DiffuSeq implements a diffusion-based approach to sequence-to-sequence text generation, where a model learns to gradually denoise text sequences conditioned on input. The project provides an accelerated version (DiffuSeq-v2) that achieves 800x faster sampling while maintaining generation quality. It supports various text generation tasks including summarization, paraphrase, and dialogue generation through a unified conditional diffusion framework.