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FreestyleNet

Official PyTorch implementation of Freestyle Layout-to-Image Synthesis

Introduction

FreestyleNet is a diffusion-based network that can generate diverse semantics onto a given layout. Compared to prior layout-to-image synthesis methods and text-to-image generation models (e.g., Stable Diffusion), FreestyleNet is armed with more controllability, enabling 1) the generation of semantics beyond the pre-defined semantic categories in the training dataset, and 2) the separate modulation of each class in the layout with text.

Teaser

A comparison to ControlNet is provided on our project website.

Updates

[2023/04/22] - Code & pre-trained models released!

Requirements

Our code is built upon Stable Diffusion. Please clone the repository and set up the environment:

git clone https://github.com/essunny310/FreestyleNet.git
cd FreestyleNet
conda env create -f environment.yaml
conda activate freestyle

You will also need to download the pre-trained Stable Diffusion model (or manually download it from here):

mkdir models/ldm/stable-diffusion
wget -O models/ldm/stable-diffusion/sd-v1-4-full-ema.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4-full-ema.ckpt

Data Preparation

COCO-Stuff. The dataset can be found here. You will need to download train2017.zip, val2017.zip, and stuffthingmaps_trainval2017.zip. Please unzip them and generate two files: COCO_train.txt and COCO_val.txt, which contain the absolute path of each image (e.g., "/path/to/dataset/COCO-Stuff/train_img/000000000009.jpg"). At last, put them under a directory as follows:

COCO-Stuff
    stuffthingmaps_trainval2017/
        train2017/
            000000000009.png
            ...
        val2017/
            000000000139.png
            ...
    train_img/
        000000000009.jpg
        ...
    val_img/
        000000000139.jpg
        ...
    COCO_train.txt
    COCO_val.txt

ADE20K. The dataset can be downloaded here. Please unzip it and generate two files: ADE20K_train.txt and ADE20K_val.txt, just like we just did for COCO-Stuff, then you should get a directory structure as follows:

ADEChallengeData2016
    annotations/
        training/
            ADE_train_00000001.png
            ...
        validation/
            ADE_val_00000001.png
            ...
    images/
        training/
            ADE_train_00000001.jpg
            ...
        validation/
            ADE_val_00000001.jpg
            ...
    ADE20K_train.txt
    ADE20K_val.txt

Training

To train FreestyleNet, run:

python main.py --base /path/to/config
               -t
               --actual_resume models/ldm/stable-diffusion/sd-v1-4-full-ema.ckpt
               -n <exp_name>
               --gpus 0,
               --data_root /path/to/dataset
               --train_txt_file /path/to/dataset/with/train.txt
               --val_txt_file /path/to/dataset/with/val.txt

We provide two training scripts: train_COCO.sh and train_ADE20K.sh. Please modify --data_root, --train_txt_file, and --val_txt_file according to the actual path.

Pre-trained Models

We provide two models trained on COCO-Stuff and ADE20K respectively.

Generation

Layout-to-Image Synthesis (LIS)

To generate images under the traditional LIS setting, run:

python scripts/LIS.py --batch_size 8
                      --config /path/to/config
                      --ckpt /path/to/trained_model
                      --dataset <dataset name>
                      --outdir /path/to/output
                      --txt_file /path/to/dataset/with/val.txt
                      --data_root /path/to/dataset
                      --plms

We provide two sampling scripts: sample_COCO.sh and sample_ADE20K.sh. Please modify --ckpt, --txt_file, and --data_root according to the actual path.

Freestyle Layout-to-Image Synthesis (FLIS)

To generate images in a freestyle way, you need to prepare a layout image and a json file that defines the mapping between text and layout.

{
  "text_label_mapping": {
    "book": 83, # each mapping should be formatted as <"text": label_value>
    "vase": 85,
    "flower": 118,
    "furniture": 122,
    "paper": 138,
    "plastic": 142,
    "table": 164,
    "concrete wall": 171
  },
  "layout_path": "examples/layout_flower.png"
}

Binding new attributes/generating unseen objects: Just describe the object with the desired attribute or describe a new object, e.g., change <"flower": 118> to <"sunflower": 118>.
Specifying the style: Just add a description of the desired image style, e.g., add a line to the "text_label_mapping" such as <"drawn by Van Gogh": -1>. Here "-1" means that we want to apply the style globally (e.g., no layout constraint).

We provide several examples in examples/ and you try them out by running:

python scripts/FLIS.py --config configs/stable-diffusion/v1-inference_FLIS.yaml
                       --ckpt /path/to/trained_model
                       --json examples/layout_flower.json \
                       --outdir outputs/FLIS \
                       --plms

A reference script sample_FLIS.sh is provided as well.

Citation

If you find FreestyleNet useful for your work, please kindly consider citing our paper:

@inproceedings{xue2023freestylenet,
  title = {Freestyle Layout-to-Image Synthesis},
  author = {Xue, Han and Huang, Zhiwu and Sun, Qianru and Song, Li and Zhang, Wenjun},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  year = {2023},
}

Acknowledgments

Our code borrows heavily from Stable Diffusion and Textual Inversion.