readme: # Model Card: DALL·E Mini
  
  This model is a reproduction of OpenAI’s DALL·E.  Please see [this link](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for project-specific details.  Below, we include the original DALL·E model card available on [the OpenAI github](https://github.com/openai/DALL-E/edit/master/model_card.md).
  
  ## 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/
  
  