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Stable Diffusion originated from a project called Latent Diffusion, [11] developed in Germany by researchers at Ludwig Maximilian University in Munich and Heidelberg University. Four of the original 5 authors (Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz) later joined Stability AI and released subsequent versions of Stable ...
7.1 surround sound is the common name for an eight-channel surround audio system commonly used in home theatre configurations. It adds two additional speakers to the more conventional six-channel ( 5.1 ) audio configuration.
Across Unix-like operating systems many different configuration-file formats exist, with each application or service potentially having a unique format, but there is a strong tradition of them being in human-editable plain text, and a simple key–value pair format is common.
Each black square depicts a loudspeaker. The center speaker in the top line of the diagram is used for dialogue. The left and right speakers on either side of the center speaker are used to create stereo sound for music and other sound effects in the film. The left and right surround speakers in the bottom line create the surround sound effect.
A four channel quadraphonic diagram showing the usual placement of speakers around the listener. Quadraphonic (or quadrophonic, also called quadrasonic or by the neologism quadio [1] [formed by analogy with "stereo"]) sound – equivalent to what is now called 4.0 surround sound – uses four audio channels in which speakers are positioned at the four corners of a listening space.
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Traditionally, the most widespread is Furse-Malham higher order format in the .amb container based on Microsoft's WAVE-EX file format. [32] It scales up to third order and has a file size limitation of 4GB. New implementations and productions might want to consider the AmbiX [33] proposal, which adopts the .caf file format and does away with ...
Various AI models, such as Llama 2, Mistral or Stable Diffusion, have been made open-weight, [312] [313] meaning that their architecture and trained parameters (the "weights") are publicly available. Open-weight models can be freely fine-tuned , which allows companies to specialize them with their own data and for their own use-case. [ 314 ]