Text Diffusion Model

Text diffusion models are a novel approach to text generation that leverages the iterative refinement capabilities of diffusion processes, offering an alternative to traditional autoregressive methods. Current research focuses on improving the quality and efficiency of text generation through various architectures, including encoder-decoder transformers and models operating in the space of pre-trained language model encodings, often incorporating techniques like self-conditioning and adaptive noise scheduling to address the discrete nature of text. These models show promise in diverse applications, such as molecule generation from textual instructions, image and video editing guided by text, and improving the quality of text within generated images. The ongoing development of efficient and high-quality text diffusion models is significantly impacting natural language processing and related fields.