12 papers
ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations
Junke Wang, Xiao Wang, Jiacheng Pan +16
This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…
OmniGen-AR: AutoRegressive Any-to-Image Generation
Junke Wang, Xun Wang, Qiushan Guo +4
Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, ex…
Leveraging Verifier-Based Reinforcement Learning in Image Editing
Hanzhong Guo, Jie Wu, Jie Liu +6
While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A k…
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…
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.…
MEF: A Systematic Evaluation Framework for Text-to-Image Models
Xiaojing Dong, Weilin Huang, Liang Li +6
Rapid advances in text-to-image (T2I) generation have raised higher requirements for evaluation methodologies. Existing benchmarks center on objective capabilities and dimensions,…