ComfyUI DWpose Deluxe

Pre-release sneak-peek

A pimped up custom node for ComfyUI that integrates the DW-Pose (Denoising Whole-Body Pose Estimation) model for high-quality pose detection. This node supports both CPU (ONNX) and high-performance GPU (TensorRT) execution, with automatic model downloading and engine building. Runs on average 20x faster than pose estimators without TensorRT engine booster. Added functionality for drawing feet, composite image with optional frame count for pose debugging, json dataset output and json converter node.

Key Features

• Dual Execution Providers: Choose between CPU (ONNX) for broad compatibility and GPU (TensorRT) for maximum performance.

• Automatic Setup:

  • Automatically downloads the required ONNX models on first use.
  • Automatically builds and caches optimized TensorRT engines when the GPU provider is selected for the first time.

• Modular Workflow: Comes with three distinct nodes for a flexible and powerful workflow:

  • DWposeDeluxeNode: The core node for performing pose estimation.
  • DWposeDeluxe Weight Options: Fine-tune the visual style of the rendered pose skeleton, such as dot size and line thickness.
  • DWposeDeluxe Keypoint Converter: A utility node to convert keypoint data between absolute (pixel) and normalized (percentage) coordinates.

• Rich I/O:

  • pose_image: The generated pose skeleton on a black background.
  • blend_image: A 50/50 blend of the source image and the pose image.
  • source_image: A passthrough of the original input image.
  • audio: A passthrough for the audio channel from video inputs.
  • frame_rate: The frame rate derived from the video_info input.
  • keypoints: Raw pose keypoints in a structured JSON format.

• Advanced Customization:

  • Toggle visibility for face, hands, and feet in the final pose.
  • Save generated keypoint data directly to your ComfyUI output directory.
  • Add a frame number overlay to batch outputs, helpful by pose debugging.

• Video Workflow Ready: Includes audio and video_info input, frame_count and frame_rate outputs, source_image and audio pass-through to seamlessly integrate with video load/combine nodes.

Full Pose With Feet (optional)

Installation

1. Navigate to your ComfyUI installation directory.

2. Clone this repository into the custom_nodes folder:

   cd ComfyUI/custom_nodes/
   git clone https://github.com/hobinrude/ComfyUI_DWposeDeluxe
   

3. Navigate into the newly cloned directory:

   cd ComfyUI_DWposeDeluxe
   

4. Install the required dependencies: ( assumes CUDA 12.X by default )

   pip install -r requirements.txt
   

5. Start or restart ComfyUI.

How to Use

1. In ComfyUI, add the DWposeDeluxeNode to your workflow.

2. Connect an image source (e.g., from a Load Image or Load Video node) to the image input.

3. Select the provider_type:

   • CPU: Uses the ONNX runtime. Models will be downloaded automatically if not found in ComfyUI/models/dwpose/.
   • GPU: Uses the TensorRT runtime. The first time you select this, the node will build optimized .trt engine files. This may take several (5-6) minutes, but subsequent runs will be significantly faster - 20x the speed of regular .onnx models.

4. (Optional) Add the DWposeDeluxe Weight Options node and connect its options output to the weight_options input on the main node to customize the appearance of the pose by setting modifier values for dot size and bone thickness.

5. (Optional) Connect the keypoints output to the DWposeDeluxe Keypoint Converter node to transform the keypoint data if downstream nodes require different formats.

6. Set poses_to_detect to a specific number to eliminate detections of unwanted background photo-bombers. This works based on bounding box size so will only detect X biggest poses in each frame.

Basic Example Workflow Included

Dependencies

This node requires the following Python libraries:

tensorrt-cu12
onnxruntime-gpu
opencv-python
matplotlib
polygraphy
colored

These can be installed by running pip install -r requirements.txt from within the node's directory. If you're using CUDA 11.X or 13.X modify the TensorRT version in requirements.txt accordingly.

or install manually with: pip install tensorrt-cu11 pis install tensorrt-cu12 pip install tensorrt-cu13

To use without GPU support a CPU onnxruntime is needed. Manually change it in requirements.txt.

Acknowledgements & License

This project is heavily based on the work of yuvraj108c and his original ComfyUI-Dwpose-Tensorrt repository. Big chunks of code have been repurposed straight from his project.

If you like this node pack and find it useful, please consider giving a star to the original repository and buying the original author a coffee, not me!

Licensing Notice

Due to the "ShareAlike" clause in the original project's license, this project is also licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

This means:

• You must give appropriate credit (Attribution).
• You may not use this material for commercial purposes (NonCommercial).
• If you remix, transform, or build upon the material, you must distribute your contributions under the same license (ShareAlike).

Enjoy Responsibly