Transformer models are a class of neural network architecture that process sequences of data using a mechanism called self-attention, which allows every element in the input to directly relate to and influence every other element simultaneously rather than processing the sequence step by step. Originally developed for natural language processing, transformer architectures have been adapted across AI domains including image generation, video synthesis, and multimodal systems, and now underpin most state-of-the-art AI generation models.

The self-attention mechanism is what distinguishes transformers from earlier sequential architectures. By computing relationships between all elements in an input simultaneously, transformers can capture long-range dependencies and contextual relationships that were difficult for earlier architectures to learn. In text-to-image and text-to-video generation, transformer-based text encoders process the prompt and build a rich representation of its meaning that then conditions the generation process. Fully transformer-based generation architectures, sometimes called diffusion transformers or DiT models, apply the attention mechanism to the generation process itself rather than only to text processing, enabling better global coherence across an image or video frame. Many leading models including Sora and FLUX use transformer-based generation architectures.

For practitioners, understanding transformers helps explain why modern AI generation models are so responsive to nuanced prompt language - the attention mechanism allows the model to understand complex relationships between concepts in a prompt rather than treating each word independently. It also contextualizes why model size matters: larger transformers with more parameters can learn and represent more complex relationships, generally producing more capable and coherent outputs.