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The layout for new additions is at Portal:Human sexuality/Random picture/Layout. Number of pictures appearing on Portal:Human sexuality : 92. ( Click here to update this number if you add new pictures )
A single image random text ASCII stereogram is an alternative to SIRDS using random ASCII text instead of dots to produce a 3D form of ASCII art. Map textured stereogram In a map textured stereogram, "a fitted texture is mapped onto the depth image and repeated a number of times" resulting in a pattern where the resulting 3D image is often ...
The process used to develop the first random-dot stereogram is illustrated below. 1. Create an image of suitable size. Fill it with random dots. Duplicate the image. 2. Select a region in one image, in this case, in the right image. 3. Shift this region horizontally by one or two dot diameters and fill in the empty region with new random dots.
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.
Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.
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 ...
Enjoy a classic game of Hearts and watch out for the Queen of Spades!
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.