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# BrushNet

This repository contains a fork of the implementation of the paper "BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion" that was used for generating PinPoint counterfactuals.
Please, refer to the <a href="https://tencentarc.github.io/BrushNet/">Original Project</a> for details on the environment installation and a complete description of BrushNet's features.




## Getting Started

### Environment Requirement 🌍

BrushNet has been implemented and tested on Pytorch 1.12.1 with python 3.9.

Clone the repo:

```
git clone https://github.com/TencentARC/BrushNet.git
```

We recommend you first use `conda` to create virtual environment, and install `pytorch` following [official instructions](https://pytorch.org/). For example:


```
conda create -n diffusers python=3.9 -y
conda activate diffusers
python -m pip install --upgrade pip
pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116
```

Then, you can install diffusers (implemented in this repo) with:

```
pip install -e .
```

After that, you can install required packages thourgh:

```
cd examples/brushnet/
pip install -r requirements.txt
```

### Data Download ⬇️


**Dataset**

After downloading the datasets (CC3M: https://ai.google.com/research/ConceptualCaptions/download and FACET https://ai.meta.com/datasets/facet-downloads/), edit the paths in 
`./examples/brushnet/inpaint_cc3m.py`, and
`./examples/brushnet/inpaint_facet.py`.

**Checkpoints**

Checkpoints of BrushNet can be downloaded from [here](https://drive.google.com/drive/folders/1fqmS1CEOvXCxNWFrsSYd_jHYXxrydh1n?usp=drive_link). The ckpt folder needs to contain pretrained checkpoints and pretrained Stable Diffusion checkpoint (realisticVisionV60B1_v51VAE from [Civitai](https://civitai.com/)). The data structure should be like:

```
|-- data
    |-- BrushData
    |-- BrushDench
    |-- EditBench
    |-- ckpt
        |-- realisticVisionV60B1_v51VAE
            |-- model_index.json
            |-- vae
            |-- ...
        |-- segmentation_mask_brushnet_ckpt
```



## 🏃🏼 Running Scripts

### Inference 📜

You can in-paint FACET with the script:

```
python examples/brushnet/inpaint_facet.py
```

You can experiment with different prompts for in-painting different values of protected attributes. For in-painting PP*, WB*, and C&A* setups, change the corresponding prompts to `f"A photo of a man/woman who is a {category}."`, otherwise leave it as `f"A photo of a man/woman."`

For in-paintinf CC3M use the script:

```
python examples/brushnet/inpaint_cc3m.py
```


### Evaluation 📏

You can evaluate the image realism using the following script, first installing the required dependencies:

```
python examples/brushnet/evaluate_dir.py
```



Make sure to set the paths to the image directories before running the script.  



Note that the evaluation script requires the following additional dependencies:
- open_clip
- hpsv2
- ImageReward
- CLIPScore