Search results
Results From The WOW.Com Content Network
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.
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.
Stable Diffusion 3 (2024-03) [65] changed the latent diffusion model from the UNet to a Transformer model, and so it is a DiT. It uses rectified flow. It uses rectified flow. Stable Video 4D (2024-07) [ 66 ] is a latent diffusion model for videos of 3D objects.
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 ...
The methodology used to run implementations of DreamBooth involves the fine-tuning the full UNet component of the diffusion model using a few images (usually 3--5) depicting a specific subject. Images are paired with text prompts that contain the name of the class the subject belongs to, plus a unique identifier.
A variant of diffusion weighted imaging, diffusion spectrum imaging (DSI), [4] was used in deriving the Connectome data sets; DSI is a variant of diffusion-weighted imaging that is sensitive to intra-voxel heterogeneities in diffusion directions caused by crossing fiber tracts and thus allows more accurate mapping of axonal trajectories than ...
A text-to-video model is a machine learning model that uses a natural language description as input to produce a video relevant to the input text. [1] Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models. [2]
A jump-diffusion model is a form of mixture model, mixing a jump process and a diffusion process. In finance, jump-diffusion models were first introduced by Robert C. Merton. [6] Such models have a range of financial applications from option pricing, to credit risk, to time series forecasting. [7]