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A latent space, also known as a latent feature space or embedding space, is an embedding of a set of items within a manifold in which items resembling each other are positioned closer to one another. Position within the latent space can be viewed as being defined by a set of latent variables that emerge from the resemblances from the objects.
This noise vector is scaled down and subtracted away from the latent image array, resulting in a slightly less noisy latent image. The denoising is repeated according to a denoising schedule ("noise schedule"), and the output of the last step is processed by the VAE decoder into a finished image.
It is a cascaded diffusion model with three sub-models. The first step denoises a white noise to a 64×64 image, conditional on the embedding vector of the text. This model has 2B parameters. The second step upscales the image by 64×64→256×256, conditional on embedding. This model has 650M parameters.
When a latent image is formed around the gold speck, the presence of gold is known to reduce the number of metallic silver atoms necessary to render the crystal developable. Another important concept in increasing photographic sensitivity is to separate photoholes away from photoelectrons and sensitivity sites.
Thus, the encoder maps each point (such as an image) from a large complex dataset into a distribution within the latent space, rather than to a single point in that space. The decoder has the opposite function, which is to map from the latent space to the input space, again according to a distribution (although in practice, noise is rarely ...
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 ...
Each generated image starts as a constant [note 1] array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance).
An image is represented as either a texture and shape, or as a latent image that has been transformed, denoted by = (). A Siamese neural network works in tandem on two different input vectors to compute comparable output vectors.