Stable Diffusion works by adding noise to an image and then reversing the noising process gradually until there is no noise, thus generating a realistic image to match the text prompt. It uses a deep neural network to predict and subtract the noise image that was added to the original. It also compresses the image into a latent space, which is 48 times smaller than the image space, to reduce computation.
Stable Diffusion uses latent diffusion which is a technique that runs the diffusion process in a compressed space, called latent space, instead of pixel space. This makes training cost lower and inference speed faster. Latent diffusion models (LDMs) are trained to remove noise from latent codes, which are obtained by applying an autoencoder to images. LDMs can generate high-resolution images with various tasks, such as inpainting, super-resolution, or semantic scene synthesis.
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