Diffusion Language Models Explained: The Shift to Parallel Generation

A visual explainer of diffusion language models. Covers how image diffusion translates to text via token masking, confidence-based parallel decoding where the most certain words emerge first, why autoregressive generation leaves GPUs idle between sequential passes, and how a fixed number of denoising steps makes generation speed independent of sequence length. Closes with an honest look at where autoregressive models still lead.