Zobrazeno 1 - 10
of 200
pro vyhledávání: '"Xiao, Xuefeng"'
Autor:
Ji, Yatai, Zhang, Shilong, Wu, Jie, Sun, Peize, Chen, Weifeng, Xiao, Xuefeng, Yang, Sidi, Yang, Yujiu, Luo, Ping
The rapid advancement of Large Vision-Language models (LVLMs) has demonstrated a spectrum of emergent capabilities. Nevertheless, current models only focus on the visual content of a single scenario, while their ability to associate instances across
Externí odkaz:
http://arxiv.org/abs/2407.07577
The rapid development of diffusion models has triggered diverse applications. Identity-preserving text-to-image generation (ID-T2I) particularly has received significant attention due to its wide range of application scenarios like AI portrait and ad
Externí odkaz:
http://arxiv.org/abs/2404.15449
Autor:
Ren, Yuxi, Xia, Xin, Lu, Yanzuo, Zhang, Jiacheng, Wu, Jie, Xie, Pan, Wang, Xing, Xiao, Xuefeng
Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs). Current distillation techniques often dichotomize into two
Externí odkaz:
http://arxiv.org/abs/2404.13686
Autor:
Li, Ming, Yang, Taojiannan, Kuang, Huafeng, Wu, Jie, Wang, Zhaoning, Xiao, Xuefeng, Chen, Chen
To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that existing methods still face significant challenges in generating images th
Externí odkaz:
http://arxiv.org/abs/2404.07987
Autor:
Zhang, Jiacheng, Wu, Jie, Ren, Yuxi, Xia, Xin, Kuang, Huafeng, Xie, Pan, Li, Jiashi, Xiao, Xuefeng, Zheng, Min, Fu, Lean, Li, Guanbin
Diffusion models have revolutionized the field of image generation, leading to the proliferation of high-quality models and diverse downstream applications. However, despite these significant advancements, the current competitive solutions still suff
Externí odkaz:
http://arxiv.org/abs/2404.05595
Autor:
Ren, Yuxi, Wu, Jie, Lu, Yanzuo, Kuang, Huafeng, Xia, Xin, Wang, Xionghui, Wang, Qianqian, Zhu, Yixing, Xie, Pan, Wang, Shiyin, Xiao, Xuefeng, Wang, Yitong, Zheng, Min, Fu, Lean
Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) i
Externí odkaz:
http://arxiv.org/abs/2404.04860
Autor:
Ma, Yuexiao, Li, Huixia, Zheng, Xiawu, Ling, Feng, Xiao, Xuefeng, Wang, Rui, Wen, Shilei, Chao, Fei, Ji, Rongrong
The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing and accelerating neural networks. Among these techniques, Post-Trainin
Externí odkaz:
http://arxiv.org/abs/2403.12544
Autor:
Shi, Yuan, Xia, Bin, Jin, Xiaoyu, Wang, Xing, Zhao, Tianyu, Xia, Xin, Xiao, Xuefeng, Yang, Wenming
Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and dif
Externí odkaz:
http://arxiv.org/abs/2403.11423
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