Awesome
LostGANs: Image Synthesis From Reconfigurable Layout and Style
This is implementation of our paper Image Synthesis From Reconfigurable Layout and Style and Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis
Network Structure
Installation
Check INSTALL.md for installation instructions.
1. Download pretrained model
Download pretrained models to pretrained_model/
2. Train models
python train.py --dataset coco --out_path outputs/
3. Run pretrained model
python test.py --dataset coco --model_path pretrained_model/G_coco.pth --sample_path samples/coco/
Results
Compare different models
Multiple samples generated from same layout
Synthesized images and learned masks for given layout
Contact
Please feel free to report issues and any related problems to Wei Sun (wsun12 at ncsu dot edu) and Tianfu Wu (tianfu_wu at ncsu dot edu).
Reference
- Synchronized-BatchNorm-PyTorch: https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
- Image Generation from Scene Graphs: https://github.com/google/sg2im
- Faster R-CNN and Mask R-CNN in PyTorch 1.0: https://github.com/facebookresearch/maskrcnn-benchmark