1 citations · 1 across the 5 of their papers we have counts for
5 papers
SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation
Zhuguanyu Wu, Ruihao Gong, Yang Yong +5
Distribution Matching Distillation (DMD) is a widely used paradigm for accelerating inference in few-step video diffusion models. However, DMD-style video distillation faces two co…
Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals
Xiangyu Fan, Zesong Qiu, Zhuguanyu Wu +6
Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling…
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation
Zhuguanyu Wu, Shihe Wang, Jiayi Zhang +2
Post-training quantization (PTQ) has stood out as a cost-effective and promising model compression paradigm in recent years, as it avoids computationally intensive model retraining…
APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers
Zhuguanyu Wu, Jiayi Zhang, Jiaxin Chen +3
Vision Transformers (ViTs) have become one of the most commonly used backbones for vision tasks. Despite their remarkable performance, they often suffer significant accuracy drops…
AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer
Zhuguanyu Wu, Jiaxin Chen, Hanwen Zhong +2
Vision Transformer (ViT) has become one of the most prevailing fundamental backbone networks in the computer vision community. Despite the high accuracy, deploying it in real appli…