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Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom.
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.
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.
In August 2022 Stability AI rose to prominence with the release of its source and weights available text-to-image model Stable Diffusion. [2] On March 23, 2024, Emad Mostaque stepped down from his position as CEO. The board of directors appointed COO, Shan Shan Wong, and CTO, Christian Laforte, as the interim co-CEOs of Stability AI. [11]
Max Resistance was an example game from the company website that was released in 2001 to promote the final version of the Game Creation System, to showcase both its gameplay and graphical abilities - particularly 3D terrain and models. It was also made quickly and cheaply, to show the relative quality that could be easily attained with the tool.
This is a list of stereoscopic video games. The following article is the list of notable stereoscopic 3D games and related productions and the platforms they can run on. Additionally, many PC games are supported or are unsupported but capable 3D graphics with AMD HD3D, DDD TriDef, Nvidia 3D Vision, 3DGM, and more. [1]
Instead of an autoregressive Transformer, DALL-E 2 uses a diffusion model conditioned on CLIP image embeddings, which, during inference, are generated from CLIP text embeddings by a prior model. [22] This is the same architecture as that of Stable Diffusion , released a few months later.
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]