3 citations · 3 across the 1 of their papers we have counts for
3 papers
cs.CV2025
CDST: Color Disentangled Style Transfer for Universal Style Reference Customization
Shiwen Zhang, Zhuowei Chen, Lang Chen +1
We introduce Color Disentangled Style Transfer (CDST), a novel and efficient two-stream style transfer training paradigm which completely isolates color from style and forces the s…
cs.CV2024
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
Zinan Guo, Yanze Wu, Zhuowei Chen +3
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation. By incorporating a Lightning T2I branch with a sta…
cs.CV2024★ 3 cited
DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations
Tianhao Qi, Shancheng Fang, Yanze Wu +5
The diffusion-based text-to-image model harbors immense potential in transferring reference style. However, current encoder-based approaches significantly impair the text controlla…