Google's DeepMind, in collaboration with the Massachusetts Institute of Technology (MIT), recently announced a significant research breakthrough. The research team developed a new autoregressive model called 'Fluid', which has made groundbreaking progress in the field of text-to-image generation. After scaling up to 10.5 billion parameters, the model has demonstrated outstanding performance. This research overturns the industry's common perception; while autoregressive models have dominated the language processing domain, they have traditionally been seen as inferior to Stable Diffusion and Goog.

Multimodal generative models are leading a new trend in artificial intelligence, focusing on the integration of visual and textual data to create multifunctional AI systems that perform various tasks, from image generation to understanding and reasoning across different data types. A key challenge is to enhance the capabilities of autoregressive (AR) models so they can generate high-detail images based on textual descriptions. While diffusion models excel in generating high-quality images, AR models lag behind in image quality, resolution flexibility, and multitasking capabilities. Researchers from Shanghai AI Lab and The Chinese University of Hong Kong have introduced Lum