2 citations · 3 across the 13 of their papers we have counts for
10 papers · 1 filter
VoT: Vision-of-Thought for Unified Multimodal Representation Alignment
Jingxiang Sun, Chao Liao, Zhengxiong Luo +6
Current text-to-image systems typically employ a "text encoder plus diffusion decoder" paradigm, in which text semantics directly modulate continuous latent noise. Despite their su…
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…
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…
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…
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
Lixue Gong, Xiaoxia Hou, Fanshi Li +25
Rapid advancement of diffusion models has catalyzed remarkable progress in the field of image generation. However, prevalent models such as Flux, SD3.5 and Midjourney, still grappl…
SeedEdit: Align Image Re-Generation to Image Editing
Yichun Shi, Peng Wang, Weilin Huang
We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance betwe…