Semantic-SAM
Segment and Recognize Anything at Any Granularity.
How much VRAM does Semantic-SAM require?
Direct Answer: The ComfyUI node Semantic-SAM requires a minimum base VRAM of 128MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 128MB (0.1GB)
- Recommended GPU:
- 4GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- MEDIUM
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
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What Python packages are required for Semantic-SAM?
Direct Answer: Running Semantic-SAM requires installing the following Python package dependencies: cityscapesscripts, diffdist, einops, ftfy, fvcore, json_tricks, kornia==0.6.4, mup, nltk, numpy==1.23.5, opencv-python, pandas, pillow==9.4.0, progressbar, pyarrow, pycocotools, pyyaml, regex, scann, scikit-image, scikit-learn, sentencepiece, shapely, timm==0.4.12, torch, torchmetrics==0.6.0, torchvision, transformers, vision-datasets==0.2.2, yacs. Ensure your ComfyUI environment has these packages active before launching.
cityscapesscripts
diffdist
einops
ftfy
fvcore
json_tricks
kornia==0.6.4
mup
nltk
numpy==1.23.5
opencv-python
pandas
pillow==9.4.0
progressbar
pyarrow
pycocotools
pyyaml
regex
scann
scikit-image
scikit-learn
sentencepiece
shapely
timm==0.4.12
torch
torchmetrics==0.6.0
torchvision
transformers
vision-datasets==0.2.2
yacsInteractive Setup & Dependency Resolver
# Loading command...Frequently Asked Questions
How much VRAM does Semantic-SAM require?
Semantic-SAM requires a minimum of 128MB (0.1GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run Semantic-SAM on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.3GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.5GB headroom
What PyTorch version does Semantic-SAM need?
Semantic-SAM requires the following PyTorch-related packages: torch, torchmetrics==0.6.0, torchvision. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).
What Python packages are required for Semantic-SAM?
To run Semantic-SAM, you need to install: cityscapesscripts, diffdist, einops, ftfy, fvcore, json_tricks, kornia==0.6.4, mup, nltk, numpy==1.23.5, opencv-python, pandas, pillow==9.4.0, progressbar, pyarrow, pycocotools, pyyaml, regex, scann, scikit-image, scikit-learn, sentencepiece, shapely, timm==0.4.12, torch, torchmetrics==0.6.0, torchvision, transformers, vision-datasets==0.2.2, yacs. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running Semantic-SAM?
Semantic-SAM supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.
How do I install Semantic-SAM in ComfyUI?
To install Semantic-SAM: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/eastoc/ComfyUI_SemanticSAM, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.