Reda, Fitsum
Cosmos World Foundation Model Platform for Physical AI
NVIDIA, null, :, null, Agarwal, Niket, Ali, Arslan, Bala, Maciej, Balaji, Yogesh, Barker, Erik, Cai, Tiffany, Chattopadhyay, Prithvijit, Chen, Yongxin, Cui, Yin, Ding, Yifan, Dworakowski, Daniel, Fan, Jiaojiao, Fenzi, Michele, Ferroni, Francesco, Fidler, Sanja, Fox, Dieter, Ge, Songwei, Ge, Yunhao, Gu, Jinwei, Gururani, Siddharth, He, Ethan, Huang, Jiahui, Huffman, Jacob, Jannaty, Pooya, Jin, Jingyi, Kim, Seung Wook, Klár, Gergely, Lam, Grace, Lan, Shiyi, Leal-Taixe, Laura, Li, Anqi, Li, Zhaoshuo, Lin, Chen-Hsuan, Lin, Tsung-Yi, Ling, Huan, Liu, Ming-Yu, Liu, Xian, Luo, Alice, Ma, Qianli, Mao, Hanzi, Mo, Kaichun, Mousavian, Arsalan, Nah, Seungjun, Niverty, Sriharsha, Page, David, Paschalidou, Despoina, Patel, Zeeshan, Pavao, Lindsey, Ramezanali, Morteza, Reda, Fitsum, Ren, Xiaowei, Sabavat, Vasanth Rao Naik, Schmerling, Ed, Shi, Stella, Stefaniak, Bartosz, Tang, Shitao, Tchapmi, Lyne, Tredak, Przemek, Tseng, Wei-Cheng, Varghese, Jibin, Wang, Hao, Wang, Haoxiang, Wang, Heng, Wang, Ting-Chun, Wei, Fangyin, Wei, Xinyue, Wu, Jay Zhangjie, Xu, Jiashu, Yang, Wei, Yen-Chen, Lin, Zeng, Xiaohui, Zeng, Yu, Zhang, Jing, Zhang, Qinsheng, Zhang, Yuxuan, Zhao, Qingqing, Zolkowski, Artur
Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make our platform open-source and our models open-weight with permissive licenses available via https://github.com/NVIDIA/Cosmos.
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models
NVIDIA, null, :, null, Atzmon, Yuval, Bala, Maciej, Balaji, Yogesh, Cai, Tiffany, Cui, Yin, Fan, Jiaojiao, Ge, Yunhao, Gururani, Siddharth, Huffman, Jacob, Isaac, Ronald, Jannaty, Pooya, Karras, Tero, Lam, Grace, Lewis, J. P., Licata, Aaron, Lin, Yen-Chen, Liu, Ming-Yu, Ma, Qianli, Mallya, Arun, Martino-Tarr, Ashlee, Mendez, Doug, Nah, Seungjun, Pruett, Chris, Reda, Fitsum, Song, Jiaming, Wang, Ting-Chun, Wei, Fangyin, Zeng, Xiaohui, Zeng, Yu, Zhang, Qinsheng
We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in which image signals at different frequency bands are attenuated at varying rates. Edify Image supports a wide range of applications, including text-to-image synthesis, 4K upsampling, ControlNets, 360 HDR panorama generation, and finetuning for image customization.