EdiBERT is a generative model based on a bi-directional transformer, suited for image manipulation

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Deep Learning EdiBERT
Overview

EdiBERT, a generative model for image editing

EdiBERT is a generative model based on a bi-directional transformer, suited for image manipulation. The same EdiBERT model, derived from a single training, can be used on a wide variety of tasks.

edibert_example

We follow the implementation of Taming-Transformers (https://github.com/CompVis/taming-transformers). Main modifications can be found in: taming/models/bert_transformer.py ; scripts/sample_mask_likelihood_maximization.py.

Requirements

A suitable conda environment named edibert can be created and activated with:

conda env create -f environment.yaml
conda activate edibert

FFHQ

Download FFHQ dataset (https://github.com/NVlabs/ffhq-dataset) and put it into data/ffhq/.

Training BERT

In the logs/ folder, download and extract the FFHQ VQGAN:

gdown --id '1P_wHLRfdzf1DjsAH_tG10GXk9NKEZqTg'
tar -xvzf 2021-04-23T18-19-01_ffhq_vqgan.tar.gz

Training on 1 GPUs:

python main.py --base configs/ffhq_transformer_bert_2D.yaml -t True --gpus 0,

Training on 2 GPUs:

python main.py --base configs/ffhq_transformer_bert_2D.yaml -t True --gpus 0,1

Running pre-trained BERT on composite/scribble-edited images

In the logs/ folder, download and extract the FFHQ VQGAN:

gdown --id '1P_wHLRfdzf1DjsAH_tG10GXk9NKEZqTg'
tar -xvzf 2021-04-23T18-19-01_ffhq_vqgan.tar.gz

In the logs/ folder, download and extract the FFHQ BERT:

gdown --id '1YGDd8XyycKgBp_whs9v1rkYdYe4Oxfb3'
tar -xvzf 2021-10-14T16-32-28_ffhq_transformer_bert_2D.tar.gz

folders and place them into logs.

Then, launch the following script for composite images:

python scripts/sample_mask_likelihood_maximization.py -r logs/2021-10-14T16-32-28_ffhq_transformer_bert_2D/checkpoints/epoch=000019.ckpt \
--image_folder data/ffhq_collages/ --mask_folder data/ffhq_collages_masks/ --image_list data/ffhq_collages.txt --keep_img \
--dilation_sampling 1 -k 100 -t 1.0 --batch_size 5 --bert --epochs 2  \
--device 0 --random_order \
--mask_collage --collage_frequency 3 --gaussian_smoothing_collage

Then, launch the following script for edits images:

python scripts/sample_mask_likelihood_maximization.py -r logs/2021-10-14T16-32-28_ffhq_transformer_bert_2D/checkpoints/epoch=000019.ckpt \
--image_folder data/ffhq_edits/ --mask_folder data/ffhq_edits_masks/ --image_list data/ffhq_edits.txt --keep_img \
--dilation_sampling 1 -k 100 -t 1.0 --batch_size 5 --bert --epochs 2  \
--device 0 --random_order \
--mask_collage --collage_frequency 3 --gaussian_smoothing_collage

The samples can then be found in logs/my_model/samples/. Here, the --batch_size argument corresponds to the number of EdiBERT generations per image.

Notebooks for playing with completion/denoising with BERT

Notebooks for image denoising and image inpainting can also be found in the main folder.

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Comments
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
  • gdown can't retrieve public link for FFHQ VQGAN

    gdown can't retrieve public link for FFHQ VQGAN

    The file can still be downloaded by going to the drive.google.com link (as suggested below). But I just wanted to give you a heads-up that the gdown command in the README does not work:

    $ gdown --id '1P_wHLRfdzf1DjsAH_tG10GXk9NKEZqTg'
Access denied with the following error:

 	Cannot retrieve the public link of the file. You may need to change
	the permission to 'Anyone with the link', or have had many accesses. 

You may still be able to access the file from the browser:

	 https://drive.google.com/uc?id=1P_wHLRfdzf1DjsAH_tG10GXk9NKEZqTg
    opened by keithcallenberg 0
Owner
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