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| title: DALL·E mini | |
| emoji: 🥑 | |
| colorFrom: red | |
| colorTo: purple | |
| sdk: streamlit | |
| app_file: app/app.py | |
| pinned: false | |
| # DALL·E Mini | |
| _Generate images from a text prompt_ | |
| <img src="img/logo.png" width="200"> | |
| 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](https://huggingface.co/spaces/flax-community/dalle-mini) (temporarily in beta on Huging Face Spaces but soon to be open to all). | |
| ## How does it work? | |
| Refer to [our report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA). | |
| ## 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. | |
| ## Development | |
| This section is for the adventurous people wanting to look into the code. | |
| ### Dependencies Installation | |
| The root folder and associated `requirements.txt` is only for the app. | |
| You will find necessary requirements in each sub-section. | |
| You should create a new python virtual environment and install the project dependencies inside the virtual env. You need to use the `-f` (`--find-links`) option for `pip` to be able to find the appropriate `libtpu` required for the TPU hardware. | |
| Adapt the installation to your own hardware and follow library installation instructions. | |
| ``` | |
| $ pip install -r requirements.txt -f https://storage.googleapis.com/jax-releases/libtpu_releases.html | |
| ``` | |
| If you use `conda`, you can create the virtual env and install everything using: `conda env update -f environments.yaml` | |
| ### Training of VQGAN | |
| The VQGAN was trained using [taming-transformers](https://github.com/CompVis/taming-transformers). | |
| We recommend using the latest version available. | |
| ### Conversion of VQGAN to JAX | |
| Use [patil-suraj/vqgan-jax](https://github.com/patil-suraj/vqgan-jax). | |
| ### Training of Seq2Seq | |
| Refer to `dev/seq2seq` folder. | |
| You can also adjust the [sweep configuration file](https://docs.wandb.ai/guides/sweeps) if you need to perform a hyperparameter search. | |
| ### Inference | |
| Refer to `dev/notebooks/demo`. | |
| ## Authors | |
| - [Boris Dayma](https://github.com/borisdayma) | |
| - [Suraj Patil](https://github.com/patil-suraj) | |
| - [Pedro Cuenca](https://github.com/pcuenca) | |
| - [Khalid Saifullah](https://github.com/khalidsaifullaah) | |
| - [Tanishq Abraham](https://github.com/tmabraham) | |
| - [Phúc Lê Khắc](https://github.com/lkhphuc) | |
| - [Luke Melas](https://github.com/lukemelas) | |
| - [Ritobrata Ghosh](https://github.com/ghosh-r) | |
| ## Acknowledgements | |
| - 🤗 Hugging Face for organizing [the FLAX/JAX community week](https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects) | |
| - Google Cloud team for providing access to TPU's | |