This repository implements a diffusion model architecture for text generation, replacing autoregressive decoding with an iterative denoising process over continuous text embeddings. The model trains on paired datasets (E2E, ROCstory) using a transformer backbone and supports controllable generation by training a classifier (e.g., syntactic parser) to guide the diffusion sampling process toward desired attributes.