# 🦙 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](https://advimman.github.io/lama-project/)] [[arXiv](https://arxiv.org/abs/2109.07161)] [[Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf)] [[BibTeX](https://senya-ashukha.github.io/projects/lama_21/paper.txt)] [[Casual GAN Papers Summary](https://www.casualganpapers.com/large-masks-fourier-convolutions-inpainting/LaMa-explained.html)]


Try out in Google Colab

# LaMa development (Feel free to share your paper by creating an issue) - Amazing results [paper](https://arxiv.org/abs/2206.13644) / [video](https://www.youtube.com/watch?v=gEukhOheWgE) / code https://github.com/advimman/lama/pull/112 / by Geomagical Labs ([geomagical.com](geomagical.com))

# Non-official 3rd party apps: (Feel free to share your app/implementation/demo by creating an issue) - [https://cleanup.pictures](https://cleanup.pictures/) - a simple interactive object removal tool by [@cyrildiagne](https://twitter.com/cyrildiagne) - [lama-cleaner](https://github.com/Sanster/lama-cleaner) by [@Sanster](https://github.com/Sanster/lama-cleaner) is a self-host version of [https://cleanup.pictures](https://cleanup.pictures/) - Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/lama) by [@AK391](https://github.com/AK391) - Telegram bot [@MagicEraserBot](https://t.me/MagicEraserBot) by [@Moldoteck](https://github.com/Moldoteck), [code](https://github.com/Moldoteck/MagicEraser) - [Auto-LaMa](https://github.com/andy971022/auto-lama) = DE:TR object detection + LaMa inpainting by [@andy971022](https://github.com/andy971022) - [LAMA-Magic-Eraser-Local](https://github.com/zhaoyun0071/LAMA-Magic-Eraser-Local) = a standalone inpainting application built with PyQt5 by [@zhaoyun0071](https://github.com/zhaoyun0071) - [Hama](https://www.hama.app/) - object removal with a smart brush which simplifies mask drawing. - [ModelScope](https://www.modelscope.cn/models/damo/cv_fft_inpainting_lama/summary) = the largest Model Community in Chinese by [@chenbinghui1](https://github.com/chenbinghui1). - [LaMa with MaskDINO](https://github.com/qwopqwop200/lama-with-maskdino) = MaskDINO object detection + LaMa inpainting with refinement by [@qwopqwop200](https://github.com/qwopqwop200). # Environment setup Clone the repo: `git clone https://github.com/advimman/lama.git` There are three options of an environment: 1. 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 ``` 2. 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 $HOME/miniconda/bin/conda init bash cd lama conda env create -f conda_env.yml conda activate lama conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -y pip install pytorch-lightning==1.2.9 ``` 3. Docker: No actions are needed 🎉. # Inference Run ``` cd lama export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd) ``` **1. Download pre-trained models** Install tool for yandex disk link extraction: ``` pip3 install wldhx.yadisk-direct ``` The best model (Places2, Places Challenge): ``` curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip unzip big-lama.zip ``` All models (Places & CelebA-HQ): ``` curl -L $(yadisk-direct https://disk.yandex.ru/d/EgqaSnLohjuzAg) -o lama-models.zip unzip lama-models.zip ``` **2. Prepare images and masks** Download test images: ``` curl -L $(yadisk-direct https://disk.yandex.ru/d/xKQJZeVRk5vLlQ) -o LaMa_test_images.zip unzip LaMa_test_images.zip ```
OR prepare your data: 1) Create masks named as `[images_name]_maskXXX[image_suffix]`, put images and masks in the same folder. - You can use the [script](https://github.com/advimman/lama/blob/main/bin/gen_mask_dataset.py) for random masks generation. - Check the format of the files: ``` image1_mask001.png image1.png image2_mask001.png image2.png ``` 2) Specify `image_suffix`, e.g. `.png` or `.jpg` or `_input.jpg` in `configs/prediction/default.yaml`.
**3. Predict** On the host machine: python3 bin/predict.py model.path=$(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 $(pwd)/output device=cpu ``` Docker cuda: TODO **4. Predict with Refinement** On the host machine: python3 bin/predict.py refine=True model.path=$(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](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf#subsection.3.1)] ⚠️ 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=$(pwd)/experiments/__lama-fourier_/ \ indir=$(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/__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__512.yaml \ # thick, thin, medium my_dataset/val_source/ \ my_dataset/val/random__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__512.yaml \ #thick, thin, medium my_dataset/visual_test_source/ \ my_dataset/visual_test/random__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__512.yaml \ #thick, thin, medium my_dataset/eval_source/ \ my_dataset/eval/random__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/__lama-fourier_/ \ indir=$(pwd)/my_dataset/eval/random__512/ \ outdir=$(pwd)/inference/my_dataset/random__512 \ model.checkpoint=epoch32.ckpt # metrics calculation: python3 bin/evaluate_predicts.py \ $(pwd)/configs/eval2_gpu.yaml \ $(pwd)/my_dataset/eval/random__512/ \ $(pwd)/inference/my_dataset/random__512 \ $(pwd)/inference/my_dataset/random__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](#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 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 ### Links - All the data (models, test images, etc.) https://disk.yandex.ru/d/AmdeG-bIjmvSug - Test images from the paper https://disk.yandex.ru/d/xKQJZeVRk5vLlQ - The pre-trained models https://disk.yandex.ru/d/EgqaSnLohjuzAg - The models for perceptual loss https://disk.yandex.ru/d/ncVmQlmT_kTemQ - Our training logs are available at https://disk.yandex.ru/d/9Bt1wNSDS4jDkQ ### Training time & resources TODO ## Acknowledgments * Segmentation code and models if form [CSAILVision](https://github.com/CSAILVision/semantic-segmentation-pytorch). * LPIPS metric is from [richzhang](https://github.com/richzhang/PerceptualSimilarity) * SSIM is from [Po-Hsun-Su](https://github.com/Po-Hsun-Su/pytorch-ssim) * FID is from [mseitzer](https://github.com/mseitzer/pytorch-fid) ## 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} } ```