2 citations · 2 across the 3 of their papers we have counts for
5 papers
Histogram-constrained Image Generation
Haoming Liu, Yuanhe Guo, Yijia Cao +2
Diffusion models have emerged as a dominant paradigm in generative modeling, enabling high-fidelity sampling from complex data distributions. Despite impressive capabilities, contr…
From Navigation to Refinement: Revealing the Two-Stage Nature of Flow-based Diffusion Models through Oracle Velocity
Haoming Liu, Jinnuo Liu, Yanhao Li +5
Flow-based diffusion models have emerged as a leading paradigm for training generative models across images and videos. However, their memorization-generalization behavior remains…
ImageGem: In-the-wild Generative Image Interaction Dataset for Generative Model Personalization
Yuanhe Guo, Linxi Xie, Zhuoran Chen +5
We introduce ImageGem, a dataset for studying generative models that understand fine-grained individual preferences. We posit that a key challenge hindering the development of such…
Diffusion Cocktail: Mixing Domain-Specific Diffusion Models for Diversified Image Generations
Haoming Liu, Yuanhe Guo, Shengjie Wang +1
Diffusion models, capable of high-quality image generation, receive unparalleled popularity for their ease of extension. Active users have created a massive collection of domain-sp…
GEMRec: Towards Generative Model Recommendation
Yuanhe Guo, Haoming Liu, Hongyi Wen
Recommender Systems are built to retrieve relevant items to satisfy users' information needs. The candidate corpus usually consists of a finite set of items that are ready to be se…