52 citations · 54 across the 8 of their papers we have counts for
6 papers · 1 filter
Post-Training Pruning for Diffusion Transformers
Chengzhi Hu, Xuewen Liu, Jing Zhang +3
Diffusion Transformers (DiTs) have demonstrated impressive performance in image generation but suffer from substantial computational overhead and resource consumption. Post-trainin…
PTQ4ARVG: Post-Training Quantization for AutoRegressive Visual Generation Models
Xuewen Liu, Zhikai Li, Jing Zhang +2
AutoRegressive Visual Generation (ARVG) models retain an architecture compatible with language models, while achieving performance comparable to diffusion-based models. Quantizatio…
Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation
Xuewen Liu, Zhikai Li, Jing Zhang +2
Diffusion Transformers dominate video generation, but the quadratic complexity of attention computation introduces substantial latency. Attention sparsity reduces computational cos…
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation
Xuewen Liu, Zhikai Li, Minhao Jiang +3
Model quantization is a promising method for accelerating and compressing diffusion models. Nevertheless, since post-training quantization (PTQ) fails catastrophically at low-bit c…
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers
Zhikai Li, Mengjuan Chen, Junrui Xiao +1
Data-free quantization can potentially address data privacy and security concerns in model compression, and thus has been widely investigated. Recently, PSAQ-ViT designs a relative…
Patch Similarity Aware Data-Free Quantization for Vision Transformers
Zhikai Li, Liping Ma, Mengjuan Chen +2
Vision transformers have recently gained great success on various computer vision tasks; nevertheless, their high model complexity makes it challenging to deploy on resource-constr…