From the 1 of 5 linked papers with an AI index.
4 papers · 1 filter
Post-Training Pruning for Diffusion Transformers
Chengzhi Hu, Xuewen Liu, Jing Zhang +3
The paper introduces DiT-Pruning, a post‑training pruning method tailored for Diffusion Transformers that uses a new energy‑based saliency metric and clustering‑aware granularity t…
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…