base on Efficient vision foundation models for high-resolution generation and perception. # Efficient Vision Foundation Models for High-Resolution Generation and Perception [](https://paperswithcode.com/sota/image-generation-on-imagenet-512x512?p=deep-compression-autoencoder-for-efficient) ## News - (🔥 New) [2025/01/24] We released DC-AE-SANA-1.1: [doc](https://github.com/mit-han-lab/efficientvit/blob/master/assets/docs/dc_ae_sana_1.1.md). - (🔥 New) [2025/01/23] DC-AE and SANA are accepted by ICLR 2025. - (🔥 New) [2025/01/14] We released **DC-AE+USiT models**: [model](https://huggingface.co/collections/mit-han-lab/dc-ae-diffusion-670dbb8d6b6914cf24c1a49d), [training](https://github.com/mit-han-lab/efficientvit/blob/master/applications/dc_ae/README.md#dc-ae--usit). 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](https://huggingface.co/collections/mit-han-lab/dc-ae-670085b9400ad7197bb1009b) are released. [Usage](https://github.com/mit-han-lab/efficientvit/tree/master/applications/dc_ae#deep-compression-autoencoder-diffusers). - [2024/10/21] DC-AE and EfficientViT block are used in our latest text-to-image diffusion model SANA! Check the [project page](https://nvlabs.github.io/Sana/) for more details. - [2024/10/15] We released **Deep Compression Autoencoder (DC-AE)**: [link](#deep-compression-autoencoder-for-efficient-high-resolution-diffusion-models-paper-readme)! - [2024/07/10] EfficientViT is used as the backbone in [Grounding DINO 1.5 Edge](https://arxiv.org/pdf/2405.10300) for efficient open-set object detection. - [2024/07/10] EfficientViT-SAM is used in [MedficientSAM](https://github.com/hieplpvip/medficientsam), the 1st place model in [CVPR 2024 Segment Anything In Medical Images On Laptop Challenge](https://www.codabench.org/competitions/1847/). - [2024/04/06] EfficientViT-SAM is accepted by [eLVM@CVPR'24](https://sites.google.com/view/elvm/home?authuser=0). - [2024/03/19] Online demo of EfficientViT-SAM is available: [https://evitsam.hanlab.ai/](https://evitsam.hanlab.ai/). - [2024/02/07] We released [EfficientViT-SAM](https://arxiv.org/abs/2402.05008), 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](https://www.jetson-ai-lab.com/tutorial_efficientvit.html). - [2023/09/12] EfficientViT is highlighted by [MIT home page](https://www.mit.edu/archive/spotlight/efficient-computer-vision/) and [MIT News](https://news.mit.edu/2023/ai-model-high-resolution-computer-vision-0912). - [2023/07/18] EfficientViT is accepted by ICCV 2023. ## Content ### [ICLR 2025] Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models [[paper](https://arxiv.org/abs/2410.10733)] [[readme](applications/dc_ae/README.md)] [[poster](assets/dc_ae_poster.pdf)] **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  <p align="center"> <b> Figure 1: We address the reconstruction accuracy drop of high spatial-compression autoencoders. </p>  <p align="center"> <b> Figure 2: DC-AE speeds up latent diffusion models. </p> <p align="center"> <img src="https://huggingface.co/mit-han-lab/dc-ae-f64c128-in-1.0/resolve/main/assets/dc_ae_sana.jpg" width="1200"> </p> <p align="center"> <b> Figure 3: DC-AE enables efficient text-to-image generation on the laptop: <a href="https://nvlabs.github.io/Sana/">SANA</a>. </p> - [Usage of Deep Compression Autoencoder](applications/dc_ae/README.md#deep-compression-autoencoder) - [Usage of DC-AE-Diffusion](applications/dc_ae/README.md#efficient-diffusion-models-with-dc-ae) - [Evaluate Deep Compression Autoencoder](applications/dc_ae/README.md#evaluate-deep-compression-autoencoder) - [Demo DC-AE-Diffusion Models](applications/dc_ae/README.md#demo-dc-ae-diffusion-models) - [Evaluate DC-AE-Diffusion Models](applications/dc_ae/README.md#evaluate-dc-ae-diffusion-models) - [Train DC-AE-Diffusion Models](applications/dc_ae/README.md#train-dc-ae-diffusion-models) - [Reference](applications/dc_ae/README.md#reference) ### [CVPR 2024 eLVM Workshop] EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss [[paper](https://arxiv.org/abs/2402.05008)] [[online demo](https://evitsam.hanlab.ai/)] [[readme](applications/efficientvit_sam/README.md)] **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.** <p align="left"> <img src="https://huggingface.co/mit-han-lab/efficientvit-sam/resolve/main/sam_zero_shot_coco_mAP.png" width="500"> </p> - [Pretrained EfficientViT-SAM Models](applications/efficientvit_sam/README.md#pretrained-efficientvit-sam-models) - [Usage of EfficientViT-SAM](applications/efficientvit_sam/README.md#usage) - [Evaluate EfficientViT-SAM](applications/efficientvit_sam/README.md#evaluation) - [Visualize EfficientViT-SAM](applications/efficientvit_sam/README.md#visualization) - [Deploy EfficientViT-SAM](applications/efficientvit_sam/README.md#deployment) - [Train EfficientViT-SAM](applications/efficientvit_sam/README.md#training) - [Reference](applications/efficientvit_sam/README.md#reference) ### [ICCV 2023] EfficientViT-Classification [[paper](https://arxiv.org/abs/2205.14756)] [[readme](applications/efficientvit_cls/README.md)] **Efficient image classification models with EfficientViT backbones.** <p align="left"> <img src="https://huggingface.co/han-cai/efficientvit-cls/resolve/main/efficientvit_cls_results.png" width="600"> </p> - [Pretrained EfficientViT Classification Models](applications/efficientvit_cls/README.md#pretrained-efficientvit-classification-models) - [Usage of EfficientViT Classification Models](applications/efficientvit_cls/README.md#usage) - [Evaluate EfficientViT Classification Models](applications/efficientvit_cls/README.md#evaluation) - [Export EfficientViT Classification Models](applications/efficientvit_cls/README.md#export) - [Train EfficientViT Classification Models](applications/efficientvit_cls/README.md#training) - [Reference](applications/efficientvit_cls/README.md#reference) ### [ICCV 2023] EfficientViT-Segmentation [[paper](https://arxiv.org/abs/2205.14756)] [[readme](applications/efficientvit_seg/README.md)] **Efficient semantic segmantation models with EfficientViT backbones.**  - [Pretrained EfficientViT Segmentation Models](applications/efficientvit_seg/README.md#pretrained-efficientvit-segmentation-models) - [Usage of EfficientViT Segmentation Models](applications/efficientvit_seg/README.md#usage) - [Evaluate EfficientViT Segmentation Models](applications/efficientvit_seg/README.md#evaluation) - [Visualize EfficientViT Segmentation Models](applications/efficientvit_seg/README.md#visualization) - [Export EfficientViT Segmentation Models](applications/efficientvit_seg/README.md#export) - [Reference](applications/efficientvit_seg/README.md#reference) ### EfficientViT-GazeSAM [[readme](applications/efficientvit_gazesam/README.md)] **Gaze-prompted image segmentation models capable of running in real time with TensorRT on an NVIDIA RTX 4070.**  ## Getting Started ```bash 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](https://www.jetson-ai-lab.com/tutorial_efficientvit.html) - [timm](https://github.com/huggingface/pytorch-image-models): [link](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/efficientvit_mit.py) - [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling): [link](https://github.com/CVHub520/X-AnyLabeling/blob/main/anylabeling/services/auto_labeling/efficientvit_sam.py) - [Grounding DINO 1.5 Edge](https://github.com/IDEA-Research/Grounding-DINO-1.5-API): [link](https://arxiv.org/pdf/2405.10300) ## Contact [Han Cai](http://hancai.ai/) ## 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: ```bibtex @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} } ``` ```bibtex @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} } ``` ```bibtex @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} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"2076","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"