Recent advancements in artificial intelligence have led to the development of several innovative models aimed at enhancing image and video generation. Researchers have introduced TurboSL, a method that captures detailed 3D images using a camera and projector, significantly improving the speed and accuracy of 3D imaging. Another notable contribution is Apple's Matryoshka Diffusion Models, which employ a hierarchical approach for efficient high-resolution image generation. Additionally, the Carve3D method utilizes reinforcement learning to enhance the consistency of multi-view reconstructions from text prompts. In the realm of video generation, CogVideoX is a large-scale diffusion transformer model designed to generate videos based on text prompts, while MaskINT focuses on efficient video editing using text. Furthermore, the Dysen-VDM system empowers dynamics-aware text-to-video diffusion, improving the generation of videos from descriptions. Other advancements include Smooth Diffusion for generating smoother images, SuperNormal for creating detailed 3D models from multiple images, and RecDiffusion for rectifying image boundaries in stitching processes. These developments signify a new era in generative deep learning, particularly in the application of diffusion models.