25 citations · 50 across the 21 of their papers we have counts for
5 papers · 1 filter
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization
Rui Xie, Tianchen Zhao, Zhihang Yuan +5
Visual Autoregressive (VAR) has emerged as a promising approach in image generation, offering competitive potential and performance comparable to diffusion-based models. However, c…
DiTFastAttn: Attention Compression for Diffusion Transformer Models
Zhihang Yuan, Hanling Zhang, Pu Lu +6
Diffusion Transformers (DiT) excel at image and video generation but face computational challenges due to the quadratic complexity of self-attention operators. We propose DiTFastAt…
ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation
Tianchen Zhao, Tongcheng Fang, Haofeng Huang +10
Diffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, la…
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
Tianchen Zhao, Xuefei Ning, Tongcheng Fang +6
Diffusion models have achieved significant visual generation quality. However, their significant computational and memory costs pose challenge for their application on resource-con…
FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative Models
Lin Zhao, Tianchen Zhao, Zinan Lin +4
In recent years, there has been significant progress in the development of text-to-image generative models. Evaluating the quality of the generative models is one essential step in…