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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.
The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]
The name diffusion is from the thermodynamic diffusion, since they were first developed with inspiration from thermodynamics. [13] [14] Models in Stable Diffusion series before SD 3 all used a variant of diffusion models, called latent diffusion model (LDM), developed in 2021 by the CompVis (Computer Vision & Learning) [15] group at LMU Munich ...
is the Diffusion coefficient [2] and is the Source term. [3] A portion of the two dimensional grid used for Discretization is shown below: Graph of 2 dimensional plot. In addition to the east (E) and west (W) neighbors, a general grid node P, now also has north (N) and south (S) neighbors.
Diffusion process is stochastic in nature and hence is used to model many real-life stochastic systems. Brownian motion , reflected Brownian motion and Ornstein–Uhlenbeck processes are examples of diffusion processes.
Logical data model, a representation of an organization's data, organized in terms of entities and relationships; Logical Disk Manager; Local Data Manager; LTSP Display Manager, an X display manager for Linux Terminal Server Project; Latent diffusion model, in machine learning; Latitude dependent mantle, a widespread layer of ice-rich material ...
Also, the model displays examples of both nearest-neighbor jumps (straight) and next-nearest-neighbor jumps (diagonal). Not to scale on a spatial or temporal basis. Surface diffusion is a general process involving the motion of adatoms , molecules , and atomic clusters ( adparticles ) at solid material surfaces . [ 1 ]
Applications based on diffusion maps include face recognition, [7] spectral clustering, low dimensional representation of images, image segmentation, [8] 3D model segmentation, [9] speaker verification [10] and identification, [11] sampling on manifolds, anomaly detection, [12] [13] image inpainting, [14] revealing brain resting state networks ...