We provide the Pytorch implementation of "Spatially-Correlative Lightweight GAN for Thermal to Visible Face Transformation". Based on the inherent self-similarity of facial attributes.

This code was tested with Pytorch 1.7.0, CUDA 10.2, and Python 3.7

• Install Pytoch 1.7.0, torchvision, and other dependencies from http://pytorch.org
• Install python libraries visdom and dominate for visualization

pip install visdom dominate

• Clone this repo:

git clone https://github.com/GANGREEK/SCL-GAN.git
cd SCL-GAN

Step 1: Configure the Jeston-TX2 Board by using Jetpack over the SDK manager. Step 2: Install PyTorch for the Jetson Borad by using the link https://github.com/Qengineering/PyTorch-Jetson-Nano. Step 3: Install the packages : ‘argparse==1.2.1’, ‘attrs==17.4.0’, ‘funcsigs==1.0.2’, ‘gps==3.17’,‘graphsurgeon==0.4.5’, ‘jetson.gpio==2.0.8’, \ ‘numpy==1.13.3’,‘pluggy==0.6.0’, ‘pycairo==1.16.2’, ‘pygobject==3.26.1’, ‘py==1.5.2’,‘pytest==3.3.2’, ‘python==2.7.17’, ‘six==1.11.0’, \ ‘tensorrt==7.1.3.0’, ‘uff==0.6.9’,‘unity-lens-photos==1.0’, ‘urwid==2.0.1’, ‘wsgiref==0.1.2’. Step 4: For visualizing of the generated images over the screen attached to the board install: Matplot and NumPy from https://forums.developer.nvidia.com/t/jetson-nano-how-can-install-matplotlib.

sh ./scripts/train_sc.sh 

• Set --use_norm for cosine similarity map, the default similarity is dot-based attention score. --learned_attn, --augment for the learned self-similarity.

• To view training results and loss plots, run python -m visdom.server and copy the URL http://localhost:port.

• Training models will be saved under the checkpoints folder.

• The more training options can be found in the options folder.
  
  

• Train the single-image translation model:

sh ./scripts/train_sinsc.sh 

sh ./scripts/test_sc.sh

- Test the *single-image* translation model:

sh ./scripts/test_sinsc.sh

- Test the FID score for all training epochs:

sh ./scripts/test_fid.sh

### Pretrained Models

Download the pre-trained models (will be released soon) using the following links and put them under```checkpoints/``` directory.

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Submitted in In ICIP-2025(Will update Soon after good news)

Our code is developed based on CUT and CycleGAN. We also thank pytorch-fid for FID computation, LPIPS for diversity score.