A research team from New York University, MIT, and Google has recently proposed an innovative framework aimed at addressing the bottleneck of inference time scaling in diffusion models. This groundbreaking research transcends the traditional method of simply increasing the denoising steps, opening up new avenues for improving generative model performance. The framework primarily unfolds from two dimensions: one is leveraging feedback from validators, and the second is implementing algorithms to discover better noise candidates. The research team builds on a pre-trained SiT-XL model with a resolution of 256×256, maintaining 250 fixed denoising steps.

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

Tokyo-based startup Sakana AI has introduced two new image generation models — Evo-Ukiyoe and Evo-Nishikie. Image from Sakana AI official These models can be found on the Hugging Face platform, with the main purpose of generating Ukiyo-e style art through text or image prompts. As is known, Ukiyo-e is a traditional Japanese art form popular from the 17th to the 19th century, depicting themes such as historical scenes, natural landscapes, and sumo wrestlers. Sakana AI aims to use AI to bring thi

The global generative artificial intelligence market size is expected to grow from $10.5 billion in 2022 to $208.8 billion by 2032, with a compound annual growth rate (CAGR) of 35.1%. The North American market currently dominates, but significant growth is expected in the Asia-Pacific region, particularly in China, over the coming years. Generative AI technology has vast application prospects and is poised to fundamentally change the future of content production. Despite the enormous growth potential in the market, challenges such as high implementation costs and talent shortages remain in the technology application process.