  Diffusion models have gained prominence in generating high-quality sequences
of text. Nevertheless, current approaches predominantly represent discrete text
within a continuous diffusion space, which incurs substantial computational
overhead during training and results in slower sampling speeds. In this paper,
we introduce a soft absorbing state that facilitates the diffusion model in
learning to reconstruct discrete mutations based on the underlying Gaussian
space, thereby enhancing its capacity to recover conditional signals. During
the sampling phase, we employ state-of-the-art ODE solvers within the
continuous space to expedite the sampling process. Comprehensive experimental
evaluations reveal that our proposed method effectively accelerates the
training convergence by 4x and generates samples of similar quality 800x
faster, rendering it significantly closer to practical application.
\footnote{The code is released at \url{https://github.com/Shark-NLP/DiffuSeq}
