CLIP-GLaSS
Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search
An in-browser demo is available here
Installation
Clone this repository
git clone https://github.com/galatolofederico/clip-glass && cd clip-glass
Create a virtual environment and install the requirements
virtualenv --python=python3.6 env && . ./env/bin/activate
pip install -r requirements.txt
Run CLIP-GLaSS
You can run CLIP-GLaSS with:
python run.py --config <config> --target <target>
Specifying <config> and <target> according to the following table:
| Config | Meaning | Target Type |
|---|---|---|
| GPT2 | Use GPT2 to solve the Image-to-Text task | Image |
| DeepMindBigGAN512 | Use DeepMind's BigGAN 512x512 to solve the Text-to-Image task | Text |
| DeepMindBigGAN256 | Use DeepMind's BigGAN 256x256 to solve the Text-to-Image task | Text |
| StyleGAN2_ffhq_d | Use StyleGAN2-ffhq to solve the Text-to-Image task | Text |
| StyleGAN2_ffhq_nod | Use StyleGAN2-ffhq without Discriminator to solve the Text-to-Image task | Text |
| StyleGAN2_church_d | Use StyleGAN2-church to solve the Text-to-Image task | Text |
| StyleGAN2_church_nod | Use StyleGAN2-church without Discriminator to solve the Text-to-Image task | Text |
| StyleGAN2_car_d | Use StyleGAN2-car to solve the Text-to-Image task | Text |
| StyleGAN2_car_nod | Use StyleGAN2-car without Discriminator to solve the Text-to-Image task | Text |
If you do not have downloaded the models weights you will be prompted to run ./download-weights.sh
You will find the results in the folder ./tmp, a different output folder can be specified with --tmp-folder
Examples
python run.py --config StyleGAN2_ffhq_d --target "the face of a man with brown eyes and stubble beard"
python run.py --config GPT2 --target gpt2_images/dog.jpeg
Acknowledgments and licensing
This work heavily relies on the following amazing repositories and would have not been possible without them:
- CLIP from openai (included in the folder
clip) - pytorch-pretrained-BigGAN from huggingface
- stylegan2-pytorch from Adrian Sahlman (included in the folder
stylegan2) - gpt-2-pytorch from Tae-Hwan Jung (included in the folder
gpt2)
All their work can be shared under the terms of the respective original licenses.
All my original work (everything except the content of the folders clip, stylegan2 and gpt2) is released under the terms of the GNU/GPLv3 license. Copying, adapting and republishing it is not only consent but also encouraged.
Citing
If you want to cite use you can use this BibTeX
@article{generating2021,
author={Federico Galatolo. and Mario Cimino. and Gigliola Vaglini},
title={Generating Images from Caption and Vice Versa via CLIP-Guided Generative Latent Space Search},
journal={Proceedings of the International Conference on Image Processing and Vision Engineering},
year={2021},
volume={},
pages={},
publisher={SCITEPRESS - Science and Technology Publications},
doi={10.5220/0010503701660174},
issn={},
}
Contacts
For any further question feel free to reach me at [email protected] or on Telegram @galatolo