TACO: Text-Adaptive COmpression

Paper | Project page

Hagyeong Lee* $^1$, Minkyu Kim* $^1$, Jun-Hyuk Kim $^2$, Seungeon Kim $^2$, Dokwan Oh $^2$, Jaeho Lee $^1$

$^1$ Pohang University of Science and Technology (POSTECH) & $^2$ Samsung Advanced Institute of Technology (SAIT)

This is the official Github page for the paper "Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity (ICML 2024)".

Summary & Architecture

We propose a compression framework that leverages text information mainly by text-adaptive encoding and training with joint image-text loss. By doing so, we avoid decoding based on text-guided generative models---known for high generative diversity---and effectively utilize the semantic information of text at a global level.

Dataset

This repository does not contain any image datasets used in the manuscript.
We used Kodak, MS-COCO 30k, and CLIC datasets for our main experiments. (Section 4.1. in our paper for details)

How to train

For training TACO, you can use the following command:

python -u train.py --dist_port (int) --train_dataset_root_path (string) --lpips_coefficient (float) --joint_image_text_loss_coefficient (float) --epochs (int) --learning_rate (float) --aux-learning-rate (float)  --num-workers (int) --lambda (float) --batch-size (int) --patch-size (int int) --seed (int) --clip_max_norm (float) --lr_epoch (int int)  

# e.g. python -u train.py --dist_port 6411 --train_dataset_root_path /data/MSCOCO --lpips_coefficient 3.50 --joint_image_text_loss_coefficient 0.0025 --epochs 50 --learning_rate 1e-4 --aux-learning-rate 1e-3 --num-workers 8 --lambda 0.0004 --batch-size 8 --patch-size 256 256 --seed 100 --clip_max_norm 1.0 --lr_epoch 45 48

Details of each arguments are as followings :

dist_port: port for using Distributed Data Parallel (DDP) (default: 6006)
train_dataset_root_path: root folder of training dataset(e.g. MSCOCO)
lpips_coefficient: coefficient of LPIPS loss (default: 1.0)
joint_image_text_loss_coefficient: coefficient of joint image-text loss (default: 0.005)
epochs: Number of epochs (default: 50)
learning_rate: Learning rate (default: 1e-4)
aux-learning-rate: Auxiliary loss learning rate (default: 1e-3)
num-workers: Dataloaders threads (default: 8)
lambda: Bit-rate distortion parameter (default: 0.0004)
batch-size: Batch size (default: 8)
patch-size: Size of the patches to be cropped (default: (256, 256))
seed: Set random seed for reproducibility
clip_max_norm: Gradient clipping max norm (default: 1.0)
lr_epoch: Set epoch to schedule the learning rate (default: 45 48)

How to inference

For testing TACO, you can use the following command:

python -u generate_images_using_image_cap_dataset.py --image_folder_root (path for original image data) --checkpoint (path of the model checkpoint)

# e.g. python -u generate_mscoco30k.py --image_folder_root /data/MSCOCO/val2014 --checkpoint /checkpoint/pre_trained_ckpt.pth.tar

If you want to download checkpoints, click this link

Citation

@inproceedings{lee2024taco,
        title={Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity},
        author={Lee, Hagyeong and Kim, Minkyu and Kim, Jun-Hyuk and Kim, Seungeon and Oh, Dokwan and Lee, Jaeho},
        booktitle={International Conference on Machine Learning},
        year={2024}

Contact

If you have any questions, please feel free to contact Hagyeong Lee (hagyeonglee@postech.ac.kr) or Minkyu Kim (minkyu.kim@postech.ac.kr).

effl-lab/TACO