5 papers
Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
Yasi Zhang, Tianyu Chen, Zhendong Wang +3
Learning generative models directly from corrupted observations is a long standing challenge across natural and scientific domains. We introduce Restoration Score Distillation (RSD…
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation
Tianyu Chen, Yasi Zhang, Zhendong Wang +3
Diffusion models have achieved remarkable success in generating high-resolution, realistic images across diverse natural distributions. However, their performance heavily relies on…
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
Yasi Zhang, Peiyu Yu, Yaxuan Zhu +4
Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the in…
Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models
Yasi Zhang, Peiyu Yu, Ying Nian Wu
Text-to-image diffusion models have shown great success in generating high-quality text-guided images. Yet, these models may still fail to semantically align generated images with…
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation
Yingshan Chang, Yasi Zhang, Zhiyuan Fang +3
The literature on text-to-image generation is plagued by issues of faithfully composing entities with relations. But there lacks a formal understanding of how entity-relation compo…