🦙 LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions

by Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky.

🔥🔥🔥
LaMa generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.

[Project page] [arXiv] [Supplementary] [BibTeX] [Casual GAN Papers Summary]

Try out in Google Colab

LaMa development

(Feel free to share your paper by creating an issue)

https://github.com/geekyutao/Inpaint-Anything --- Inpaint Anything: Segment Anything Meets Image Inpainting

Feature Refinement to Improve High Resolution Image Inpainting / video / code advimman#112 / by Geomagical Labs (geomagical.com)

Non-official 3rd party apps:

(Feel free to share your app/implementation/demo by creating an issue)

https://github.com/enesmsahin/simple-lama-inpainting - a simple pip package for LaMa inpainting.
https://github.com/mallman/CoreMLaMa - Apple's Core ML model format
https://cleanup.pictures - a simple interactive object removal tool by @cyrildiagne
- lama-cleaner by @Sanster is a self-host version of https://cleanup.pictures
Integrated to Huggingface Spaces with Gradio. See demo: by @AK391
Telegram bot @MagicEraserBot by @Moldoteck, code
Auto-LaMa = DE:TR object detection + LaMa inpainting by @andy971022
LAMA-Magic-Eraser-Local = a standalone inpainting application built with PyQt5 by @zhaoyun0071
Hama - object removal with a smart brush which simplifies mask drawing.
ModelScope = the largest Model Community in Chinese by @chenbinghui1.
LaMa with MaskDINO = MaskDINO object detection + LaMa inpainting with refinement by @qwopqwop200.
CoreMLaMa - a script to convert Lama Cleaner's port of LaMa to Apple's Core ML model format.

Environment setup

Clone the repo: git clone https://github.com/advimman/lama.git

There are three options of an environment:

Python virtualenv:

virtualenv inpenv --python=/usr/bin/python3
source inpenv/bin/activate
pip install torch==1.8.0 torchvision==0.9.0

cd lama
pip install -r requirements.txt

Conda

% Install conda for Linux, for other OS download miniconda at https://docs.conda.io/en/latest/miniconda.html
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda
$HOME/miniconda/bin/conda init bash

cd lama
conda env create -f conda_env.yml
conda activate lama
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -y
pip install pytorch-lightning==1.2.9

Docker: No actions are needed 🎉.

Inference

Run

cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

1. Download pre-trained models

The best model (Places2, Places Challenge):

curl -LJO https://huggingface.co/smartywu/big-lama/resolve/main/big-lama.zip
unzip big-lama.zip

All models (Places & CelebA-HQ):

download [https://drive.google.com/drive/folders/1B2x7eQDgecTL0oh3LSIBDGj0fTxs6Ips?usp=drive_link]
unzip lama-models.zip

2. Prepare images and masks

Download test images:

unzip LaMa_test_images.zip

OR prepare your data:

1) Create masks named as `[images_name]_maskXXX[image_suffix]`, put images and masks in the same folder.

You can use the script for random masks generation.

Check the format of the files:

image1_mask001.png
image1.png
image2_mask001.png
image2.png

Specify image_suffix, e.g. .png or .jpg or _input.jpg in configs/prediction/default.yaml.

3. Predict

On the host machine:

python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output

OR in the docker

The following command will pull the docker image from Docker Hub and execute the prediction script

bash docker/2_predict.sh $(pwd)/big-lama $(pwd)/LaMa_test_images $(pwd)/output device=cpu

Docker cuda:

bash docker/2_predict_with_gpu.sh $(pwd)/big-lama $(pwd)/LaMa_test_images $(pwd)/output

4. Predict with Refinement

On the host machine:

python3 bin/predict.py refine=True model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output

Train and Eval

Make sure you run:

cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

Then download models for perceptual loss:

mkdir -p ade20k/ade20k-resnet50dilated-ppm_deepsup/
wget -P ade20k/ade20k-resnet50dilated-ppm_deepsup/ http://sceneparsing.csail.mit.edu/model/pytorch/ade20k-resnet50dilated-ppm_deepsup/encoder_epoch_20.pth

Places

⚠️ NB: FID/SSIM/LPIPS metric values for Places that we see in LaMa paper are computed on 30000 images that we produce in evaluation section below. For more details on evaluation data check [Section 3. Dataset splits in Supplementary] ⚠️

On the host machine:

# Download data from http://places2.csail.mit.edu/download.html
# Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images section
wget http://data.csail.mit.edu/places/places365/train_large_places365standard.tar
wget http://data.csail.mit.edu/places/places365/val_large.tar
wget http://data.csail.mit.edu/places/places365/test_large.tar

# Unpack train/test/val data and create .yaml config for it
bash fetch_data/places_standard_train_prepare.sh
bash fetch_data/places_standard_test_val_prepare.sh

# Sample images for test and viz at the end of epoch
bash fetch_data/places_standard_test_val_sample.sh
bash fetch_data/places_standard_test_val_gen_masks.sh

# Run training
python3 bin/train.py -cn lama-fourier location=places_standard

# To evaluate trained model and report metrics as in our paper
# we need to sample previously unseen 30k images and generate masks for them
bash fetch_data/places_standard_evaluation_prepare_data.sh

# Infer model on thick/thin/medium masks in 256 and 512 and run evaluation 
# like this:
python3 bin/predict.py \
model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier_/ \
indir=$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \
outdir=$(pwd)/inference/random_thick_512 model.checkpoint=last.ckpt

python3 bin/evaluate_predicts.py \
$(pwd)/configs/eval2_gpu.yaml \
$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \
$(pwd)/inference/random_thick_512 \
$(pwd)/inference/random_thick_512_metrics.csv

Docker: TODO

CelebA

On the host machine:

# Make shure you are in lama folder
cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

# Download CelebA-HQ dataset
# Download data256x256.zip from https://drive.google.com/drive/folders/11Vz0fqHS2rXDb5pprgTjpD7S2BAJhi1P

# unzip & split into train/test/visualization & create config for it
bash fetch_data/celebahq_dataset_prepare.sh

# generate masks for test and visual_test at the end of epoch
bash fetch_data/celebahq_gen_masks.sh

# Run training
python3 bin/train.py -cn lama-fourier-celeba data.batch_size=10

# Infer model on thick/thin/medium masks in 256 and run evaluation 
# like this:
python3 bin/predict.py \
model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier-celeba_/ \
indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \
outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt

Docker: TODO

Places Challenge

On the host machine:

# This script downloads multiple .tar files in parallel and unpacks them
# Places365-Challenge: Train(476GB) from High-resolution images (to train Big-Lama) 
bash places_challenge_train_download.sh

TODO: prepare
TODO: train 
TODO: eval

Docker: TODO

Create your data

Please check bash scripts for data preparation and mask generation from CelebaHQ section, if you stuck at one of the following steps.

On the host machine:

# Make shure you are in lama folder
cd lama
export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)

# You need to prepare following image folders:
$ ls my_dataset
train
val_source # 2000 or more images
visual_test_source # 100 or more images
eval_source # 2000 or more images

# LaMa generates random masks for the train data on the flight,
# but needs fixed masks for test and visual_test for consistency of evaluation.

# Suppose, we want to evaluate and pick best models 
# on 512x512 val dataset  with thick/thin/medium masks 
# And your images have .jpg extention:

python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_<size>_512.yaml \ # thick, thin, medium
my_dataset/val_source/ \
my_dataset/val/random_<size>_512.yaml \# thick, thin, medium
--ext jpg

# So the mask generator will: 
# 1. resize and crop val images and save them as .png
# 2. generate masks

ls my_dataset/val/random_medium_512/
image1_crop000_mask000.png
image1_crop000.png
image2_crop000_mask000.png
image2_crop000.png
...

# Generate thick, thin, medium masks for visual_test folder:

python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_<size>_512.yaml \  #thick, thin, medium
my_dataset/visual_test_source/ \
my_dataset/visual_test/random_<size>_512/ \ #thick, thin, medium
--ext jpg


ls my_dataset/visual_test/random_thick_512/
image1_crop000_mask000.png
image1_crop000.png
image2_crop000_mask000.png
image2_crop000.png
...

# Same process for eval_source image folder:

python3 bin/gen_mask_dataset.py \
$(pwd)/configs/data_gen/random_<size>_512.yaml \  #thick, thin, medium
my_dataset/eval_source/ \
my_dataset/eval/random_<size>_512/ \ #thick, thin, medium
--ext jpg



# Generate location config file which locate these folders:

touch my_dataset.yaml
echo "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yaml
echo "out_root_dir: $(pwd)/experiments/" >> my_dataset.yaml
echo "tb_dir: $(pwd)/tb_logs/" >> my_dataset.yaml
mv my_dataset.yaml ${PWD}/configs/training/location/


# Check data config for consistency with my_dataset folder structure:
$ cat ${PWD}/configs/training/data/abl-04-256-mh-dist
...
train:
  indir: ${location.data_root_dir}/train
  ...
val:
  indir: ${location.data_root_dir}/val
  img_suffix: .png
visual_test:
  indir: ${location.data_root_dir}/visual_test
  img_suffix: .png


# Run training
python3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10

# Evaluation: LaMa training procedure picks best few models according to 
# scores on my_dataset/val/ 

# To evaluate one of your best models (i.e. at epoch=32) 
# on previously unseen my_dataset/eval do the following 
# for thin, thick and medium:

# infer:
python3 bin/predict.py \
model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier_/ \
indir=$(pwd)/my_dataset/eval/random_<size>_512/ \
outdir=$(pwd)/inference/my_dataset/random_<size>_512 \
model.checkpoint=epoch32.ckpt

# metrics calculation:
python3 bin/evaluate_predicts.py \
$(pwd)/configs/eval2_gpu.yaml \
$(pwd)/my_dataset/eval/random_<size>_512/ \
$(pwd)/inference/my_dataset/random_<size>_512 \
$(pwd)/inference/my_dataset/random_<size>_512_metrics.csv

OR in the docker:

TODO: train
TODO: eval

Hints

Generate different kinds of masks

The following command will execute a script that generates random masks.

bash docker/1_generate_masks_from_raw_images.sh \
    configs/data_gen/random_medium_512.yaml \
    /directory_with_input_images \
    /directory_where_to_store_images_and_masks \
    --ext png

The test data generation command stores images in the format, which is suitable for prediction.

The table below describes which configs we used to generate different test sets from the paper. Note that we do not fix a random seed, so the results will be slightly different each time.

	Places 512x512	CelebA 256x256
Narrow	random_thin_512.yaml	random_thin_256.yaml
Medium	random_medium_512.yaml	random_medium_256.yaml
Wide	random_thick_512.yaml	random_thick_256.yaml

Feel free to change the config path (argument #1) to any other config in configs/data_gen or adjust config files themselves.

Override parameters in configs

Also you can override parameters in config like this:

python3 bin/train.py -cn <config> data.batch_size=10 run_title=my-title

Where .yaml file extension is omitted

Models options

Config names for models from paper (substitude into the training command):

* big-lama
* big-lama-regular
* lama-fourier
* lama-regular
* lama_small_train_masks

Which are seated in configs/training/folder

Training time & resources

TODO

Acknowledgments

Segmentation code and models if form CSAILVision.
LPIPS metric is from richzhang
SSIM is from Po-Hsun-Su
FID is from mseitzer

Citation

If you found this code helpful, please consider citing:

@article{suvorov2021resolution,
  title={Resolution-robust Large Mask Inpainting with Fourier Convolutions},
  author={Suvorov, Roman and Logacheva, Elizaveta and Mashikhin, Anton and Remizova, Anastasia and Ashukha, Arsenii and Silvestrov, Aleksei and Kong, Naejin and Goka, Harshith and Park, Kiwoong and Lempitsky, Victor},
  journal={arXiv preprint arXiv:2109.07161},
  year={2021}
}

limitedonly41/lama

🦙 LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions

LaMa development

Non-official 3rd party apps:

Environment setup

Inference

Train and Eval

Places

CelebA

Places Challenge

Create your data

Hints

Generate different kinds of masks

Override parameters in configs

Models options

Links

Training time & resources

Acknowledgments

Citation