Transformer Based Diffusion Model

Transformer-based diffusion models are a rapidly evolving class of generative models leveraging the strengths of both transformer architectures and diffusion processes to generate high-quality data across diverse modalities, including images, videos, and 3D surfaces. Current research focuses on improving efficiency (e.g., through local attention mechanisms and caching strategies), enhancing controllability (e.g., via trajectory-oriented and rich-contextual conditioning), and expanding applications to areas like medical image generation and time-series forecasting. These models offer significant advancements in data augmentation, synthetic data generation, and various downstream tasks, impacting fields ranging from healthcare to computer graphics.