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

Last update: Dec 07, 2022

Related tags

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.

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.

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

Related tags

Overview

EdiBERT, a generative model for image editing

Requirements

FFHQ

Training BERT

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

Notebooks for playing with completion/denoising with BERT

Owner

Object-aware Contrastive Learning for Debiased Scene Representation

Bayesian Optimization Library for Medical Image Segmentation.

Hierarchical User Intent Graph Network for Multimedia Recommendation

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

Reinforcement Learning with Q-Learning Algorithm on gym's frozen lake environment implemented in python

Fake videos detection by tracing the source using video hashing retrieval.

Code for Understanding Pooling in Graph Neural Networks

NER for Indian languages

Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend

An API-first distributed deployment system of deep learning models using timeseries data to analyze and predict systems behaviour

This repository contains code demonstrating the methods outlined in Path Signature Area-Based Causal Discovery in Coupled Time Series presented at Causal Analysis Workshop 2021.

Official repository for Natural Image Matting via Guided Contextual Attention

This repository contains implementations and illustrative code to accompany DeepMind publications

PyTorch implementation of Super SloMo by Jiang et al.

Adjusting for Autocorrelated Errors in Neural Networks for Time Series

Large-scale Hyperspectral Image Clustering Using Contrastive Learning, CIKM 21 Workshop

Open-AI's DALL-E for large scale training in mesh-tensorflow.

Official implementation for paper Render In-between: Motion Guided Video Synthesis for Action Interpolation

A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

Code for Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games