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title: DALL·E mini
emoji: 🥑
colorFrom: yellow
colorTo: green
sdk: streamlit
app_file: app/streamlit/app.py
pinned: true
DALL·E Mini
Generate images from a text prompt
Our logo was generated with DALL·E mini using the prompt "logo of an armchair in the shape of an avocado".
You can create your own pictures with the demo.
How does it work?
Refer to our report.
Development
Dependencies Installation
For inference only, use pip install git+https://github.com/borisdayma/dalle-mini.git.
For development, clone the repo and use pip install -e ".[dev]".
Training of VQGAN
The VQGAN was trained using taming-transformers.
We recommend using the latest version available.
Conversion of VQGAN to JAX
Training of Seq2Seq
Use tools/train/train.py.
You can also adjust the sweep configuration file if you need to perform a hyperparameter search.
Inference Pipeline
To generate sample predictions and understand the inference pipeline step by step, refer to tools/inference/inference_pipeline.ipynb.
FAQ
Where to find the latest models?
Trained models are on 🤗 Model Hub:
- VQGAN-f16-16384 for encoding/decoding images
- DALL·E mini for generating images from a text prompt
Where does the logo come from?
The "armchair in the shape of an avocado" was used by OpenAI when releasing DALL·E to illustrate the model's capabilities. Having successful predictions on this prompt represents a big milestone to us.
Authors & Contributors
Main Authors
Other Members of dalle-mini team during FLAX/JAX community week
Contributing
Join the community on the DALLE-Pytorch Discord. Any contribution is welcome, from reporting issues to proposing fixes/improvements or testing the model on cool prompts!
Acknowledgements
- 🤗 Hugging Face for organizing the FLAX/JAX community week
- Google TPU Research Cloud (TRC) program for providing computing resources
- Weights & Biases for providing the infrastructure for experiment tracking and model management
Citing DALL·E mini
If you find DALL·E mini useful in your research or wish to refer, please use the following BibTeX entry.
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
References
@misc{ramesh2021zeroshot,
title={Zero-Shot Text-to-Image Generation},
author={Aditya Ramesh and Mikhail Pavlov and Gabriel Goh and Scott Gray and Chelsea Voss and Alec Radford and Mark Chen and Ilya Sutskever},
year={2021},
eprint={2102.12092},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{esser2021taming,
title={Taming Transformers for High-Resolution Image Synthesis},
author={Patrick Esser and Robin Rombach and Björn Ommer},
year={2021},
eprint={2012.09841},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{lewis2019bart,
title={BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension},
author={Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Ves Stoyanov and Luke Zettlemoyer},
year={2019},
eprint={1910.13461},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{radford2021learning,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
year={2021},
eprint={2103.00020},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{anil2021scalable,
title={Scalable Second Order Optimization for Deep Learning},
author={Rohan Anil and Vineet Gupta and Tomer Koren and Kevin Regan and Yoram Singer},
year={2021},
eprint={2002.09018},
archivePrefix={arXiv},
primaryClass={cs.LG}
}