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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]
This paper describes the latent diffusion model (LDM). This is the backbone of the Stable Diffusion architecture. Classifier-Free Diffusion Guidance (2022). [29] This paper describes CFG, which allows the text encoding vector to steer the diffusion model towards creating the image described by the text.
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 Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model. [ 2 ] [ 3 ] The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set).
Analogously, a classifier based on a generative model is a generative classifier, while a classifier based on a discriminative model is a discriminative classifier, though this term also refers to classifiers that are not based on a model. Standard examples of each, all of which are linear classifiers, are: generative classifiers:
The drift-diffusion model (DDM) is a well defined [19] model, that is proposed to implement an optimal decision policy for 2AFC. [20] It is the continuous analog of a random walk model. [ 7 ] The DDM assumes that in a 2AFC task, the subject is accumulating evidence for one or other of the alternatives at each time step, and integrating that ...
Text-to-Image Generation: Models like Stable Diffusion use CLIP's text encoder to transform text prompts into embeddings for image generation. [3] CLIP can also be used as a gradient signal for directly guiding diffusion ("CLIP guidance") [35] [36] or other generative art. [37]
A fast-and-frugal tree is a classification or a decision tree that has m+1 exits, with one exit for each of the first m −1 cues and two exits for the last cue. Mathematically, fast-and-frugal trees can be viewed as lexicographic heuristics or as linear classification models with non-compensatory weights and a threshold.