saharmor / dalle-mini

DALL·E Mini - Generate images from a text prompt

title	emoji	colorFrom	colorTo	sdk	app_file	pinned
DALL·E mini	🥑	yellow	green	streamlit	app/app.py	false

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 this demo, or spin off your own playground and experiement with a full-stack app using the DALL-E Playground repository.

How does it work?

Refer to our report.

Development

Dependencies Installation

For development, use dev/requirements.txt or dev/environment.yaml.

Training of VQGAN

The VQGAN was trained using taming-transformers.

We recommend using the latest version available.

Conversion of VQGAN to JAX

Use patil-suraj/vqgan-jax.

Training of Seq2Seq

Refer to dev/seq2seq folder.

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 dev/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

• Boris Dayma
• Suraj Patil
• Pedro Cuenca
• Khalid Saifullah
• Tanishq Abraham
• Phúc Lê Khắc
• Luke Melas
• Ritobrata Ghosh

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}
}