pmh9960 / gcdp

Official PyTorch implementation of "Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis." (ICCV 2023)

Gaussian-Categorical Diffusion Process: Official PyTorch Implementation (ICCV 2023)

This is the official PyTorch implementation of the paper: Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis.

GCDP stands for Gaussian-Categorical Diffusion Process

Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis
Minho Park*, Jooyeol Yun*, Seunghwan Choi, and Jaegul Choo
KAIST
In ICCV 2023. (* indicate equal contribution)

Paper: https://arxiv.org/abs/2308.08157
Project page: https://pmh9960.github.io/research/GCDP/

Abstract: Existing text-to-image generation approaches have set high standards for photorealism and text-image correspondence, largely benefiting from web-scale text-image datasets, which can include up to 5 billion pairs. However, text-to-image generation models trained on domain-specific datasets, such as urban scenes, medical images, and faces, still suffer from low text-image correspondence due to the lack of text-image pairs. Additionally, collecting billions of text-image pairs for a specific domain can be time-consuming and costly. Thus, ensuring high text-image correspondence without relying on web-scale text-image datasets remains a challenging task. In this paper, we present a novel approach for enhancing text-image correspondence by leveraging available semantic layouts. Specifically, we propose a Gaussian-categorical diffusion process that simultaneously generates both images and corresponding layout pairs. Our experiments reveal that we can guide text-to-image generation models to be aware of the semantics of different image regions, by training the model to generate semantic labels for each pixel. We demonstrate that our approach achieves higher text-image correspondence compared to existing text-to-image generation approaches in the Multi-Modal CelebA-HQ and the Cityscapes dataset, where text-image pairs are scarce.

Updates

• [16/08/2023] Pretrained models have been released!
• [30/07/2023] Code and project page is open to the public.

Gaussian-categorical Diffusion Process

Implementation of the GCDP is available in imagen_pytorch/joint_imagen.py.

Installation

conda env create -f environment.yaml
conda activate GCDP

Training GCDP

First prepare an official Cityscapes / MM CelebA-HQ dataset with the following structure.

Cityscapes

root
 └ leftImg8bit
   └ train
    └ aachen
    └ ...
   └ val
   └ test
 └ gtFine
   └ train
    └ aachen
    └ ...
   └ val
   └ test

MM CelebA-HQ

root
 └ CelebA-HQ-img
   └ 1.jpg
   └ 2.jpg
   └ ...
 └ CelebAMask-HQ-mask-anno
   └ preprocessed
    └ 1.png
    └ 2.png
    └ ...

Please fill in the train/evaluation directories in the scripts/celeba/train_base_128x128.sh file and execute

bash scripts/celeba/train_base_128x128.sh --root /path/to/data

Testing GCDP

You can generate text-conditioned image-layout pairs leveraging pre-trained GCDP models. Please fill in the path to the model checkpoints and validation directories in the scripts/celeba/test_base.sh and scripts/celeba/test_sr.sh file.

bash scripts/celeba/test_base.sh --checkpoint_path /path/to/base/checkpoint

bash scripts/celeba/test_sr.sh --checkpoint_path /path/to/sr/checkpoint

Pretrained Models

Checkpoints for GCDP models are available in here.

• password: GCDP

Citation

@inproceedings{park2023learning,
  title={Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis},
  author={Park, Minho and Yun, Jooyeol and Choi, Seunghwan and Choo, Jaegul},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={7591--7600},
  year={2023}
}

Acknowledgements

This repository is based on imagen-pytorch by lucidrains and Multinomial Diffusion by Hoogeboom et al.