1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
OmniStyle2: Learning to Stylize by Learning to Destylize
Ye Wang, Zili Yi, Yibo Zhang +6
This paper introduces a scalable paradigm for supervised style transfer by inverting the problem: instead of learning to stylize directly, we learn to destylize, reducing stylistic…
FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization
Peng Zheng, Ye Wang, Rui Ma +1
Subject-driven image generation plays a crucial role in applications such as virtual try-on and poster design. Existing approaches typically fine-tune pretrained generative models…
OmniStyle: Filtering High Quality Style Transfer Data at Scale
Ye Wang, Ruiqi Liu, Jiang Lin +4
In this paper, we introduce OmniStyle-1M, a large-scale paired style transfer dataset comprising over one million content-style-stylized image triplets across 1,000 diverse style c…
Interpretable Style Takagi-Sugeno-Kang Fuzzy Clustering
Suhang Gu, Ye Wang, Yongxin Chou +3
Clustering is an efficient and essential technique for exploring latent knowledge of data. However, limited attention has been given to the interpretability of the clusters detecte…