OUSAC: Accelerating Diffusion Models with Optimized Guidance and Adaptive Caching

This research explores optimizations for diffusion models, specifically targeting acceleration through guidance scheduling and caching. The focus on DiT (Denoising Diffusion Transformer) suggests a practical application within the rapidly evolving field of generative AI.