ComfyUI YoloNasPose Tensorrt

By yuvraj108cView on GitHub →

This repo provides a ComfyUI Custom Node implementation of [a/YOLO-NAS-POSE](https://github.com/Deci-AI/super-gradients), powered by TensorRT for ultra fast pose estimation. It has been adapted to work with openpose controlnet (experimental)

How much VRAM does ComfyUI YoloNasPose Tensorrt require?

Direct Answer: The ComfyUI node ComfyUI YoloNasPose Tensorrt requires a minimum base VRAM of 2048MB and is optimized for GPUs with at least 6GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.

High (2-4GB)
Base VRAM:
2048MB (2.0GB)
Recommended GPU:
6GB+ VRAM
Low VRAM Mode:
✓ Supported
Estimation Confidence:
MEDIUM

Interactive VRAM Compatibility Estimator

Estimated Total VRAM: 3.00 GBTarget: 8 GB
✅ Comfortable Fit

Your GPU has plenty of headroom. You can run this node safely with your active configurations!

Deploy on High-Performance GPUs

Need more VRAM to run ComfyUI with this node? Rent low-cost, high-performance GPUs on Vast.ai instantly.

🚀 Deploy on Vast.ai

Deploy on Cloud GPUs

Need more VRAM to run ComfyUI with this node? Rent low-cost, high-performance cloud GPUs on RunPod instantly.

🚀 Deploy on RunPod

What Python packages are required for ComfyUI YoloNasPose Tensorrt?

Direct Answer: Running ComfyUI YoloNasPose Tensorrt requires installing the following Python package dependencies: matplotlib, opencv-python, polygraphy, tensorrt. Ensure your ComfyUI environment has these packages active before launching.

requirements.txt
matplotlib
opencv-python
polygraphy
tensorrt

Interactive Setup & Dependency Resolver

Operating System:
Environment Type:
Run this terminal command in your ComfyUI root folder:
# Loading command...

Related Custom Nodes

The following custom nodes run in the same PyTorch / CUDA environment and share similar categories or workflows:

ComfyUI Depth Anything TensorRT
by yuvraj108c128MB VRAM
ComfyUI Dwpose TensorRT
by yuvraj108c128MB VRAM
ComfyUI Video Depth Anything
by yuvraj108c4.0GB VRAM
ComfyUI-Light-N-Color
by KAVVATARE128MB VRAM
AlekPet/ComfyUI_Custom_Nodes_AlekPet
by AlekPet256MB VRAM

Frequently Asked Questions

How much VRAM does ComfyUI YoloNasPose Tensorrt require?

ComfyUI YoloNasPose Tensorrt requires a minimum of 2048MB (2.0GB) of VRAM for base operation. For optimal performance, a GPU with at least 6GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.

Can I run ComfyUI YoloNasPose Tensorrt on an RTX 3060, RTX 4070, or RTX 4090?

✅ RTX 3060 (12GB): Yes, fully compatible with 8.8GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 8.8GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 12.4GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 19.6GB headroom

What Python packages are required for ComfyUI YoloNasPose Tensorrt?

To run ComfyUI YoloNasPose Tensorrt, you need to install: matplotlib, opencv-python, polygraphy, tensorrt. You can install these using pip or add them to your requirements.txt file.

How can I reduce VRAM usage when running ComfyUI YoloNasPose Tensorrt?

ComfyUI YoloNasPose Tensorrt 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 YoloNasPose Tensorrt in ComfyUI?

To install ComfyUI YoloNasPose Tensorrt: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/yuvraj108c/ComfyUI-YoloNasPose-Tensorrt, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.