CezarsaintandClaude Opus 4.8
5df2cc2c4d
Previously locked_center always let the crop size follow the (smoothed) subject size, so the zoom breathed on the depth axis. Now zoom_mode=fixed picks one constant zoom level (percentile of per-frame sizes) for the whole clip -- near- edge frames still tighten via the max_width clamp so the crop never leaves the frame. zoom_mode=smooth keeps the old breathing behavior. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
FaceTag Crop (YOLO) — ComfyUI custom node
A ComfyUI custom node that uses an Ultralytics YOLO model to find a specific bbox in an image (or a batch of video frames) and crops the image centered on that detection, with temporal smoothing, normalization, and graceful fallbacks.
It outputs the cropped image, the aligned mask, and a preview with the mask drawn on top.
Install
Clone into your ComfyUI custom_nodes directory:
cd ComfyUI/custom_nodes
git clone https://github.com/adbrasi/facetaggg.git
Requires ultralytics, opencv-python, numpy, and torch (torch ships with
ComfyUI). Install ultralytics/opencv if you don't have them:
pip install ultralytics opencv-python
Put your YOLO models under ComfyUI/models/ultralytics (subfolders like
bbox/ are searched recursively). The node lists them in a dropdown.
Node: FaceTag Crop (YOLO)
Category: image/facetag
Inputs
| Input | Description |
|---|---|
image |
A single image or a batch of frames (video). |
model_name |
YOLO model from models/ultralytics (recursive). |
confidence |
Detection confidence threshold. |
padding |
Crop size as a multiple of the detected box. |
output_width / output_height |
Output resolution. |
smoothing |
Temporal smoothing (zero-phase EMA) across the batch. |
zoom_mode |
fixed (one zoom for the whole batch) or smooth. |
zoom_percentile |
Percentile used to pick the fixed zoom level. |
mask_mode |
follow_detection (steady camera, mask tracks the box), locked_center (subject nailed to center, crop auto-zooms to stay inside the frame), or full_frame (NO crop — keep the original scene and just output the face mask; padding expands the mask box, width/height/zoom are ignored). |
max_gap |
Max missing-frame gap bridged by interpolation. |
mask_grow |
Grow (+) / shrink (-) the mask before feathering, in pixels. |
mask_feather |
Gaussian blur radius on the mask edges (px). |
detector_imgsz |
Inference image size for the detector. |
batch_size |
Frames per YOLO forward pass (bigger = more GPU use / faster). |
device |
auto/cuda use the GPU when available, cpu forces CPU. |
In locked_center the mask is a single fixed size for the whole batch (it does
not breathe with the detection), so it is a stable paste target.
Outputs
- image — crop centered on the detection, at the chosen output size.
- mask — the detection as a mask, aligned to the crop.
- preview — the crop with the mask drawn as a translucent green overlay.
Robustness
- Missing detections in some frames are interpolated; gaps larger than
max_gaphold the nearest detection instead of sweeping across the frame. - If no detection is found in the whole batch, it falls back to a full-frame crop and mask (with a console warning) instead of erroring.
- The YOLO model is cached between runs; uses CUDA + FP16 when available.