5 papers
Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs
Yunhong Lu, Qichao Wang, Hengyuan Cao +2
Existing preference datasets for text-to-image models typically store only the final winner/loser images. This representation is insufficient for rectified flow (RF) models, whose…
DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models
Qichao Wang, Yunhong Lu, Hengyuan Cao +2
Dataset distillation enables efficient training by distilling the information of large-scale datasets into significantly smaller synthetic datasets. Diffusion based paradigms have…
Spherical Geometry Diffusion: Generating High-quality 3D Face Geometry via Sphere-anchored Representations
Junyi Zhang, Yiming Wang, Yunhong Lu +5
A fundamental challenge in text-to-3D face generation is achieving high-quality geometry. The core difficulty lies in the arbitrary and intricate distribution of vertices in 3D spa…
Smoothed Preference Optimization via ReNoise Inversion for Aligning Diffusion Models with Varied Human Preferences
Yunhong Lu, Qichao Wang, Hengyuan Cao +2
Direct Preference Optimization (DPO) aligns text-to-image (T2I) generation models with human preferences using pairwise preference data. Although substantial resources are expended…
InPO: Inversion Preference Optimization with Reparametrized DDIM for Efficient Diffusion Model Alignment
Yunhong Lu, Qichao Wang, Hengyuan Cao +3
Without using explicit reward, direct preference optimization (DPO) employs paired human preference data to fine-tune generative models, a method that has garnered considerable att…