"Quickly, they are iterative models that take random noise as inputs, which can be conditioned with a text or an image, so it is not completely random noise."

"So the basic diffusion models will take random noise with the size of the image and learn to apply even further noise until we get back to a real image."

"This is possible because the model will have access to the real images during training and will be able to learn the right parameters by applying such noise to the image iteratively until it reaches complete noise and is unrecognizable."

"Then, when we are satisfied with the noise we get from all images, meaning that they are similar and generate noise from a similar distribution, we are ready to use our model in reverse and feed it similar noise in the reverse order to expect an image similar to the ones used during training."

"So the main problem here is that you are working directly with the pixels and large data inputs like images."

"How can these powerful diffusion models be computationally efficient? By transforming them into latent diffusion models."

"This means that Robin Rombach and his colleagues implemented this diffusion approach we just covered within a compressed image representation instead of the image itself and then worked to reconstruct the image."

"Finally, you reconstruct the image using a decoder which you can see as the reverse step of your initial encoder."