Introduction:
- ComfyUI-Impact-Pack Repository
- Stars: 2500
- Author: ltdrdata
This document provides an overview of the ONNXDetectorProvider
node within the ComfyUI-Impact-Pack, a custom node pack designed to enhance image processing workflows in ComfyUI. The Impact Pack offers a suite of tools focusing on detection, detailing, upscaling, and more. This document specifically focuses on the ONNXDetectorProvider
.
ComfyUI-Impact-Pack Introduction:
The ComfyUI-Impact-Pack is a collection of custom nodes for ComfyUI, aiming to simplify and enhance image manipulation workflows. It provides tools for:
- Detection: Identifying objects within images.
- Detailing: Enhancing the details of specific regions in images.
- Upscaling: Increasing the resolution of images.
- Pipes: Streamlining complex workflows.
ONNXDetectorProvider Introduction:
The ONNXDetectorProvider
node allows users to load and utilize ONNX-based object detection models within ComfyUI. ONNX (Open Neural Network Exchange) is an open standard for representing machine learning models, enabling interoperability between different frameworks. This node provides a way to integrate pre-trained ONNX detection models into ComfyUI workflows.
ONNXDetectorProvider Input:
The ONNXDetectorProvider
node accepts the following input:
model_name
: (Dropdown List) - A selection of available ONNX models listed in the "onnx" folder within the ComfyUI models directory. The models listed will be the files found usingfolder_paths.get_filename_list("onnx")
.
ONNXDetectorProvider Output:
The ONNXDetectorProvider
node outputs the following:
BBOX_DETECTOR
: An object that represents the loaded ONNX object detection model. This output can be connected to other nodes that require a bounding box detector, such asONNXDetectorForEach
.
ONNXDetectorProvider Usage Tips:
- Model Placement: Ensure that your ONNX models are placed in the correct directory (typically
ComfyUI/models/onnx
) so that they appear in themodel_name
dropdown list. The code explicitly usesos.path.join(model_path, "onnx")
and adds the.onnx
extension. - Dependency: This node relies on the
impact.core.ONNXDetector
class. Ensure that the Impact Pack is correctly installed and that the necessary dependencies for ONNX inference are met. - Versatility: The
BBOX_DETECTOR
output can be used with various downstream nodes within the Impact Pack to perform tasks such as object detection, segmentation, and detailing on specific regions of an image. - Integration: The
ONNXDetectorProvider
is often used in conjunction with nodes likeONNXDetectorForEach
to apply the detection model to an image and process the detected regions.