!After Detailer

!After Detailer is a extension for stable diffusion webui, similar to Detection Detailer, except it uses ultralytics instead of the mmdet.

Install

(from Mikubill/sd-webui-controlnet)

1. Open "Extensions" tab.
2. Open "Install from URL" tab in the tab.
3. Enter https://github.com/Bing-su/adetailer.git to "URL for extension's git repository".
4. Press "Install" button.
5. Wait 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\adetailer. Use Installed tab to restart".
6. 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.)
7. 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 DON'T need to download any model from huggingface.

Usage

TO DO

ControlNet Inpainting

You can use the ControlNet inpaint extension if you have ControlNet installed and a ControlNet inpaint model.

On the ControlNet tab, select a ControlNet inpaint model and set the model weights.

Model

Model	Target	mAP 50	mAP 50-95
face_yolov8n.pt	2D / realistic face	0.660	0.366
face_yolov8s.pt	2D / realistic face	0.713	0.404
mediapipe_face_full	realistic face	-	-
mediapipe_face_short	realistic face	-	-
hand_yolov8n.pt	2D / realistic hand	0.767	0.505
person_yolov8n-seg.pt	2D / realistic person	0.782 (bbox) 0.761 (mask)	0.555 (bbox) 0.460 (mask)
person_yolov8s-seg.pt	2D / realistic person	0.824 (bbox) 0.809 (mask)	0.605 (bbox) 0.508 (mask)

The yolo models can be found on huggingface Bingsu/adetailer.

User Model

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.

Dataset

Datasets used for training the yolo models are:

Face

• Anime Face CreateML
• xml2txt
• AN
• wider face

Hand

• AnHDet
• hand-detection-fuao9

Person

• coco2017 (only person)
• AniSeg
• skytnt/anime-segmentation

Example