GitHub - mit-han-lab/efficientvit: Efficient vision foundation models for high-resolution generation and perception. (original) (raw)
Efficient Vision Foundation Models for High-Resolution Generation and Perception
News
- (🔥 New) [2025/01/24] We released DC-AE-SANA-1.1: doc.
- (🔥 New) [2025/01/23] DC-AE and SANA are accepted by ICLR 2025.
- (🔥 New) [2025/01/14] We released DC-AE+USiT models: model, training. Using the default training settings and sampling strategy, DC-AE+USiT-2B achieves 1.72 FID on ImageNet 512x512, surpassing the SOTA diffusion model EDM2-XXL and SOTA auto-regressive image generative models (MAGVIT-v2 and MAR-L).
- (🔥 New) [2024/12/24] diffusers supports DC-AE models. All DC-AE models in diffusers safetensors are released. Usage.
- [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.
- [2024/04/06] 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.
Content
[ICLR 2025] Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models [paper] [readme] [poster]
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.
Figure 2: DC-AE speeds up latent diffusion models.
Figure 3: DC-AE enables efficient text-to-image generation on the laptop: 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**
### [CVPR 2024 eLVM Workshop] 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**
### [ICCV 2023] 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**
### [ICCV 2023] 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.
## 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
## 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} }