!After Detailer
!After Detailer is a extension for stable diffusion webui, similar to Detection Detailer, except it uses ultralytics instead of the mmdet.
(from Mikubill/sd-webui-controlnet)
Open "Extensions" tab.
Open "Install from URL" tab in the tab.
Enter?https://github.com/Bing-su/adetailer.git?to "URL for extension's git repository".
Press "Install" button.
Wait 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\adetailer. Use Installed tab to restart".
Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use this method to update extensions.)
Completely restart A1111 webui including your terminal. (If you do not know what is a "terminal", you can reboot your computer: turn your computer off and turn it on again.)
You can now install it directly from the Extensions tab.
You?DON'T?need to download any model from huggingface.
Model, Prompts
ADetailer modelDetermine what to detect.None= disable
ADetailer prompt,?negative promptPrompts and negative prompts to applyIf left blank, it will use the same as the input.
Detection
Detection model confidence thresholdOnly objects with a detection model confidence above this threshold are used for inpainting.
Mask min/max ratioOnly use masks whose area is between those ratios for the area of the entire image.
If you want to exclude objects in the background, try setting the min ratio to around?0.01.
Mask Preprocessing
Mask x, y offsetMoves the mask horizontally and vertically by
Mask erosion (-) / dilation (+)Enlarge or reduce the detected mask.opencv example
Mask merge modeNone: Inpaint each mask
Merge: Merge all masks and inpaint
Merge and Invert: Merge all masks and Invert, then inpaint
Applied in this order: x, y offset → erosion/dilation → merge/invert.
Each option corresponds to a corresponding option on the inpaint tab.
You can use the ControlNet extension if you have ControlNet installed and ControlNet models.
Support?inpaint, scribble, lineart, openpose, tile?controlnet models. Once you choose a model, the preprocessor is set automatically.
ModelTargetmAP 50mAP 50-95
face_yolov8n.pt2D / realistic face0.6600.366
face_yolov8s.pt2D / realistic face0.7130.404
hand_yolov8n.pt2D / realistic hand0.7670.505
person_yolov8n-seg.pt2D / realistic person0.782 (bbox)
0.761 (mask)
0.555 (bbox)
0.460 (mask)
person_yolov8s-seg.pt2D / realistic person0.824 (bbox)
0.809 (mask)
0.605 (bbox)
0.508 (mask)
mediapipe_face_fullrealistic face--
mediapipe_face_shortrealistic face--
mediapipe_face_meshrealistic face--
The yolo models can be found on huggingface?Bingsu/adetailer.
Put your?ultralytics?model in?webui/models/adetailer. The model name should end with?.pt?or?.pth.
It must be a bbox detection or segment model and use all label.
Datasets used for training the yolo models are:
coco2017?(only person)
?
