5 papers
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…
Geometric Regularity in Deterministic Sampling Dynamics of Diffusion-based Generative Models
Defang Chen, Zhenyu Zhou, Can Wang +1
Diffusion-based generative models employ stochastic differential equations (SDEs) and their equivalent probability flow ordinary differential equations (ODEs) to establish a smooth…
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…