Here we go:

1- What is Stable-Diffusion?

• Stable Diffusion is a deep learning, text-to-image model.
• Every Image on the internet make up the bulk of training data for image generation models.
• In order to generate images from text, we need to first “train” a machine learning model on a large dataset of images and their corresponding text descriptions. This training data is used to teach the model the relationships between the words in the text descriptions and the visual features of the corresponding images.
• Once we teach the model how to link words in a text description to the corresponding images, it can use deep learning to figure out the relationships between the two on its own. The way “deep learning” works is it creates neural networks with layers of interconnected “neurons”, which process and analyze large amounts of data to solve problems like matching text to images. All of this means it can take a new text description and make a related new image

2- Clipping:

• In the context of embeddings, clipping refers to the process of limiting the values of the embeddings to a certain range. Embeddings are mathematical representations of data, such as words or images, that are used in natural language processing and computer vision tasks. These embeddings are typically represented as vectors (arrays of numbers) and are used as input for machine learning models.
• When training a model, it is often necessary to normalize the embeddings so that they have similar ranges of values. One common way to do this is by clipping the values of the embeddings to a certain range, for example, between -1 and 1.
• This process is usually done to prevent the embeddings from taking on extreme values that can cause issues during training, such as causing the model to converge to a suboptimal solution or diverge. Clipping can also be used to prevent overflow or underflow in the numerical computations done during training.
• In simple terms, in embedding, clipping is the process of limiting the value of the embeddings to a certain range, this is typically done to prevent the embeddings from taking on extreme values that can cause issues during training or numerical computations.

3- The Diffusion in Stable-Diffusion:

• In your head, you’ve got at least three “axes” - one for shape, one for color, another for size maybe. Footballs are in one spot on this graph, bowling balls are on another.
• Stable Diffusion does something similar, except it has a lot more dimensions and variables. Here our big brain gets left behind - it can’t visualize more than 3 dimensions, but our models have more than 500 dimensions and an insane number of variables. This is called latent space.

• Imagine you're training a machine learning model to generate cat pictures from text. The latent space in this case would be a space where each point represents a different cat picture. So, if you were to drop a description of a fluffy white cat into the latent space, the model would navigate through the space and find the point that represents a fluffy white cat. Then, it would use that point as a starting point to generate a new, related cat picture.