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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 .
Substantial information concerning Stable Diffusion v1 was only added to GitHub on August 10, 2022. [16] All of Stable Diffusion (SD) versions 1.1 to XL were particular instantiations of the LDM architecture. SD 1.1 to 1.4 were released by CompVis in August 2022. There is no "version 1.0". SD 1.1 was a LDM trained on the laion2B-en dataset.
As the HTTP/1.0 standard did not define any 1xx status codes, servers must not [note 1] send a 1xx response to an HTTP/1.0 compliant client except under experimental conditions. 100 Continue The server has received the request headers and the client should proceed to send the request body (in the case of a request for which a body needs to be ...
Fig 1:Flow domain illustrating false diffusion. In figure 1, u = 2 and v = 2 m/s everywhere so the velocity field is uniform and perpendicular to the diagonal (XX). The boundary conditions for temperature on north and west wall is 100 ̊C and for east and south wall is 0 ̊C. This region is meshed into 10×10 equal grids.
A 1-bit image of the Statue of David, dithered with Floyd–Steinberg algorithm. Floyd–Steinberg dithering is an image dithering algorithm first published in 1976 by Robert W. Floyd and Louis Steinberg. It is commonly used by image manipulation software.
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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.
Lower case denotes the face and upper case denotes node; , , and refer to the "East," "West," and "Central" cell. (again, see Fig. 1 below). Defining variable F as convection mass flux and variable D as diffusion conductance = and =