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docs: changelog, install
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CHANGELOG.md
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# Changelog
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### 2023-05-05 (어린이날)
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- v23.5.5
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- `Save images before ADetailer` 옵션 추가
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- 입력으로 들어온 인자와 ALL_ARGS의 길이가 다르면 에러메세지
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- README.md에 설치방법 추가
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### 2023-05-04
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- v23.5.4
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- use pydantic for arguments validation
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- revert: ad_model to `None` as default
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- revert: `__future__` imports
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- lazily import yolo and mediapipe
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### 2023-05-03
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- v23.5.3.post0
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- remove `__future__` imports
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- change to copy scripts and scripts args
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- v23.5.3.post1
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- change default ad_model from `None`
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### 2023-05-02
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- v23.5.3
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- Remove `None` from model list and add `Enable ADetailer` checkbox.
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- install.py `skip_install` fix.
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README.md
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README.md
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!After Detailer is a extension for stable diffusion webui, similar to Detection Detailer, except it uses ultralytics instead of the mmdet.
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!After Detailer is a extension for stable diffusion webui, similar to Detection Detailer, except it uses ultralytics instead of the mmdet.
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## Install
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(from Mikubill/sd-webui-controlnet)
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1. Open "Extensions" tab.
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2. Open "Install from URL" tab in the tab.
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3. Enter `https://github.com/Bing-su/adetailer.git` to "URL for extension's git repository".
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4. Press "Install" button.
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5. Wait 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\adetailer. Use Installed tab to restart".
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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.)
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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.)
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You **DON'T** need to download any model from huggingface.
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## Model
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## Model
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| Model | Target | mAP 50 | mAP 50-95 |
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| Model | Target | mAP 50 | mAP 50-95 |
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The yolo models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer).
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The yolo models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer).
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You DON'T need to download models from huggingface.
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### Dataset
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### Dataset
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Datasets used for training the yolo face detection models are:
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Datasets used for training the yolo face detection models are:
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@@ -40,28 +52,3 @@ On the ControlNet tab, select a ControlNet inpaint model and set the model weigh
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## Changelog
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### 2023-05-04
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- v23.5.4
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- use pydantic for arguments validation
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- revert: ad_model to `None` as default
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- revert: `__future__` imports
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- lazily import yolo and mediapipe
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### 2023-05-03
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- v23.5.3.post0
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- remove `__future__` imports
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- change to copy scripts and scripts args
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- v23.5.3.post1
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- change default ad_model from `None`
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### 2023-05-02
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- v23.5.3
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- Remove `None` from model list and add `Enable ADetailer` checkbox.
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- install.py `skip_install` fix.
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