126 citations · 376 across the 37 of their papers we have counts for
7 papers · 1 filter
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling
Huangjie Zheng, Zhendong Wang, Jianbo Yuan +5
Diffusion models excel at generating photo-realistic images but come with significant computational costs in both training and sampling. While various techniques address these comp…
Beta Diffusion
Mingyuan Zhou, Tianqi Chen, Zhendong Wang +1
We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distri…
Class-Balancing Diffusion Models
Yiming Qin, Huangjie Zheng, Jiangchao Yao +2
Diffusion-based models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies. However, such observation is only justified…
POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models
Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng +2
Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big models have zero-shot ca…
Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond
Mohammadreza Armandpour, Ali Sadeghian, Huangjie Zheng +2
Although text-to-image diffusion models have made significant strides in generating images from text, they are sometimes more inclined to generate images like the data on which the…
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Zhendong Wang, Yifan Jiang, Huangjie Zheng +5
Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the tr…
DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration
Zhixin Wang, Xiaoyun Zhang, Ziying Zhang +4
Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. Thi…