126 citations · 376 across the 37 of their papers we have counts for
14 papers · 1 filter
Score Distillation of Flow Matching Models
Mingyuan Zhou, Yi Gu, Huangjie Zheng +5
Diffusion models achieve high-quality image generation but are limited by slow iterative sampling. Distillation methods alleviate this by enabling one- or few-step generation. Flow…
STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot +7
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer…
Normalizing Flows are Capable Generative Models
Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran +7
Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but ha…
What If We Recaption Billions of Web Images with LLaMA-3?
Xianhang Li, Haoqin Tu, Mude Hui +9
Web-crawled image-text pairs are inherently noisy. Prior studies demonstrate that semantically aligning and enriching textual descriptions of these pairs can significantly enhance…
Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation
Mingyuan Zhou, Zhendong Wang, Huangjie Zheng +1
Diffusion-based text-to-image generation models trained on extensive text-image pairs have demonstrated the ability to produce photorealistic images aligned with textual descriptio…
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…
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…
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…
Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs
Huangjie Zheng, Pengcheng He, Weizhu Chen +1
Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks with a simple architecture and relatively small computational cost. The…
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
Fei Ye, Huangjie Zheng, Chaoqin Huang +1
Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consisten…
Elastic Boundary Projection for 3D Medical Image Segmentation
Tianwei Ni, Lingxi Xie, Huangjie Zheng +2
We focus on an important yet challenging problem: using a 2D deep network to deal with 3D segmentation for medical image analysis. Existing approaches either applied multi-view pla…
Phase Collaborative Network for Two-Phase Medical Image Segmentation
Huangjie Zheng, Lingxi Xie, Tianwei Ni +5
In real-world practice, medical images acquired in different phases possess complementary information, {\em e.g.}, radiologists often refer to both arterial and venous scans in ord…