!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.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 can now install it directly from the Extensions tab.
You DON'T need to download any model from huggingface.
| Model, Prompts | ||
|---|---|---|
| ADetailer model | Determine what to detect. | None = disable |
| ADetailer prompt, negative prompt | Prompts and negative prompts to apply | If left blank, it will use the same as the input. |
| Detection | ||
|---|---|---|
| Detection model confidence threshold | Only objects with a detection model confidence above this threshold are used for inpainting. | |
| Mask min/max ratio | Only 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 offset | Moves the mask horizontally and vertically by | |
| Mask erosion (-) / dilation (+) | Enlarge or reduce the detected mask. | opencv example |
| Mask merge mode | None: Inpaint each maskMerge: Merge all masks and inpaintMerge and Invert: Merge all masks and Invert, then inpaint |
Each option corresponds to a corresponding option on the inpaint tab.
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 | 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.
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)
- AniSeg
- skytnt/anime-segmentation
