Pre Trained Image Diffusion Model

Pre-trained image diffusion models are being extensively adapted for various video and image processing tasks, leveraging their strong semantic understanding and generative capabilities to achieve high-quality results without extensive task-specific training. Current research focuses on improving temporal consistency in video applications, enhancing efficiency through techniques like token merging and flow matching, and applying these models to tasks such as image enhancement, interpolation, restoration, and text-to-video generation. This adaptability makes pre-trained image diffusion models a powerful tool with significant implications for diverse fields, including computer vision, graphics, and multimedia editing.