3 citations · 4 across the 5 of their papers we have counts for
10 papers · 1 filter
Object-Aware Background-Controlled Editing via Weighted Velocity Guidance
Wuji Wang, Yue Wu, Chenhao Yi +1
Training-free image editing steers diffusion or flow-matching generative models at inference time by modifying prompt-conditioned denoising velocities. Existing velocity-based edit…
CARE-Edit: Condition-Aware Routing of Experts for Contextual Image Editing
Yucheng Wang, Zedong Wang, Yuetong Wu +2
Unified diffusion editors often rely on a fixed, shared backbone for diverse tasks, suffering from task interference and poor adaptation to heterogeneous demands (e.g., local vs gl…
T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
Kaiyue Sun, Kaiyi Huang, Xian Liu +4
Text-to-video (T2V) generative models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored.…
DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis
Yao Teng, Yue Wu, Han Shi +5
Diffusion models have achieved great success in image generation, with the backbone evolving from U-Net to Vision Transformers. However, the computational cost of Transformers is q…
Editing Massive Concepts in Text-to-Image Diffusion Models
Tianwei Xiong, Yue Wu, Enze Xie +2
Text-to-image diffusion models suffer from the risk of generating outdated, copyrighted, incorrect, and biased content. While previous methods have mitigated the issues on a small…
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
Junsong Chen, Chongjian Ge, Enze Xie +7
In this paper, we introduce PixArt-Σ, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution. PixArt-Σrepresents a significant advancement over…