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!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)
- Open "Extensions" tab.
- Open "Install from URL" tab in the tab.
- Enter
https://github.com/Bing-su/adetailer.gitto "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 DON'T need to download any model from huggingface.
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 | - | - |
| mediapipe_face_short | realistic | - | - |
The yolo models can be found on huggingface Bingsu/adetailer.
Dataset
Datasets used for training the yolo face detection models are:
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 model and use only label 0.
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.
Example
Description
Languages
Python
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