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ComfyDeploy: How Semantic-SAM works in ComfyUI?

What is Semantic-SAM?

Segment and Recognize Anything at Any Granularity.

How to install it in ComfyDeploy?

Head over to the machine page

  1. Click on the "Create a new machine" button
  2. Select the Edit build steps
  3. Add a new step -> Custom Node
  4. Search for Semantic-SAM and select it
  5. Close the build step dialig and then click on the "Save" button to rebuild the machine

ComfyUI_SemanticSAM

This is a ComfyUI node based-on Semantic-SAM official implementation. We provide a workflow node for one-click segment. Compared with SAM, Semantic-SAM has better fine-grained capabilities and more candidate masks. Users can take this node as the pre-node for inpainting to obtain the mask region.

This node have been valided on Ubuntu-20.04 & CUDA-11.8. Due to detectron2, this node is currently not supported on Windows (unless the compatibility of Detectron2 on Windows is addressed) image

Get Start

1. Prepare repo

cd ComfyUI/custom
git clone https://github.com/eastoc/ComfyUI_SemanticSAM
cd ComfyUI_SemanticSAM
git clone https://github.com/facebookresearch/detectron2
git clone https://github.com/facebookresearch/Mask2Former

Because detectron2 has not been updated, if the CUDA=11.8, you can

git clone https://github.com/johnnynunez/detectron2

or you can try other 3rd detectron2 implementation.

2. Download models

download Semantic-SAM model to "ComfyUI_SemanticSAM/ckpt"

<table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Name</th> <th valign="bottom">Training Dataset</th> <th valign="bottom">Backbone</th> <th valign="bottom">1-IoU@Multi-Granularity</th> <th valign="bottom">1-IoU@COCO(Max|Oracle)</th> <th valign="bottom">download</th> <tr><td align="left">Semantic-SAM | <a href="configs/semantic_sam_only_sa-1b_swinT.yaml">config</a></td> <td align="center">SA-1B</td> <td align="center">SwinT</td> <td align="center">88.1</td> <td align="center">54.5|73.8</td> <td align="center"><a href="https://github.com/UX-Decoder/Semantic-SAM/releases/download/checkpoint/swint_only_sam_many2many.pth">model</a></td> <tr><td align="left">Semantic-SAM | <a href="configs/semantic_sam_only_sa-1b_swinL.yaml">config</a></td> <td align="center">SA-1B</td> <td align="center">SwinL</td> <td align="center">89.0</td> <td align="center">55.1|74.1</td> <td align="center"><a href="https://github.com/UX-Decoder/Semantic-SAM/releases/download/checkpoint/swinl_only_sam_many2many.pth">model</a></td> </tbody></table>

3. Install environment

Install PyTorch & torchvision through the official method. We have been valid that from pytorch 1.13.0 to 2.3.0.

Install Semantic-SAM dependencies

pip install -r requirements.txt

Install detectron2

cd detectron2
pip install -e .
cd ..

Install Mask2Former

cd Mask2Former/mask2former/modeling/pixel_decoder/ops
sh make.sh

Workflow

one-click segment workflow is in "./workflow/workflow.json"