README.md

# ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation


<a href="https://arxiv.org/pdf/2302.13848.pdf"><img src="https://img.shields.io/badge/arXiv-2302.13848-b31b1b.svg" height=22.5></a>
<a href="https://huggingface.co/spaces/ELITE-library/ELITE"><img src="https://img.shields.io/static/v1?label=HuggingFace&message=gradio demo&color=darkgreen" height=22.5></a>

## Getting Started

----

### Environment Setup

```shell
git clone https://github.com/csyxwei/ELITE.git
cd ELITE
conda create -n elite python=3.9
conda activate elite
pip install -r requirements.txt
```

### Pretrained Models

We provide the pretrained checkpoints in [Google Drive](https://drive.google.com/drive/folders/1VkiVZzA_i9gbfuzvHaLH2VYh7kOTzE0x?usp=sharing). One can download them and save to the directory `checkpoints`.

### Setting up Diffusers

Our code is built on the [diffusers](https://github.com/huggingface/diffusers/), and you can follow the guideline [here](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion#cat-toy-example) to set it.

### Customized Generation

We provide the testing dataset in [test_datasets](./test_datasets), which contains both images and object masks. For testing, you can run, 
```
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR='./test_datasets/'
CUDA_VISIBLE_DEVICES=0 python inference_local.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --test_data_dir=$DATA_DIR \
  --output_dir="./outputs/local_mapping"  \
  --suffix="object" \
  --template="a photo of a S" \
  --llambda="0.8" \
  --global_mapper_path="./checkpoints/global_mapper.pt" \
  --local_mapper_path="./checkpoints/local_mapper.pt"
```
or you can use the shell script:
```
bash inference_local.sh
```
If you want to test your customized dataset, you should align the image to ensure the object is at the center of image, and also provide the corresponding object mask. The object mask can be obtained by [image-matting-app](https://huggingface.co/spaces/SankarSrin/image-matting-app), or other image matting methods. 

## Training

----

### Preparing Dataset

We use the **test** dataset of Open-Images V6 to train our ELITE. You can prepare the dataset as follows:

- Download Open-Images test dataset from [CVDF's site](https://github.com/cvdfoundation/open-images-dataset#download-images-with-bounding-boxes-annotations) and unzip it to the directory `datasets/Open_Images/images/test`.
- Download attribute names file `oidv6-attributes-description.csv` of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and save it to the directory `datasets/Open_Images/annotations/`.
- Download bbox annotations file `test-annotations-bbox.csv` of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and save it to the directory `datasets/Open_Images/annotations/`.
- Download segmentation annotations of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and unzip them to the directory `datasets/Open_Images/segs/test`. And put the `test-annotations-object-segmentation.csv` into `datasets/Open_Images/annotations/`. 
- Obtain the mask bbox by running the following command:
    ```shell
    python data_scripts/cal_bbox_by_seg.py
    ```

The final data structure is like this:

```
datasets
├── Open_Images
│  ├── annotations
│  │  ├── oidv6-class-descriptions.csv
│  │  ├── test-annotations-object-segmentation.csv
│  │  ├── test-annotations-bbox.csv
│  ├── images
│  │  ├── test
│  │  │  ├── xxx.jpg
│  │  │  ├── ...
│  ├── segs
│  │  ├── test
│  │  │  ├── xxx.png
│  │  │  ├── ...
│  │  ├── test_bbox_dict.npy
```

### Training Global Mapping Network

To train the global mapping network, run the following command:

```Shell
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR='./datasets/Open_Images/'
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --config_file 4_gpu.json --main_process_port 25656 train_global.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATA_DIR \
  --placeholder_token="S" \
  --resolution=512 \
  --train_batch_size=4 \
  --gradient_accumulation_steps=4 \
  --max_train_steps=200000 \
  --learning_rate=1e-06 --scale_lr \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --output_dir="./elite_experiments/global_mapping" \
  --save_steps 200
```
or you can use the shell script:
```shell
bash train_global.sh
```

### Training Local Mapping Network

After the global mapping is trained, you can train the local mapping by running the following command:

```Shell
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR='/home/weiyuxiang/datasets/Open_Images/'
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --config_file 4_gpu.json --main_process_port 25657 train_local.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATA_DIR \
  --placeholder_token="S" \
  --resolution=512 \
  --train_batch_size=2 \
  --gradient_accumulation_steps=4 \
  --max_train_steps=200000 \
  --learning_rate=1e-5 --scale_lr \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --global_mapper_path "./elite_experiments/global_mapping/mapper_070000.pt" \
  --output_dir="./elite_experiments/local_mapping" \
  --save_steps 200
```
or you can use the shell script:
```shell
bash train_local.sh
```


## Citation

```
@article{wei2023elite,
  title={ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation},
  author={Wei, Yuxiang and Zhang, Yabo and Ji, Zhilong and Bai, Jinfeng and Zhang, Lei and Zuo, Wangmeng},
  journal={arXiv preprint arXiv:2302.13848},
  year={2023}
}
```

## Acknowledgements

This code is built on [diffusers](https://github.com/huggingface/diffusers/). We thank the authors for sharing the codes.