AI Design Glossary

The attention mechanism is an AI component that allows neural networks to selectively focus on different parts of input data.

A multimodal AI model developed by OpenAI that can represent text and images in the same vector space. Used as a prompt understanding layer in image generation tools.

Cross-attention is a specialized attention mechanism where computations are performed between two different data sequences.

A deep learning model that generates images by gradually denoising. It starts from random noise and step by step creates a meaningful image.

The process of converting text, images, or other data types into dense, fixed-size numerical vectors. Used for semantic similarity calculation and model input representation.

A deep learning model where two neural networks are trained against each other: a generator and a discriminator. The generator tries to produce realistic data, while the discriminator tries to distinguish between real and fake data.

LCM reduces traditional diffusion models' dozens of steps to just 4-8 steps.

A multidimensional space where data is compressed and mathematically represented. Diffusion models perform image generation in this compressed space for computational efficiency.

SDXL is an advanced diffusion model released by Stability AI in 2023, offering 1024x1024 native resolution.

A deep learning architecture based on the attention mechanism with parallel processing capability. It forms the foundation of both language and visual models.

A probabilistic deep learning model that encodes data into a compressed latent space and can generate new data from this space. Used in the image encoding layer of diffusion models.

The extension of diffusion models to the time dimension for use in video generation. By denoising in both spatial and temporal dimensions, it creates consistent and fluid video sequences.