| # Model Card: DALL路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路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/ | |