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| title: DALL·E mini | |
| emoji: 🥑 | |
| colorFrom: yellow | |
| colorTo: green | |
| sdk: static | |
| pinned: True | |
| license: apache-2.0 | |
| # 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-Explained-with-Demo--Vmlldzo4NjIxODA) 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/ | |