5 papers · 1 filter
Beyond Binary Preference: Aligning Diffusion Models to Fine-grained Criteria by Decoupling Attributes
Chenye Meng, Zejian Li, Zhongni Liu +9
Post-training alignment of diffusion models relies on simplified signals, such as scalar rewards or binary preferences. This limits alignment with complex human expertise, which is…
Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models
Zejian Li, Yize Li, Chenye Meng +7
Recent advancements in diffusion models (DMs) have been propelled by alignment methods that post-train models to better conform to human preferences. However, these approaches typi…
Diffusion Distillation With Direct Preference Optimization For Efficient 3D LiDAR Scene Completion
An Zhao, Shengyuan Zhang, Ling Yang +6
The application of diffusion models in 3D LiDAR scene completion is limited due to diffusion's slow sampling speed. Score distillation accelerates diffusion sampling but with perfo…
LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural Annotations
Zejian Li, Chenye Meng, Yize Li +9
Recent advances in text-to-image (T2I) generation have shown remarkable success in producing high-quality images from text. However, existing T2I models show decayed performance in…
Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion
Shengyuan Zhang, An Zhao, Ling Yang +7
Diffusion models have been applied to 3D LiDAR scene completion due to their strong training stability and high completion quality. However, the slow sampling speed limits the prac…