Introduction to Video Diffusion Models and Computational Challenges Diffusion models have made impressive progress in generating high-quality, coherent videos, building on their success in image synthesis. However, handling the extra temporal dimension in videos significantly increases computational demands, especially since self-attention scales poorly with sequence length. This makes it difficult to train or run these…

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly when operating directly on high-dimensional pixel data. Researchers have been investigating ways to optimize latent space representations to improve efficiency without compromising image quality. A critical problem in diffusion models is…