In this paper, we propose SVEET, a framework that requires merely training on a pretrained bidirectional video diffusion model but supports high-quality streaming video editing in an auto-regressive fashion. To tackle this problem, we first systematically revisit existing video-to-video diffusion approaches and identif...
Yu-Jia Hu, Jia-Jun Li, Zi-Hao He et al.· 0 citations
Recent advances in generative models have achieved remarkable performance in text- and image-conditioned editing. However, preserving the content of a given image while referencing style patterns from another remains challenging, often leading to uncontrollable stylization results. In this paper, we approach image styl...
Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer. However, existing methods for training these agents typically treat all steps within a trajectory uniformly during both supervised fine-tuning (SFT) and reinforcement lea...
Yi-Jun Lu, Rui Ye, Jiajun Wang et al.· 0 citations
FIT employs a lightweight Degradation Encoder to predict a global degradation vector and a spatial degradation map from local degradation severity, which jointly condition the patch embedding and unembedding through adaptive deformation, and introduces a task-token dropout strategy that regularizes task conditioning du...
Zi-Hao He, Yunfeng Wu, Xinchao Wang et al.· 0 citations
This work proposes EditBridge, a diffusion bridge framework for efficient ultra high-resolution editing that achieves high-fidelity editing with superior perceptual quality at resolutions up to 4K, delivering 3.6--8.4$\times$ speedup at 2K and enabling practical 4K editing in 61 seconds.
Jiayi Song, Shijie Huang, Fangtai Wu et al.· 0 citations
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