36 citations · 118 across the 6 of their papers we have counts for
6 papers
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
Guangxuan Xiao, Tianwei Yin, William T. Freeman +2
Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-sp…
Offsite-Tuning: Transfer Learning without Full Model
Guangxuan Xiao, Ji Lin, Song Han
Transfer learning is important for foundation models to adapt to downstream tasks. However, many foundation models are proprietary, so users must share their data with model owners…
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
Muyang Li, Ji Lin, Chenlin Meng +3
During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of com…
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
Han Cai, Junyan Li, Muyan Hu +2
High-resolution dense prediction enables many appealing real-world applications, such as computational photography, autonomous driving, etc. However, the vast computational cost ma…
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
Yihan Wang, Muyang Li, Han Cai +2
Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-cons…
Defensive Quantization: When Efficiency Meets Robustness
Ji Lin, Chuang Gan, Song Han
Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe…