/efficientvit

Efficient vision foundation models for high-resolution generation and perception.

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EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction [paper]

Efficient vision foundation models for high-resolution generation and perception.

Content

Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models [paper] [readme]

Deep Compression Autoencoder (DC-AE) is a new family of high-spatial compression autoencoders with a spatial compression ratio of up to 128 while maintaining reconstruction quality. It accelerates all latent diffusion models regardless of the diffusion model architecture.

demo

Figure 1: We address the reconstruction accuracy drop of high spatial-compression autoencoders.

demo

Figure 2: DC-AE speeds up latent diffusion models.

Figure 3: DC-AE enables efficient text-to-image generation on the laptop. For more details, please check our text-to-image diffusion model SANA.

EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss [paper] [online demo] [readme]

EfficientViT-SAM is a new family of accelerated segment anything models by replacing SAM's heavy image encoder with EfficientViT. It delivers a 48.9x measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing accuracy.

EfficientViT-Classification [paper] [readme]

Efficient image classification models with EfficientViT backbones.

EfficientViT-Segmentation [paper] [readme]

Efficient semantic segmantation models with EfficientViT backbones.

demo

EfficientViT-GazeSAM [readme]

Gaze-prompted image segmentation models capable of running in real time with TensorRT on an NVIDIA RTX 4070.

GazeSAM demo

News

If you are interested in getting updates, please join our mailing list here.

  • [2024/10/21] DC-AE and EfficientViT block are used in our latest text-to-image diffusion model SANA! Check the project page for more details.
  • [2024/10/15] We released Deep Compression Autoencoder (DC-AE): link!
  • [2024/07/10] EfficientViT is used as the backbone in Grounding DINO 1.5 Edge for efficient open-set object detection.
  • [2024/07/10] EfficientViT-SAM is used in MedficientSAM, the 1st place model in CVPR 2024 Segment Anything In Medical Images On Laptop Challenge.
  • [2024/07/10] An FPGA-based accelerator for EfficientViT: link.
  • [2024/04/23] We released the training code of EfficientViT-SAM.
  • [2024/04/06] EfficientViT-SAM is accepted by eLVM@CVPR'24.
  • [2024/03/19] Online demo of EfficientViT-SAM is available: https://evitsam.hanlab.ai/.
  • [2024/02/07] We released EfficientViT-SAM, the first accelerated SAM model that matches/outperforms SAM-ViT-H's zero-shot performance, delivering the SOTA performance-efficiency trade-off.
  • [2023/11/20] EfficientViT is available in the NVIDIA Jetson Generative AI Lab.
  • [2023/09/12] EfficientViT is highlighted by MIT home page and MIT News.
  • [2023/07/18] EfficientViT is accepted by ICCV 2023.

Getting Started

conda create -n efficientvit python=3.10
conda activate efficientvit
pip install -U -r requirements.txt

Third-Party Implementation/Integration

Contact

Han Cai

Reference

If EfficientViT or EfficientViT-SAM or DC-AE is useful or relevant to your research, please kindly recognize our contributions by citing our paper:

@inproceedings{cai2023efficientvit,
  title={Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction},
  author={Cai, Han and Li, Junyan and Hu, Muyan and Gan, Chuang and Han, Song},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={17302--17313},
  year={2023}
}
@article{zhang2024efficientvit,
  title={EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss},
  author={Zhang, Zhuoyang and Cai, Han and Han, Song},
  journal={arXiv preprint arXiv:2402.05008},
  year={2024}
}
@article{chen2024deep,
  title={Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models},
  author={Chen, Junyu and Cai, Han and Chen, Junsong and Xie, Enze and Yang, Shang and Tang, Haotian and Li, Muyang and Lu, Yao and Han, Song},
  journal={arXiv preprint arXiv:2410.10733},
  year={2024}
}