5 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models
Zhenyu Zhou, Defang Chen, Siwei Lyu +2
Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free samplin…
Mesh-Pro: Asynchronous Advantage-guided Ranking Preference Optimization for Artist-style Quadrilateral Mesh Generation
Zhen Zhou, Jian Liu, Biwen Lei +10
Reinforcement learning (RL) has demonstrated remarkable success in text and image generation, yet its potential in 3D generation remains largely unexplored. Existing attempts typic…
DICE: Distilling Classifier-Free Guidance into Text Embeddings
Zhenyu Zhou, Defang Chen, Can Wang +2
Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these images failing to align closely…
Simple and Fast Distillation of Diffusion Models
Zhenyu Zhou, Defang Chen, Can Wang +2
Diffusion-based generative models have demonstrated their powerful performance across various tasks, but this comes at a cost of the slow sampling speed. To achieve both efficient…
Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
Zhenyu Zhou, Defang Chen, Can Wang +1
Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs), with the aim of obtaining an accurate solution with as few number…
A Geometric Perspective on Diffusion Models
Defang Chen, Zhenyu Zhou, Jian-Ping Mei +3
Recent years have witnessed significant progress in developing effective training and fast sampling techniques for diffusion models. A remarkable advancement is the use of stochast…