and used cross-attention for conditioning, reducing image-generation computational requirements.
Scientific scope
Ran diffusion generation in pretrained autoencoder latent spaces and used cross-attention for conditioning, reducing image-generation computational requirements. Year follows CVPR 2022; first preprint was 2021. The method precedes later Stable Diffusion releases and does not assign those products' development exclusively to the paper authors.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Year follows CVPR 2022; first preprint was 2021. The method precedes later Stable Diffusion releases and does not assign those products' development exclusively to the paper authors.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- High-Resolution Image Synthesis with Latent Diffusion Models
- CVPR 2022: original latent diffusion paper
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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