crfill
Usage | Web App | | Paper | Supplementary Material | More results |
code for paper ``CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction". This repo (including code and models) are for research purposes only.
Usage
Dependencies
- Download code
git clone --single-branch https://github.com/zengxianyu/crfill
git submodule init
git submodule update
- Download data and model
chmod +x download/*
./download/download_model.sh
./download/download_data.sh
- Install dependencies:
conda env create -f environment.yml
or install these packages manually in a Python 3.6 enviroment:
pytorch=1.3.1, opencv=3.4.2, tqdm, torchvision, dill, matplotlib, opencv
Inference
./test.sh
These script will run the inpainting model on the samples I provided. Modify the options --image_dir, --mask_dir, --output_dir in test.sh to test on custom data.
Train
-
Prepare training datasets and put them in
./datasets/following the example./datasets/places -
run the training script:
./train.sh
open the html files in ./output to visualize training
After the training is finished, the model files can be found in ./checkpoints/debugarr0
you may modify the training script to use different settings, e.g., batch size, hyperparameters
Finetune
For finetune on custom dataset based on my pretrained models, use the following command:
- download checkpoints
./download/download_pretrain.sh
- run the training script
./finetune.sh
you may change the options in finetune.sh to use different hyperparameters or your own dataset
Web APP
To use the web app, these additional packages are required:
flask, requests, pillow
./demo.sh
then open http://localhost:2334 in the browser to use the web app
Citing
@inproceedings{zeng2021generative,
title={CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction},
author={Zeng, Yu and Lin, Zhe and Lu, Huchuan and Patel, Vishal M.},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
year={2021}
}