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Detectors

The Impact Pack's Detector includes three main types: BBOX, SEGM, and SAM.

  • The Detector detects specific regions based on the model and returns processed data in the form of SEGS.

    • SEGS is a comprehensive data format that includes information required for Detailer operations, such as masks, bbox, crop regions, confidence, label, and controlnet information.

    • Through SEGS, conditioning can be applied for Detailer[ControlNet], and SEGS can also be categorized using information such as labels or size within SEGS[SEGSFilter, Crowd Control].

    • bbox: Detected regions are represented by rectangular bounding boxes consisting of left, top, right, and bottom coordinates.

    • mask: Represents the silhouette of the object within the bbox as a mask, providing a more precise delineation of the object's area. In the case of BBOX detector, the mask area covers the entire bbox region.

    • crop region: Determines the size of the region to be cropped based on the bbox.

      • When the bbox is formed near the border, the area on the opposite side is expanded, resulting in the bbox being off-centered within the crop region.
      • Having a larger crop region provides more context for a more natural inpaint, but it also increases the time required for inpainting.

  • BBOX stands for Bounding Box, which captures detection areas as rectangular regions.

    • For example, using the bbox/face_yolov8m.pt model, you can obtain masks for the rectangular regions of faces.
    • This can be obtained using the BBOX_DETECTOR obtained through UltralyticsDetectorProvider or ONNXDetectorProvider.

  • SEGM stands for Segmentation, which captures detection areas in the form of silhouettes.

    • For instance, when using the segm/person_yolov8n-seg.pt model, you can obtain silhouette masks for human shapes.
    • This can be obtained using the SEGM_DETECTOR obtained through UltralyticsDetectorProvider.

  • SAM generates silhouette masks using the Segment Anything technique.

    • It cannot be used independently but, when used in conjunction with a BBOX model to specify the target for detection, it can create finely detailed silhouette masks for the detected objects.

Ultralytics Detector

The UltralyticsDetectorProvider node loads Ultralytics' detection models and returns either a BBOX_DETECTOR or SEGM_DETECTOR.

  • When using a model that starts with bbox/, only BBOX_DETECTOR is valid, and SEGM_DETECTOR cannot be used.
  • If using a model that starts with segm/, both BBOX_DETECTOR and SEGM_DETECTOR can be used.
  • BBOX_DETECTOR and SEGM_DETECTOR can be used with the BBOX Detector node and SEGM Detector node, respectively.

The workflow below is an example that utilizes BBOX_DETECTOR and SEGM_DETECTOR for detection. workflow

SAM Detector

The SAMDetector node loads the SAM model through the SAMLoader node for use.

  • Please note that it is not compatible with WAS-NS's SAMLoader.

  • The following workflow demonstrates the use of the BBOX Detector to generate SEGS, and then using the SAMDetector for creating more precise segments. workflow2

Simple Detector

The Simple Detector first performs primary detection using BBOX_DETECTOR and then generates SEGS using the intersected mask from the detection results of SEGM_DETECTOR or SAMDetector.

  • sam_model_opt and segm_detector_opt are both optional inputs, and if neither of them is provided, the detection results from BBOX are returned as is. When both inputs are provided, sam_model_opt takes precedence, and the segm_detector_opt input is ignored.

  • SAM has the disadvantage of requiring direct specification of the target for segmentation, but it generates more precise silhouettes compared to SEGM. The workflow below is an example of compensate BBOX with SAM and SEGM. In the case of person SEGM, it creates a silhouette that includes the entire face BBOX, resulting in a silhouette that is too rough and doesn't show significant improvement.

workflow3

Simple Detector For AnimateDiff

Simple Detector For AnimateDiff is a detector designed for video processing, such as AnimateDiff, based on the Simple Detector.

  • The basic configuration is similar to the Simple Detector, but additional features such as masking_mode and segs_pivot are provided.

  • masking_mode configures how masks are composed:

    • Pivot SEGS: Utilizes only one mask from the segs specified by segs_pivot. When used in conjunction with the Combined mask, it employs a unified mask that encompasses all frames.
    • Combine neighboring frames: Constructs each frame's mask by combining three masks, including the current frame and its adjacent frames. This method occasionally compensates for detection failures in specific frames.
    • Don't combine: Uses the original mask for each frame without combining them.

  • segs_pivot sets the mask image that serves as the basis for identifying SEGS.

    • Combined mask: Combines the masks of all frames into one frame before identifying separate masks for each SEGS.
    • 1st frame mask: Identifies separate masks for each SEGS based on the mask of the first frame.
    • Each option has its advantages and disadvantages. For instance, when 1st frame mask is selected, if the target object moves rapidly, the SEGS created based on the mask of the first frame might extend beyond its initial boundaries in later frames. Conversely, when using the Combined mask, if moving objects overlap, they may be recognized as a single object.

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