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  2. Stable Diffusion - Wikipedia

    en.wikipedia.org/wiki/Stable_Diffusion

    [14] [8] Introduced in 2015, diffusion models are trained with the objective of removing successive applications of Gaussian noise on training images, which can be thought of as a sequence of denoising autoencoders. Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. [15]

  3. Latent diffusion model - Wikipedia

    en.wikipedia.org/wiki/Latent_Diffusion_Model

    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.

  4. Diffusion model - Wikipedia

    en.wikipedia.org/wiki/Diffusion_model

    After training to convergence, it can be used for image generation by starting with an image composed of random noise, and applying the network iteratively to denoise the image. Diffusion-based image generators have seen widespread commercial interest, such as Stable Diffusion and DALL-E. These models typically combine diffusion models with ...

  5. Variational autoencoder - Wikipedia

    en.wikipedia.org/wiki/Variational_autoencoder

    In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods .

  6. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.

  7. Flow-based generative model - Wikipedia

    en.wikipedia.org/wiki/Flow-based_generative_model

    A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.

  8. Displacement ventilation - Wikipedia

    en.wikipedia.org/wiki/Displacement_ventilation

    Displacement ventilation is best suited for taller spaces (higher than 3 meters [10 feet]). [2] Standard mixing ventilation may be better suited for smaller spaces where air quality is not as great a concern, such as single-occupant offices, and where the room height is not tall (e.g., lower than 2.3 meters [7.5 feet]).

  9. Diffusion (acoustics) - Wikipedia

    en.wikipedia.org/wiki/Diffusion_(acoustics)

    Diffusors (or diffusers) are used to treat sound aberrations, such as echoes, in rooms. They are an excellent alternative or complement to sound absorption because they do not remove sound energy, but can be used to effectively reduce distinct echoes and reflections while still leaving a live sounding space.