5 citations · 5 across the 4 of their papers we have counts for
6 papers · 1 filter
DSAQuant: Denoising-Stage-Aligned Quantization-Aware Training for Video Generation
Shuaiting Li, Zelin Gao, Haibin Shen +3
Video diffusion models (VDMs) have achieved impressive progress in text-to-video generation, but their high memory and computational costs hinder practical deployment. Quantization…
ViM-VQ: Efficient Post-Training Vector Quantization for Visual Mamba
Juncan Deng, Shuaiting Li, Zeyu Wang +3
Visual Mamba networks (ViMs) extend the selective state space model (Mamba) to various vision tasks and demonstrate significant potential. As a promising compression technique, vec…
SSVQ: Unleashing the Potential of Vector Quantization with Sign-Splitting
Shuaiting Li, Juncan Deng, Chenxuan Wang +5
Vector Quantization (VQ) has emerged as a prominent weight compression technique, showcasing substantially lower quantization errors than uniform quantization across diverse models…
MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization
Shuaiting Li, Chengxuan Wang, Juncan Deng +5
Vector quantization(VQ) is a hardware-friendly DNN compression method that can reduce the storage cost and weight-loading datawidth of hardware accelerators. However, conventional…
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion
Shuaiting Li, Juncan Deng, Zeyu Wang +5
Text-to-image generation via Stable Diffusion models (SDM) have demonstrated remarkable capabilities. However, their computational intensity, particularly in the iterative denoisin…
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers
Juncan Deng, Shuaiting Li, Zeyu Wang +3
The Diffusion Transformers Models (DiTs) have transitioned the network architecture from traditional UNets to transformers, demonstrating exceptional capabilities in image generati…