Architecture of Stable Diffusion
Stable Diffusion is founded on a diffusion model known as Latent Diffusion, which is recognized for its advanced abilities in image synthesis, specifically in tasks such as image painting, style transfer, and text-to-image generation. Unlike other diffusion models that focus solely on pixel manipulation, latent diffusion integrates cross-attention layers into its architecture. These layers enable the model to assimilate information from various sources, including text and other inputs.
There are three main components in latent diffusion:
- Autoencoder
- U-Net
- Text Encoder
Autoencoder:
An autoencoder is designed to learn a compressed version of the input image. A Variational Autoencoder consists of two main parts i.e. an encoder and a decoder. The encoder’s task is to compress the image into a latent form, and then the decoder uses this latent representation to recreate the original image.
U-Net:
U-Net is a kind of convolutional neural network (CNN) that’s used for cleaning up the latent representation of an image. It’s made up of a series of encoder-decoder blocks that step by step increase the image quality. The encoder part of the network reduces the image down to a lower resolution, and then the decoder works to bring this compressed image backup to its original, higher resolution and eliminate any noise in the process.
Text Encoder
The job of the text encoder is to convert text prompts into a latent form. Typically, this is achieved using a transformer-based model, such as the Text Encoder from CLIP, which takes a series of input tokens and transforms them into a sequence of latent text embeddings.
Generate Images from Text in Python – Stable Diffusion
Looking for the images can be quite a hassle don’t you think? Guess what? AI is here to make it much easier! Just imagine telling your computer what kind of picture you’re looking for and voila it generates it for you. That’s where Stable Diffusion, in Python, comes into play. It’s like magic – transforming words into visuals. In this article, we’ll explore how you can utilize Diffusion in Python to discover and craft stunning images. It’s, like having an artist right at your fingertips!