Informed Correctors for Discrete Diffusion Models

The recent publication on arXiv presents a novel approach to discrete diffusion models through an informed corrector that aims to improve sampling efficiency and quality. Traditional sampling methods often struggle with balancing computational demands and sample fidelity, especially when the number of sampling steps is minimized. The proposed predictor-corrector sampling scheme effectively mitigates these issues by leveraging insights from the diffusion model to correct approximation errors. Complementary architectural enhancements, including hollow transformers and a specialized training objective, further bolster the method's effectiveness. Empirical results on datasets such as text8 and tokenized ImageNet 256x256 reveal that the informed corrector consistently yields superior samples with fewer errors, as evidenced by improved FID scores. This advancement not only highlights the potential of informed correctors for generating high-fidelity outputs but also sets a new benchmark for f…