Introduction to Generative AI

In the realm of image generation, models like DALL-E 2, Imagen, and Stable Diffusion are among the most notable. These models use a type of neural network architecture called "Diffusion Models" to generate images from text descriptions. In this article, we will discuss what these models are, how they work, and some potential applications for them in the future. What is a Diffusion Model? A diffusion model is a type of generative model that learns to denoise data by iteratively adding noise to it and then learning to remove that noise. This process effectively reverses the natural tendency of data to become "noisier" over time due to various physical processes, such as thermal motion in particles or random mutations in genetic information. By learning this reverse process, diffusion models can generate new, synthetic data samples that resemble the original training data. In terms of image generation, a diffusion model takes a simple noise pattern as input and learns how to gradually transform it into an actual photo-realistic image through multiple iterations. This transformation process is guided by a "conditioning" signal, such as a text description or another image, which helps the model generate images that match the desired content or style. The training process for diffusion models involves learning two main components: the denoising function and the reverse process. The denoising function learns to remove noise from data samples, while the reverse process learns how to iteratively apply this denoising function in order to generate new data samples. During training, a diffusion model is fed with noisy input images and corresponding original clean images. It then learns to predict the added noise at each step of the forward diffusion process and uses this information to gradually remove the noise from the input image until it reaches the original clean image. Once trained, a diffusion model can be used for generating new data samples by taking random noise patterns as input and applying the learned reverse process iteratively until an actual image is generated. This generation process can be guided by a conditioning signal, such as a text description or another image, which helps control the content and style of the generated images. Applications of Diffusion Models in Image Generation: The ability to generate high-quality photo-realistic images from simple text descriptions has numerous potential applications in various fields, including: 1. Art and Design: � The Theory (Coming soon) �️