May 20, 2026

The Mathematical Aesthetics of Timestep Design: Beyond Simple Iteration

While Diffusion models excel at generating high-quality data by incrementally removing noise, the massive computational load and time efficiency issues they pose remain significant challenges. Because denoising requires

Diffusion ModelTimestep SchedulingConvergence OptimizationGenerative AI+1

May 20, 2026

The Two Pillars of Generative Model Precision and Efficiency: Optimization Algorithms and Hardware Resource Design

Diffusion models, the mainstream of modern generative modeling, demonstrate an extraordinary ability to produce high-quality samples. However, they simultaneously face the challenge of balancing sophisticated sampling qu

Diffusion ModelEM AlgorithmFPGAGenerative AI+1

May 17, 2026

Closure Swap and Data Integrity: A Technical Defense Against Information Loss in Generative Models

While Diffusion models—the core of modern generative AI—exhibit an extraordinary ability to produce high-quality samples, the issue of information loss during the data reconstruction process remains a significant challen

DiffusionGenerative AIKnowledge DistillationData Integrity+1