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20212024
most citedLatent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

37 citations · 79 across the 7 of their papers we have counts for

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7 papers

cs.CV2024

Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation

Tong Shao, Zhuotao Tian, Hang Zhao +1

CLIP, as a vision-language model, has significantly advanced Open-Vocabulary Semantic Segmentation (OVSS) with its zero-shot capabilities. Despite its success, its application to O…

cs.CV202323 cited

LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Simian Luo, Yiqin Tan, Suraj Patil +6

Latent Consistency Models (LCMs) have achieved impressive performance in accelerating text-to-image generative tasks, producing high-quality images with minimal inference steps. LC…

cs.CV202337 cited

Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Simian Luo, Yiqin Tan, Longbo Huang +2

Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and lea…

cs.CV20231 cited

AutoEncoding Tree for City Generation and Applications

Wenyu Han, Congcong Wen, Lazarus Chok +4

City modeling and generation have attracted an increased interest in various applications, including gaming, urban planning, and autonomous driving. Unlike previous works focused o…

cs.CV20233 cited

StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction

Tianyuan Yuan, Yicheng Liu, Yue Wang +2

High-Definition (HD) maps are essential for the safety of autonomous driving systems. While existing techniques employ camera images and onboard sensors to generate vectorized high…

cs.AI202313 cited

ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory

Chenxu Hu, Jie Fu, Chenzhuang Du +3

Large language models (LLMs) with memory are computationally universal. However, mainstream LLMs are not taking full advantage of memory, and the designs are heavily influenced by…