Awesome

PLGAN: Interactive Image Synthesis with Panoptic Layout Generation

This repository includes the implementation for Interactive Image Synthesis with Panoptic Layout Generation (to appear in CVPR 2022).

Installation

1. Requiements

• NVIDIA GPU, tested with V100
• python3
• pytorch == 1.2.0
• imageio

pip install -r requirements.txt

2. Setup roi_layers

python setup.py build develop

GUI Demo

1. Pretrained model

Download frozen model plgan_256_frozen.pt or freeze pretrained model

CUDA_VISIBLE_DEVICES="0" python freeze.py \
    --dataset coco \
    --set val \
    --input_size 256 \
    --sample_path ./ \
    --model_path ${your_checkpoint_path} \
    --output_name ${frozen_model_name} \
    --sample_times 1

2. Run

python src/simple_server.py --model_path ./plgan_256_frozen.pt

3. Play with the GUI

Open "localhost:6006" in your browser. Zoom the webpage according to your screen size.

Data Preparation

Download COCO dataset to datasets/coco

bash scripts/download_coco.sh

Download VG dataset to datasets/vg

bash scripts/download_vg.sh
python scripts/preprocess_vg.py

Training

Our PLGAN leverages the state-of-the-art models, such as Grid2Im[1], LostGAN-V1[2], LostGAN-V2[3] and CAL2I[4], for layout-to-image synthesis. Here, we support two versions of our PLGAN based on LostGAN-V2[3] and CAL2I[4].

# LostGAN-V2
CUDA_VISIBLE_DEVICES="0,1,2,3" python train_lostgan.py \
    --dataset coco \
    --data_dir ${your_data_directory} \
    --input_size 256 \
    --batch_size 32 \
    --workers 16 \
    --out_path ./models/plgan_lostgan_256_coco

# CAL2I
CUDA_VISIBLE_DEVICES="0,1,2,3" python train_cal2i.py \
    --dataset coco \
    --data_dir ${your_data_directory} \
    --input_size 256 \
    --batch_size 32 \
    --workers 16 \
    --out_path ./models/plgan_cal2i_256_coco

Inference

# LostGAN-V2
CUDA_VISIBLE_DEVICES="0" python eval_lostgan.py \
    --dataset coco \
    --data_dir ${your_data_directory} \
    --set val \
    --input_size 256 \
    --sample_path ${your_checkpoint_path} \
    --model_path {} \
    --sample_times 5 \
    --gt_bb

# CAL2I
CUDA_VISIBLE_DEVICES="0" python eval_cal2i.py \
    --dataset coco \
    --data_dir ${your_data_directory} \
    --set val \
    --input_size 256 \
    --sample_path ${your_checkpoint_path} \
    --model_path {} \
    --sample_times 5 \
    --gt_bb

Performance

Reference

[1] Grid2Im: https://github.com/ashual/scene_generation

[2-3] LostGAN: https://github.com/WillSuen/LostGANs

[4] CAL2I: https://github.com/wtliao/layout2img

Citation

If you find this code useful in your research then please cite

@article{wang2022interactive,
  title={Interactive Image Synthesis with Panoptic Layout Generation},
  author={Wang, Bo and Wu, Tao and Zhu, Minfeng and Du, Peng},
  booktitle={CVPR},
  year={2022}
}