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.

Updates

• We add UViT-2B trained with DC-AE-f64p1 on ImageNet 512x512: link.

• We add model scaling results on ImageNet 512x512 with UViT variants (UViT-S -> UViT-2B). DC-AE-f64 benefits more from scaling up than SD-VAE-f8:

Demo

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

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.

• Usage of Deep Compression Autoencoder
• Usage of DC-AE-Diffusion
• Evaluate Deep Compression Autoencoder
• Demo DC-AE-Diffusion Models
• Evaluate DC-AE-Diffusion Models
• Train DC-AE-Diffusion Models
• Reference

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.

• Pretrained EfficientViT-SAM Models
• Usage of EfficientViT-SAM
• Evaluate EfficientViT-SAM
• Visualize EfficientViT-SAM
• Deploy EfficientViT-SAM
• Train EfficientViT-SAM
• Reference

EfficientViT-Classification [paper] [readme]

Efficient image classification models with EfficientViT backbones.

• Pretrained EfficientViT Classification Models
• Usage of EfficientViT Classification Models
• Evaluate EfficientViT Classification Models
• Export EfficientViT Classification Models
• Train EfficientViT Classification Models
• Reference

EfficientViT-Segmentation [paper] [readme]

Efficient semantic segmantation models with EfficientViT backbones.

• Pretrained EfficientViT Segmentation Models
• Usage of EfficientViT Segmentation Models
• Evaluate EfficientViT Segmentation Models
• Visualize EfficientViT Segmentation Models
• Export EfficientViT Segmentation Models
• Reference

EfficientViT-GazeSAM [readme]

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

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

• NVIDIA Jetson Generative AI Lab
• timm: link
• X-AnyLabeling: link
• Grounding DINO 1.5 Edge: link

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}
}