#GPU performance

Discover efficient approaches to optimize algorithms with CUDA, featuring implementations ranging from PyTorch compilation to advanced techniques like FastAtomicAdd and UpsampleNearest2D. This project provides a thorough guide with comprehensive notes and empirical results, assisting in executing effective CUDA methods for better computational performance, with notable improvements in bandwidth and processing speed. Suitable for developers exploring complex insights and real-world applications in CUDA optimization across multiple platforms.

This project provides a refined solution for converting image sequences into stylized videos, optimized for GPUs comparable to RTX 3080. It utilizes various samplers and schedulers like DDIM, DPM++ 2M Karras, LCM, and Euler for efficient video generation up to 120+ frames. The integration with ComfyUI ensures a modular workflow. Future enhancements focus on accelerating processing speeds through pre-trained models and techniques like RCFG and stable-fast conversion.