6 papers
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…
Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning
Shiwen Zhang, Haoyuan Wang, Xianghao Zang +3
Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to entangled content and style features. With a rev…
TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillation
Shiwen Zhang, Yifan Xu, Haibin Huang +2
Given a content reference and a style reference, content-preserving style transfer requires the model to generate stylized outputs with content and style consistency. We introduced…
Tele-Omni: a Unified Multimodal Framework for Video Generation and Editing
Jialun Liu, Tian Li, Xiao Cao +20
Recent advances in diffusion-based video generation have substantially improved visual fidelity and temporal coherence. However, most existing approaches remain task-specific and r…
TeleStyle: Content-Preserving Style Transfer in Images and Videos
Shiwen Zhang, Xiaoyan Yang, Bojia Zi +3
Content-preserving style transfer, generating stylized outputs based on content and style references, remains a significant challenge for Diffusion Transformers (DiTs) due to the i…
QwenStyle: Content-Preserving Style Transfer with Qwen-Image-Edit
Shiwen Zhang, Haibin Huang, Chi Zhang +1
Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to its internal entangled content and style feature…