6 papers · 1 filter
ViPO: Visual Preference Optimization at Scale
Ming Li, Jie Wu, Justin Cui +3
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference d…
Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization
Xinxin Liu, Ming Li, Zonglin Lyu +2
Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holisti…
CPO: Condition Preference Optimization for Controllable Image Generation
Zonglin Lyu, Ming Li, Xinxin Liu +1
To enhance controllability in text-to-image generation, ControlNet introduces image-based control signals, while ControlNet++ improves pixel-level cycle consistency between generat…
TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation
Zonglin Lyu, Chen Chen
Video Frame Interpolation (VFI) aims to predict the intermediate frame (we use n to denote time in videos to avoid notation overload with the timestep in diffusion models…
Frame Interpolation with Consecutive Brownian Bridge Diffusion
Zonglin Lyu, Ming Li, Jianbo Jiao +1
Recent work in Video Frame Interpolation (VFI) tries to formulate VFI as a diffusion-based conditional image generation problem, synthesizing the intermediate frame given a random…
ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback
Ming Li, Taojiannan Yang, Huafeng Kuang +4
To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that exis…