22 citations · 45 across the 5 of their papers we have counts for
5 papers
PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models
Yongsen Cheng, Kai Liu, Kaiwen Tao +5
Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challenging in resource-constrained sc…
DenoiseGS: Gaussian Reconstruction Model for Burst Denoising
Yongsen Cheng, Yuanhao Cai, Yulun Zhang
Burst denoising methods are crucial for enhancing images captured on handheld devices, but they often struggle with large motion or suffer from prohibitive computational costs. In…
VideoPoet: A Large Language Model for Zero-Shot Video Generation
Dan Kondratyuk, Lijun Yu, Xiuye Gu +28
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-on…
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
Lijun Yu, José Lezama, Nitesh B. Gundavarapu +13
While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation. To effec…
MAGVIT: Masked Generative Video Transformer
Lijun Yu, Yong Cheng, Kihyuk Sohn +8
We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spat…