8 citations · 8 across the 8 of their papers we have counts for
8 papers
Self-OPD: On-Policy Distillation for Flow Matching Models without Teacher
Shiyi Zhang, Mushui Liu, Yunze Tong +8
On-policy distillation (OPD), which leverages a pre-trained, specialized teacher model to provide dense supervisory signals, has achieved significant success in Large Language Mode…
InfinityEdit: Infinite Video Editing with a Lightweight Edit-Ignition Adapter
Yunze Tong, Mushui Liu, Canyu Zhao +9
With large pretrained models, existing methods have effectively improved instruction-based video editing. However, most of them rely on an in-place editing assumption. They align t…
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
Zhengkang Guan, Yikang Chen, Yi He +5
Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and noisy systems, or suffer fro…
MARBLE: Multi-Aspect Reward Balance for Diffusion RL
Canyu Zhao, Hao Chen, Yunze Tong +3
Reinforcement learning fine-tuning has become the dominant approach for aligning diffusion models with human preferences. However, assessing images is intrinsically a multi-dimensi…
Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO
Yunze Tong, Mushui Liu, Canyu Zhao +7
Deploying GRPO on Flow Matching models has proven effective for text-to-image generation. However, existing paradigms typically propagate an outcome-based reward to all preceding d…
Noise Projection: Closing the Prompt-Agnostic Gap Behind Text-to-Image Misalignment in Diffusion Models
Yunze Tong, Didi Zhu, Zijing Hu +2
In text-to-image generation, different initial noises induce distinct denoising paths with a pretrained Stable Diffusion (SD) model. While this pattern could output diverse images,…
Asynchronous Denoising Diffusion Models for Aligning Text-to-Image Generation
Zijing Hu, Yunze Tong, Fengda Zhang +3
Diffusion models have achieved impressive results in generating high-quality images. Yet, they often struggle to faithfully align the generated images with the input prompts. This…
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization
Yunze Tong, Junkun Yuan, Min Zhang +4
Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source doma…