arXiv:2605.26582v1 Announce Type: new Abstract: Discrete diffusion models achieve strong performance in text and image generation, but their inference remains slow and must inherently balance sampling efficiency and sample quality. In this work, we present a systematic study of how the \emph{degree of stochasticity} in Markov transitions governs the sampling tradeoff. We show that highly deterministic transitions converge rapidly but suffer from error accumulation, while more stochastic transitions converge more slowly yet can achieve higher final sample quality. Using an information-theoretic
This paper addresses a fundamental limitation in discrete diffusion models, a key technique in modern AI generation, at a time when these models are seeing widespread adoption and demand for improved efficiency.
Improving the efficiency and quality of generative AI models directly impacts the economic viability and deployment speed of many AI applications, particularly those in text and image generation.
New understanding of stochasticity in diffusion models could lead to more efficient and higher-quality generative AI, potentially accelerating development cycles and reducing computational resource requirements.
- · AI developers
- · Cloud providers (via better resource utilization)
- · Content creation industries
- · Generative AI startups
- · AI models with suboptimal sampling
- · Compute-intensive model training paradigms
More efficient and higher quality generative AI models become available for various applications.
Reduced computational costs for deploying such models could lower barriers to entry for new AI services and products.
Accelerated development and adoption of AI systems could further exacerbate demand for advanced compute infrastructure, impacting the compute supply chain.
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Read at arXiv cs.LG