| --- |
| license: apache-2.0 |
| pipeline_tag: mask-generation |
| library_name: mlx |
| tags: |
| - mlx |
| - moonbox |
| - efficientvit |
| - efficientvit-sam |
| - segment-anything |
| - image-segmentation |
| --- |
| |
| # EfficientViT-SAM MLX |
|
|
| MLX builds of [EfficientViT-SAM](https://github.com/mit-han-lab/efficientvit), made by Moonbox for Apple Silicon Macs. Each precision is a folder; keep a folder's |
| files together. Only the parts listed below are converted: the rest of the pipeline comes from the original |
| repository unless the section below says otherwise. |
|
|
| ## Files |
|
|
| The last column is how far each build is from the original PyTorch model on the same inputs (see the section below |
| for what was measured). |
|
|
| | Precision | File | Size | sha256 | Checked against the original | |
| |---|---|---|---|---| |
| | bf16 | [`bf16/config.json`](https://huggingface.co/moonbox-ai/efficientvit-MLX/blob/main/bf16/config.json) | 0.3 kB | `9e98792690f7` | below threshold (experimental): mask IoU 0.714 mean / 0.091 worst | |
| | bf16 | [`bf16/mlx_model.py`](https://huggingface.co/moonbox-ai/efficientvit-MLX/blob/main/bf16/mlx_model.py) | 25.4 kB | `42cea84fb312` | below threshold (experimental): mask IoU 0.714 mean / 0.091 worst | |
| | bf16 | [`bf16/model.safetensors`](https://huggingface.co/moonbox-ai/efficientvit-MLX/blob/main/bf16/model.safetensors) | 122.7 MB | `e7acc4ef2253` | below threshold (experimental): mask IoU 0.714 mean / 0.091 worst | |
| | fp16 | [`fp16/config.json`](https://huggingface.co/moonbox-ai/efficientvit-MLX/blob/main/fp16/config.json) | 0.3 kB | `9b286f991620` | below threshold (experimental): mask IoU 0.942 mean / 0.611 worst (float32 port: 0.9996 / 0.997) | |
| | fp16 | [`fp16/mlx_model.py`](https://huggingface.co/moonbox-ai/efficientvit-MLX/blob/main/fp16/mlx_model.py) | 25.4 kB | `42cea84fb312` | below threshold (experimental): mask IoU 0.942 mean / 0.611 worst (float32 port: 0.9996 / 0.997) | |
| | fp16 | [`fp16/model.safetensors`](https://huggingface.co/moonbox-ai/efficientvit-MLX/blob/main/fp16/model.safetensors) | 122.7 MB | `f4b4b159c9f7` | below threshold (experimental): mask IoU 0.942 mean / 0.611 worst (float32 port: 0.9996 / 0.997) | |
|
|
| ## How to run |
|
|
| EfficientViT-SAM **L2** (EfficientViT-L2 image encoder at 512 px + SAM's prompt encoder and mask decoder) ported to |
| MLX. Each folder holds `model.safetensors`, `config.json` and `mlx_model.py`, the port (only `mlx`, `numpy` and |
| `pillow` needed), with EfficientViTSamPredictor's interface: |
|
|
| ```python |
| import numpy as np, mlx_model |
| from PIL import Image |
| model = mlx_model.load("fp16") |
| rgb = np.array(Image.open("in.jpg").convert("RGB")) |
| emb = mlx_model.embed(model, rgb) |
| masks, scores, low_res, logits = mlx_model.predict(model, emb, points=[[320, 240]], labels=[1], multimask=True) |
| ``` |
|
|
| **Both folders are experimental: this model needs float32.** Thirty of its 1x1 convolutions are followed by a |
| BatchNorm with a running variance down to 5.5e-7 (scales up to 600): those channels carry their signal in the 4th-5th |
| significant digit of the convolution output, which 16-bit weights or arithmetic can't hold. The port itself is exact: |
| run in float32 (with `MLX_ENABLE_TF32=0`, else MLX's float32 matmuls use TF32) it matches PyTorch to mask IoU 0.9996. |
| There are no 8bit / 6bit / 4bit folders: quantized, the model gives no usable masks. |
|
|
| | Precision | Size | Mask IoU vs PyTorch fp32 (mean / worst) | Status | |
| |---|---|---|---| |
| | `fp16/` | 122.7 MB | 0.942 / 0.611 | **below threshold (0.95), experimental** | |
| | `bf16/` | 122.7 MB | 0.714 / 0.091 | **below threshold, experimental** | |
|
|
| To run in float32, load either folder and cast the parameters up (`model.update(mlx.utils.tree_map(lambda a: |
| a.astype(mx.float32), model.parameters()))`): fp16 weights in float32 give 0.966 mean / 0.62 worst, still short of |
| the original's float32 weights. About 55 ms per image (encoder + 2 prompts) on an Apple M5. |
| |
| ## How these were made |
| |
| From [EfficientViT-SAM](https://github.com/mit-han-lab/efficientvit) at revision `de7d7733cc`: the original weights loaded in MLX and stored as bf16 or fp16, or |
| quantized with MLX's affine quantization (`8bit`, `6bit`, `4bit`, group size 64; layers MLX can't quantize, such as |
| convolutions and norms, stay bf16). Every build was checked against the original before upload; the `sha256` column |
| above is what the files must hash to. |
| |
| ## License |
| |
| apache-2.0. These files are a derivative of the original model and carry its license: it is copied in this |
| repository, and it governs your use of these files. The original model card follows unchanged. |
| |
| --- |
| |
| # Original model card |
| |
| # 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) [2025/09/05] We will no longer maintain this codebase. All future updates and announcements will be made on [DC-Gen](https://github.com/dc-ai-projects/DC-Gen). |
| - (🔥 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} |
| } |
| ``` |
|
|
