Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

A generative model that learns to denoise data, achieving state-of-the-art image generation (Stable Diffusion, DALL-E 2).

A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.

Creating new images from scratch or from text descriptions using generative models (GANs, diffusion models, VAEs).

A collection of data examples used for training, validating, or testing machine learning models.

The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.

A function that turns a list of scores (logits) into probabilities that are all positive and sum to 1; the standard output of classifiers and language models.

Training a smaller 'student' model to mimic a larger 'teacher' model, transferring knowledge while reducing size.