66 lines
2.2 KiB
Markdown
66 lines
2.2 KiB
Markdown
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# FuseDream
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This repo contains code for our paper ([paper link](https://arxiv.org/abs/2112.01573)):
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**FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization**
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by *Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su and Qiang Liu* from UCSD and UT Austin.
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## Introduction
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FuseDream uses pre-trained GANs (we support BigGAN-256 and BigGAN-512 for now) and CLIP to achieve high-fidelity text-to-image generation.
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## Requirements
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Please use `pip` or `conda` to install the following packages:
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`PyTorch==1.7.1, torchvision==0.8.2, lpips==0.1.4` and also the requirements from [BigGAN](https://github.com/ajbrock/BigGAN-PyTorch).
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## Getting Started
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We transformed the pre-trained weights of BigGAN from TFHub to PyTorch. To save your time, you can download the transformed BigGAN checkpoints from:
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https://drive.google.com/drive/folders/1nJ3HmgYgeA9NZr-oU-enqbYeO7zBaANs?usp=sharing
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Put the checkpoints into `./BigGAN_utils/weights/`
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Run the following command to generate images from text query:
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`python fusedream_generator.py --text 'YOUR TEXT' --seed YOUR_SEED`
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For example, to get an image of a blue dog:
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`python fusedream_generator.py --text 'A photo of a blue dog.' --seed 1234`
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The generated image will be stored in `./samples`
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## Colab Notebook
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For a quick test of *FuseDream*, we provide Colab notebooks for [*FuseDream*(Single Image)](https://colab.research.google.com/drive/17qkzkoQQtzDRFaSCJQzIaNj88xjO9Rm9?usp=sharing) and *FuseDream-Composition*(TODO). Have fun!
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## Citations
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If you use the code, please cite:
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```BibTex
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@inproceedings{
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brock2018large,
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title={Large Scale {GAN} Training for High Fidelity Natural Image Synthesis},
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author={Andrew Brock and Jeff Donahue and Karen Simonyan},
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booktitle={International Conference on Learning Representations},
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year={2019},
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url={https://openreview.net/forum?id=B1xsqj09Fm},
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}
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```
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and
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```BibTex
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@misc{
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liu2021fusedream,
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title={FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization},
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author={Xingchao Liu and Chengyue Gong and Lemeng Wu and Shujian Zhang and Hao Su and Qiang Liu},
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year={2021},
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eprint={2112.01573},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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