Transformers, originally designed for natural language processing, have been adapted for image generation and processing. Vision Transformers (ViTs) process images by dividing them into patches, which are then encoded into tokenized representations.
Self-Attention Mechanism: Captures long-range dependencies across the entire image instead of focusing on local features like CNNs.
Positional Encoding: Helps the model understand spatial relationships between image patches.
Pretraining with Large Datasets: Enables transformers to generalize across various image domains.
Transformer-based architectures such as DALL·E and Imagen outperform traditional CNNs in text-to-image synthesis by leveraging large-scale training data and improved pattern recognition.