Explain how Stable Diffusion works in a general sense. In two paragraphs.
Stable diffusion is a deep generative neural network which used a kind of diffusion model called a latent diffusion model (LDM). Diffusion models are constructed by first describing a procedure for gradually turning data into noise, and then training a neural network that learns to invert this procedure step-by-step. Each of these steps consists of taking a noisy input and making it slightly less noisy, by filling in some of the information obscured by the noise.
Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. The VAE encoder is used to reduce the size of the input image (down sampling) to what is called the latent image, then gaussian noise is added to this latent image and the U-Net is asked to reverse this noising step. Finally, a VAE decoder is used to convert the latent image back to the original size of the input image (up sampling).
Can I make my own latent diffusion model? And if so how?