ComfyUI YOLOv8 Object Detection Node
A powerful ComfyUI custom node that performs object detection using YOLOv8 and outputs individual images for each detected object. Perfect for automatic mask generation, object isolation, and batch processing workflows.
How much VRAM does ComfyUI YOLOv8 Object Detection Node require?
Direct Answer: The ComfyUI node ComfyUI YOLOv8 Object Detection Node 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 ComfyUI YOLOv8 Object Detection Node?
Direct Answer: Running ComfyUI YOLOv8 Object Detection Node requires installing the following Python package dependencies: numpy>=1.21.0, opencv-python>=4.5.0, pillow>=8.0.0, torch>=1.9.0, torchvision>=0.10.0, ultralytics>=8.0.0. This node specifically requires PyTorch version 1.9.0 or newer.
numpy>=1.21.0
opencv-python>=4.5.0
pillow>=8.0.0
torch>=1.9.0
torchvision>=0.10.0
ultralytics>=8.0.0Interactive Setup & Dependency Resolver
# Loading command...Special Environment Requirements:
Requires PyTorch version 1.9.0+.
# Loading PyTorch command...Frequently Asked Questions
How much VRAM does ComfyUI YOLOv8 Object Detection Node require?
ComfyUI YOLOv8 Object Detection Node 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 ComfyUI YOLOv8 Object Detection Node 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 ComfyUI YOLOv8 Object Detection Node need?
ComfyUI YOLOv8 Object Detection Node requires the following PyTorch-related packages: torch>=1.9.0, torchvision>=0.10.0. 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 ComfyUI YOLOv8 Object Detection Node?
To run ComfyUI YOLOv8 Object Detection Node, you need to install: numpy>=1.21.0, opencv-python>=4.5.0, pillow>=8.0.0, torch>=1.9.0, torchvision>=0.10.0, ultralytics>=8.0.0. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running ComfyUI YOLOv8 Object Detection Node?
ComfyUI YOLOv8 Object Detection Node 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 ComfyUI YOLOv8 Object Detection Node in ComfyUI?
To install ComfyUI YOLOv8 Object Detection Node: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/mangobyed/ComfyUI_Detection_List, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.