4 citations · 5 across the 3 of their papers we have counts for
5 papers
Sink-Sparse Hybrid Attention for Efficient Inference in T2I/T2V Diffusion Transformers
Bingxin Xing, Yu Zhang, Jiatong Ding +2
Diffusion Transformers (DiTs) have become a common backbone for high-quality text-to-image and text-to-video generation, but repeated dense self-attention remains a major inference…
HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates
Lei Lu, Yize Li, Yanzhi Wang +2
Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling. Conventional LIC…
HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
Lei Lu, Yanyue Xie, Wei Jiang +3
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates. Previous LIC methods based on transmitting quantized continuous featur…
Squat: Quant Small Language Models on the Edge
Xuan Shen, Peiyan Dong, Zhenglun Kong +9
A growing trend has emerged in designing high-quality Small Language Models (SLMs) with a few million parameters. This trend is driven by the increasing concerns over cloud costs,…
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
Xuan Shen, Peiyan Dong, Lei Lu +5
Large Language Models (LLMs) stand out for their impressive performance in intricate language modeling tasks. However, their demanding computational and memory needs pose obstacles…