2 citations · 2 across the 3 of their papers we have counts for
7 papers
Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization
Yu Xu, Fan Tang, Juan Cao +5
Personalized generation paradigms empower designers to customize visual intellectual properties with the help of textual descriptions by tuning or adapting pre-trained text-to-imag…
Music Style Transfer with Time-Varying Inversion of Diffusion Models
Sifei Li, Yuxin Zhang, Fan Tang +3
With the development of diffusion models, text-guided image style transfer has demonstrated high-quality controllable synthesis results. However, the utilization of text for divers…
Learning Image Demoireing from Unpaired Real Data
Yunshan Zhong, Yuyao Zhou, Yuxin Zhang +2
This paper focuses on addressing the issue of image demoireing. Unlike the large volume of existing studies that rely on learning from paired real data, we attempt to learn a demoi…
MotionCrafter: One-Shot Motion Customization of Diffusion Models
Yuxin Zhang, Fan Tang, Nisha Huang +4
The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements. While text-to-video generative diffusion models have recent…
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution Networks
Yunshan Zhong, Mingbao Lin, Jingjing Xie +3
This paper introduces Distribution-Flexible Subset Quantization (DFSQ), a post-training quantization method for super-resolution networks. Our motivation for developing DFSQ is bas…
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
Nisha Huang, Yuxin Zhang, Weiming Dong
Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and t…