Non-Asymptotic Convergence of Discrete Diffusion Models: Masked and Random Walk dynamics

Researchers have established new and sharp convergence guarantees for three discrete diffusion models (DDMs) used in generative modeling, addressing challenges posed by their combinatorial structure and recent introduction. This work provides optimal non-asymptotic convergence bounds under minimal assumptions, offering practical insights for developers working with complex data distributions in high-dimensional spaces.

Read the full article at arXiv stat.ML

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