Transformer-based models are machine learning models that use transformer architecture to handle tasks like language processing. They are known for their efficiency in managing large amounts of data.
DALL·E 2 uses a powerful combination of transformer-based models and unsupervised learning to generate images with specific compositions or visual arrangements. It understands the relationships between different parts of an image and can create unique visuals based on textual prompts.
DALL·E 2 utilizes a neural network architecture that can generate images with varying levels of abstraction by leveraging transformer-based models. These models can understand patterns and relationships within images, allowing for the generation of complex visual concepts. Through a process known as conditional image generation, DALL·E 2 can translate textual descriptions into image representations, enabling the creation of diverse and detailed images.
The training data for training DALL·E 2 consists of a diverse range of images and text descriptions to teach the model to generate images from textual prompts. The techniques used to train DALL·E 2 include self-supervised learning, transformer-based models, and large-scale datasets for improved performance.
