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LICENSE

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+Modified MIT License
+
+Software Copyright (c) 2021 OpenAI
+
+We don’t claim ownership of the content you create with the DALL-E discrete VAE, so it is yours to
+do with as you please. We only ask that you use the model responsibly and clearly indicate that it
+was used.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+associated documentation files (the "Software"), to deal in the Software without restriction,
+including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
+and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included
+in all copies or substantial portions of the Software.
+The above copyright notice and this permission notice need not be included
+with content created by the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+# Overview
+
+[[Blog]](https://openai.com/blog/dall-e/) [[Paper]](https://arxiv.org/abs/2102.12092) [[Model Card]](model_card.md) [[Usage]](notebooks/usage.ipynb)
+
+This is the official PyTorch package for the discrete VAE used for DALL·E. The transformer used to generate the images from the text is not part of this code release.
+
+# Installation
+
+Before running [the example notebook](notebooks/usage.ipynb), you will need to install the package using
+
+	pip install DALL-E

model_card.md

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+# Model Card: DALL·E dVAE
+
+Following [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from
+Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we're providing some information about about the discrete
+VAE (dVAE) that was used to train DALL·E.
+
+## Model Details
+
+The dVAE was developed by researchers at OpenAI to reduce the memory footprint of the transformer trained on the
+text-to-image generation task. The details involved in training the dVAE are described in [the paper][dalle_paper]. This
+model card describes the first version of the model, released in February 2021. The model consists of a convolutional
+encoder and decoder whose architectures are described [here](dall_e/encoder.py) and [here](dall_e/decoder.py), respectively.
+For questions or comments about the models or the code release, please file a Github issue.
+
+## Model Use
+
+### Intended Use
+
+The model is intended for others to use for training their own generative models.
+
+### Out-of-Scope Use Cases
+
+This model is inappropriate for high-fidelity image processing applications. We also do not recommend its use as a
+general-purpose image compressor.
+
+## Training Data
+
+The model was trained on publicly available text-image pairs collected from the internet. This data consists partly of
+[Conceptual Captions][cc] and a filtered subset of [YFCC100M][yfcc100m]. We used a subset of the filters described in
+[Sharma et al.][cc_paper] to construct this dataset; further details are described in [our paper][dalle_paper]. We will
+not be releasing the dataset.
+
+## Performance and Limitations
+
+The heavy compression from the encoding process results in a noticeable loss of detail in the reconstructed images. This
+renders it inappropriate for applications that require fine-grained details of the image to be preserved.
+
+[dalle_paper]: https://arxiv.org/abs/2102.12092
+[cc]: https://ai.google.com/research/ConceptualCaptions
+[cc_paper]: https://www.aclweb.org/anthology/P18-1238/
+[yfcc100m]: http://projects.dfki.uni-kl.de/yfcc100m/

requirements.txt

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+Pillow
+blobfile
+mypy
+numpy
+pytest
+requests
+torch
+torchvision

setup.py

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+from setuptools import setup
+
+def parse_requirements(filename):
+	lines = (line.strip() for line in open(filename))
+	return [line for line in lines if line and not line.startswith("#")]
+
+setup(name='DALL-E',
+        version='0.1',
+        description='PyTorch package for the discrete VAE used for DALL·E.',
+        url='http://github.com/openai/DALL-E',
+        author='Aditya Ramesh',
+        author_email='aramesh@openai.com',
+        license='BSD',
+        packages=['dall_e'],
+        install_requires=parse_requirements('requirements.txt'),
+        zip_safe=True)