Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Li, Huixia"'
Autor:
Cheng, Jiaxiang, Xie, Pan, Xia, Xin, Li, Jiashi, Wu, Jie, Ren, Yuxi, Li, Huixia, Xiao, Xuefeng, Zheng, Min, Fu, Lean
Recent advancement in text-to-image models (e.g., Stable Diffusion) and corresponding personalized technologies (e.g., DreamBooth and LoRA) enables individuals to generate high-quality and imaginative images. However, they often suffer from limitatio
Externí odkaz:
http://arxiv.org/abs/2403.02084
Autor:
Li, Lijiang, Li, Huixia, Zheng, Xiawu, Wu, Jie, Xiao, Xuefeng, Wang, Rui, Zheng, Min, Pan, Xin, Chao, Fei, Ji, Rongrong
Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an undispute
Externí odkaz:
http://arxiv.org/abs/2309.10438
Autor:
Ma, Yuexiao, Li, Huixia, Zheng, Xiawu, Xiao, Xuefeng, Wang, Rui, Wen, Shilei, Pan, Xin, Chao, Fei, Ji, Rongrong
Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. I
Externí odkaz:
http://arxiv.org/abs/2303.11906
Autor:
Li, Jiashi, Xia, Xin, Li, Wei, Li, Huixia, Wang, Xing, Xiao, Xuefeng, Wang, Rui, Zheng, Min, Pan, Xin
Due to the complex attention mechanisms and model design, most existing vision Transformers (ViTs) can not perform as efficiently as convolutional neural networks (CNNs) in realistic industrial deployment scenarios, e.g. TensorRT and CoreML. This pos
Externí odkaz:
http://arxiv.org/abs/2207.05501
Autor:
Li, Huixia, Yan, Chenqian, Lin, Shaohui, Zheng, Xiawu, Li, Yuchao, Zhang, Baochang, Yang, Fan, Ji, Rongrong
Deep convolutional neural networks (DCNNs) have shown dominant performance in the task of super-resolution (SR). However, their heavy memory cost and computation overhead significantly restrict their practical deployments on resource-limited devices,
Externí odkaz:
http://arxiv.org/abs/2011.04212