Efficient Vision Foundation Models for High-Resolution Generation and Perception

News

• (🔥 New) [2025/09/05] We will no longer maintain this codebase. All future updates and announcements will be made on DC-Gen.
• (🔥 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.

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

@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}
}