2 citations · 3 across the 8 of their papers we have counts for
9 papers
Seedream 4.0: Toward Next-generation Multimodal Image Generation
Team Seedream, :, Yunpeng Chen +48
We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image compositi…
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Yu Gao, Haoyuan Guo, Tuyen Hoang +41
Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously b…
Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation
Chao Liao, Liyang Liu, Xun Wang +7
Recent progress in unified models for image understanding and generation has been impressive, yet most approaches remain limited to single-modal generation conditioned on multiple…
Scaling Diffusion Transformers Efficiently via P
Chenyu Zheng, Xinyu Zhang, Rongzhen Wang +5
Diffusion Transformers have emerged as the foundation for vision generative models, but their scalability is limited by the high cost of hyperparameter (HP) tuning at large scales.…
SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL
Junke Wang, Zhi Tian, Xun Wang +4
This work presents SimpleAR, a vanilla autoregressive visual generation framework without complex architecure modifications. Through careful exploration of training and inference o…
Seedream 3.0 Technical Report
Yu Gao, Lixue Gong, Qiushan Guo +28
We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in…