Package: dalle-mini
DALL·E MiniHow to use it?You can use the model on🖍️ craiyonHow does it work?Refer to our reports:DALL·E mini - Generate Images from Any Text PromptDALL·E mini - ExplainedDALL·E mega - Training JournalDevelopmentDependencies InstallationFor inference only, usepip install dalle-mini.For development, clone the repo and usepip install -e ".[dev]".
Before making a PR, check style withmake style.You can experiment with the pipeline step by step through ourinference pipeline notebookTraining of DALL·E miniUsetools/train/train.py.You can also adjust thesweep configuration fileif you need to perform a hyperparameter search.FAQWhere to find the latest models?Trained models are on 🤗 Model Hub:VQGAN-f16-16384for encoding/decoding imagesDALL·E miniorDALL·E megafor generating images from a text promptWhere 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 for us.ContributingJoin the community on theLAION Discord.
Any contribution is welcome, from reporting issues to proposing fixes/improvements or testing the model with cool prompts!You can also use these great projects from the community:spin off your own app withDALL-E Playground repository(thanksSahar)tryDALL·E Flowproject for generating, diffusion, and upscaling in a Human-in-the-Loop workflow (thanksHan Xiao)run onReplicate, in the browser or via APIAcknowledgements🤗 Hugging Face for organizingthe FLAX/JAX community weekGoogleTPU Research Cloud (TRC) programfor providing computing resourcesWeights & Biasesfor providing the infrastructure for experiment tracking and model managementAuthors & ContributorsDALL·E mini was initially developed by:Boris DaymaSuraj PatilPedro CuencaKhalid SaifullahTanishq AbrahamPhúc Lê KhắcLuke MelasRitobrata GhoshMany thanks to the people who helped make it better:theDALLE-PytorchandEleutherAIcommunities for testing and exchanging cool ideasRohan Anilfor adding Distributed Shampoo optimizer and always giving great suggestionsPhil Wanghas provided a lot of cool implementations of transformer variants and gives interesting insights withx-transformersKatherine Crowsonforsuper conditioningtheGradio teammade an amazing UI for our appCiting DALL·E miniIf you find DALL·E mini useful in your research or wish to refer, please use the following BibTeX entry.@misc{Dayma_DALL·E_Mini_2021,
      author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
      doi = {10.5281/zenodo.5146400},
      month = {7},
      title = {DALL·E Mini},
      url = {https://github.com/borisdayma/dalle-mini},
      year = {2021}
}ReferencesOriginal DALL·E from "Zero-Shot Text-to-Image Generation" with image quantization from "Learning Transferable Visual Models From Natural Language Supervision".Image encoder from "Taming Transformers for High-Resolution Image Synthesis".Sequence to sequence model based on "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" with implementation of a few variants:"GLU Variants Improve Transformer""Deepnet: Scaling Transformers to 1,000 Layers""NormFormer: Improved Transformer Pretraining with Extra Normalization""Swin Transformer: Hierarchical Vision Transformer using Shifted Windows""CogView: Mastering Text-to-Image Generation via Transformers""Root Mean Square Layer Normalization""Sinkformers: Transformers with Doubly Stochastic Attention""Foundation TransformersMain optimizer (Distributed Shampoo) from "Scalable Second Order Optimization for Deep Learning".Citations@misc{
  title={Zero-Shot Text-to-Image Generation}, 
  author={Aditya Ramesh and Mikhail Pavlov and Gabriel Goh and Scott Gray and Chelsea Voss and Alec Radford and Mark Chen and Ilya Sutskever},
  year={2021},
  eprint={2102.12092},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}@misc{
  title={Learning Transferable Visual Models From Natural Language Supervision}, 
  author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
  year={2021},
  eprint={2103.00020},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}@misc{
  title={Taming Transformers for High-Resolution Image Synthesis}, 
  author={Patrick Esser and Robin Rombach and Björn Ommer},
  year={2021},
  eprint={2012.09841},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}@misc{
  title={BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, 
  author={Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Ves Stoyanov and Luke Zettlemoyer},
  year={2019},
  eprint={1910.13461},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}@misc{
  title={Scalable Second Order Optimization for Deep Learning},
  author={Rohan Anil and Vineet Gupta and Tomer Koren and Kevin Regan and Yoram Singer},
  year={2021},
  eprint={2002.09018},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}@misc{
  title={GLU Variants Improve Transformer},
  author={Noam Shazeer},
  year={2020},
  url={https://arxiv.org/abs/2002.05202}    
}@misc{
  title={DeepNet: Scaling transformers to 1,000 layers},
  author={Wang, Hongyu and Ma, Shuming and Dong, Li and Huang, Shaohan and Zhang, Dongdong and Wei, Furu},
  year={2022},
  eprint={2203.00555}
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}@misc{
  title={NormFormer: Improved Transformer Pretraining with Extra Normalization},
  author={Sam Shleifer and Jason Weston and Myle Ott},
  year={2021},
  eprint={2110.09456},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}@inproceedings{
  title={Swin Transformer V2: Scaling Up Capacity and Resolution}, 
  author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
  booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}@misc{
  title = {CogView: Mastering Text-to-Image Generation via Transformers},
  author = {Ming Ding and Zhuoyi Yang and Wenyi Hong and Wendi Zheng and Chang Zhou and Da Yin and Junyang Lin and Xu Zou and Zhou Shao and Hongxia Yang and Jie Tang},
  year = {2021},
  eprint = {2105.13290},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV}
}@misc{
  title = {Root Mean Square Layer Normalization},
  author = {Biao Zhang and Rico Sennrich},
  year = {2019},
  eprint = {1910.07467},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG}
}@misc{
  title = {Sinkformers: Transformers with Doubly Stochastic Attention},
  url = {https://arxiv.org/abs/2110.11773},
  author = {Sander, Michael E. and Ablin, Pierre and Blondel, Mathieu and Peyré, Gabriel},
  publisher = {arXiv},
  year = {2021},
}@misc{
  title = {Smooth activations and reproducibility in deep networks},
  url = {https://arxiv.org/abs/2010.09931},
  author = {Shamir, Gil I. and Lin, Dong and Coviello, Lorenzo},
  publisher = {arXiv},
  year = {2020},
}@misc{
  title = {Foundation Transformers},
  url = {https://arxiv.org/abs/2210.06423},
  author = {Wang, Hongyu and Ma, Shuming and Huang, Shaohan and Dong, Li and Wang, Wenhui and Peng, Zhiliang and Wu, Yu and Bajaj, Payal and Singhal, Saksham and Benhaim, Alon and Patra, Barun and Liu, Zhun and Chaudhary, Vishrav and Song, Xia and Wei, Furu},
  publisher = {arXiv},
  year = {2022},
}