According to DeepLearning.AI, Kevin Frans and colleagues at UCBerkeley have introduced a novel method to accelerate image generation using diffusion models. This 'shortcut' approach allows models to take larger noise-removal steps, effectively equivalent to multiple smaller steps, without compromising output quality. This advancement could potentially improve the efficiency of image-based trading analytics by allowing faster data processing and model training. [Source: DeepLearning.AI]