GADD accelerates discrete diffusion sampling from many steps to logarithmically few steps without additional training, providing both theoretical guarantees and practical speedups for text and symbolic generation tasks.
This paper speeds up discrete diffusion models (used for text and symbolic data generation) by introducing GADD, a new method that uses Gibbs corrections to reduce sampling steps. Unlike existing acceleration techniques, GADD doesn't require extra training and achieves theoretically optimal speedup, making it practical for real applications like text and music generation.