FuseDream

This repo contains code for our paper (paper link):

FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization

by Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su and Qiang Liu from UCSD and UT Austin.

Introduction

FuseDream uses pre-trained GANs (we support BigGAN-256 and BigGAN-512 for now) and CLIP to achieve high-fidelity text-to-image generation.

Requirements

Please use pip or conda to install the following packages: PyTorch==1.7.1, torchvision==0.8.2, lpips==0.1.4 and also the requirements from BigGAN.

Getting Started

We transformed the pre-trained weights of BigGAN from TFHub to PyTorch. To save your time, you can download the transformed BigGAN checkpoints from:

https://drive.google.com/drive/folders/1nJ3HmgYgeA9NZr-oU-enqbYeO7zBaANs?usp=sharing

Put the checkpoints into ./BigGAN_utils/weights/

Run the following command to generate images from text query:

python fusedream_generator.py --text 'YOUR TEXT' --seed YOUR_SEED

For example, to get an image of a blue dog:

python fusedream_generator.py --text 'A photo of a blue dog.' --seed 1234

The generated image will be stored in ./samples

Colab Notebook

For a quick test of FuseDream, we provide Colab notebooks for FuseDream(Single Image) and FuseDream-Composition(TODO). Have fun!

Citations

If you use the code, please cite:

@inproceedings{
brock2018large,
title={Large Scale {GAN} Training for High Fidelity Natural Image Synthesis},
author={Andrew Brock and Jeff Donahue and Karen Simonyan},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=B1xsqj09Fm},
}

and

@misc{
liu2021fusedream,
title={FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization}, 
author={Xingchao Liu and Chengyue Gong and Lemeng Wu and Shujian Zhang and Hao Su and Qiang Liu},
year={2021},
eprint={2112.01573},
archivePrefix={arXiv},
primaryClass={cs.CV}
}