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Masked Autoencoders Are Effective Tokenizers for Diffusion Models
5 February 2025
Zeyang Zhang
Yujin Han
Fangyi Chen
Xianrui Li
Yidong Wang
Jindong Wang
Zihan Wang
Zicheng Liu
Difan Zou
Bhiksha Raj
DiffM
SyDa
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Papers citing
"Masked Autoencoders Are Effective Tokenizers for Diffusion Models"
7 / 7 papers shown
Rotary Masked Autoencoders are Versatile Learners
Uros Zivanovic
Serafina Di Gioia
Andre Scaffidi
Martín de los Rios
Gabriella Contardo
R. Trotta
350
1
0
26 May 2025
VFRTok: Variable Frame Rates Video Tokenizer with Duration-Proportional Information Assumption
Tianxiong Zhong
Xingye Tian
Boyuan Jiang
Xuebo Wang
Xin Tao
Pengfei Wan
Zhiwei Zhang
349
3
0
17 May 2025
No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
Dengyang Jiang
Mengmeng Wang
Liuzhuozheng Li
Lei Zhang
Haoyu Wang
Wei Wei
Guang Dai
Yanning Zhang
Jingdong Wang
DiffM
800
25
0
05 May 2025
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models
Yushu Wu
Yanyu Li
Ivan Skorokhodov
Vidit Goel
Willi Menapace
Sharath Girish
Aliaksandr Siarohin
Yanzhi Wang
Sergey Tulyakov
DiffM
VGen
424
8
0
14 Apr 2025
GigaTok: Scaling Visual Tokenizers to 3 Billion Parameters for Autoregressive Image Generation
Tianwei Xiong
Jun Hao Liew
Zilong Huang
Jiashi Feng
Xihui Liu
512
35
0
11 Apr 2025
"Principal Components" Enable A New Language of Images
Xin Wen
Bingchen Zhao
Ismail Elezi
Jiankang Deng
Xiaojuan Qi
456
1
0
11 Mar 2025
Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis
Kai Qiu
Xianrui Li
Jason Kuen
Zeyang Zhang
Xiaohao Xu
Jiuxiang Gu
Yinyi Luo
Bhiksha Raj
Zhe Lin
Marios Savvides
665
9
0
11 Mar 2025
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