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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 ...
AUTOMATIC1111 Stable Diffusion Web UI (SD WebUI, A1111, or Automatic1111 [3]) is an open source generative artificial intelligence program that allows users to generate images from a text prompt. [4] It uses Stable Diffusion as the base model for its image capabilities together with a large set of extensions and features to customize its output.
DreamBooth can be used to fine-tune models such as Stable Diffusion, where it may alleviate a common shortcoming of Stable Diffusion not being able to adequately generate images of specific individual people. [4] Such a use case is quite VRAM intensive, however, and thus cost-prohibitive for hobbyist users. [4]
ComfyUI is an open source, node-based program that allows users to generate images from a series of text prompts.It uses free diffusion models such as Stable Diffusion as the base model for its image capabilities combined with other tools such as ControlNet and LCM Low-rank adaptation with each tool being represented by a node in the program.
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.
Carbon on Earth naturally occurs in two stable isotopes, with 98.9% in the form of 12 C and 1.1% in 13 C. [1] [8] The ratio between these isotopes varies in biological organisms due to metabolic processes that selectively use one carbon isotope over the other, or "fractionate" carbon through kinetic or thermodynamic effects. [1]
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]
In probability theory and statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion process is stochastic in nature and hence is used to model many real-life stochastic systems.