Diffusion Transformer

Diffusion Transformers (DiTs) are a class of generative models leveraging the transformer architecture to improve upon the capabilities of traditional diffusion models, primarily aiming for efficient and high-quality generation of various data modalities, including images, audio, and video. Current research focuses on optimizing DiT architectures for speed and efficiency through techniques like dynamic computation, token caching, and quantization, as well as exploring their application in diverse tasks such as image super-resolution, text-to-speech synthesis, and medical image segmentation. The improved efficiency and scalability of DiTs, along with their ability to handle complex data dependencies, are significantly impacting generative modeling across multiple scientific fields and practical applications.