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Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [ 3 ] 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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Stable Diffusion (2022-08), released by Stability AI, consists of a denoising latent diffusion model (860 million parameters), a VAE, and a text encoder. The denoising network is a U-Net, with cross-attention blocks to allow for conditional image generation.
In June, 2023, a news report citing more than 30 sources, including investors and former Stability AI employees, stated that Mostaque had misled investors and the public about his educational background, a partnership with Amazon Web Services and the extent of his involvement in developing Stable Diffusion.
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The reason given is: The article fails to adequately capture recent criticism against Chrome (and by extension Google) for anti-competitive practises and privacy compromising behavior. Please help update this article to reflect recent events or newly available information. (September 2024)
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