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 12, 2026

Efficient Model Scaling: The Correlation Between Diffusion Training and Knowledge Distillation

The current trend in AI modeling is shifting from simply increasing parameter counts to focusing on how to maintain high performance with less data while operating models efficiently. Particularly, as Large Language Mode

AIDeep LearningKnowledge DistillationDiffusion Model+1