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2024-08-15 16:59:04.AIbase.11.1k
Disrupting Tradition! Lumina-mGPT Can Create Realistic and High-Resolution Images from Text
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
2024-07-23 17:14:34.AIbase.10.5k
Sakana AI Launches New Model to Revive Traditional Japanese Ukiyo-e Art
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
2023-10-09 11:57:54.AIbase.1.9k
The Generative AI Market is Expected to Reach $208.8 Billion by 2032
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.