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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
| Date | Stars |
|---|---|
| 2026-07-24 | 5628 |
| 2026-07-25 | 5628 |
| 2026-07-28 | 5628 |
| 2026-07-30 | 5628 |
| 2026-07-31 | 5628 |
| 2026-08-06 | 5628 |
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# DALL-E in Pytorch
<p align='center'>
<a href="https://colab.research.google.com/gist/afiaka87/b29213684a1dd633df20cab49d05209d/train_dalle_pytorch.ipynb">
<img alt="Train DALL-E w/ DeepSpeed" src="https://colab.research.google.com/assets/colab-badge.svg">
</a>
<a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a></br>
<a href="https://github.com/robvanvolt/DALLE-models">Released DALLE Models</a></br>
<a href="https://github.com/rom1504/dalle-service">Web-Hostable DALLE Checkpoints</a></br>
<a href="https://www.youtube.com/watch?v=j4xgkjWlfL4">Yannic Kilcher's video</a>
<p>
Implementation / replication of <a href="https://openai.com/blog/dall-e/">DALL-E</a> (<a href="https://arxiv.org/abs/2102.12092">paper</a>), OpenAI's Text to Image Transformer, in Pytorch. It will also contain <a href="https://openai.com/blog/clip/">CLIP</a> for ranking the generations.
---
[Quick Start](https://github.com/lucidrains/DALLE-pytorch/wiki)
<a href="https://github.com/lucidrains/deep-daze">Deep Daze</a> or <a href="https://github.com/lucidrains/big-sleep">Big Sleep</a> are great alternatives!
For generating video and audio, please see <a href="https://github.com/lucidrains/nuwa-pytorch">NÜWA</a>
## Appreciation
This library could not have been possible without the contributions of <a href="https://github.com/janEbert">janEbert</a>, <a href="https://github.com/afiaka87">Clay</a>, <a href="https://github.com/robvanvolt">robvanvolt</a>, <a href="https://github.com/rom1504">Romain Beaumont</a>, and <a href="https://github.com/borzunov">Alexander</a>! 🙏
## Status
<p align='center'>
- <a href="https://github.com/htoyryla">Hannu</a> has managed to train a small 6 layer DALL-E on a dataset of just 2000 landscape images! (2048 visual tokens)
<img src="./images/landscape.png"></img>
- <a href="https://github.com/kobiso">Kobiso</a>, a research engineer from Naver, has trained on the CUB200 dataset <a href="https://github.com/lucidrains/DALLE-pytorch/discussions/131">here</a>, using full and deepspeed sparse attention
<img src="./images/birds.png" width="256"></img>
- (3/15/21) <a href="https://github.com/afiaka87">afiaka87</a> has managed one epoch using a reversible DALL-E and the dVaE <a href="https://github.com/lucidrains/DALLE-pytorch/issues/86#issue-832121328">here</a>
- <a href="https://github.com/robvanvolt">TheodoreGalanos</a> has trained on 150k layouts with the following results
<p>
<img src="./images/layouts-1.jpg" width="256"></img>
<img src="./images/layouts-2.jpg" width="256"></img>
</p>
- <a href="https://github.com/rom1504">Rom1504</a> has trained on 50k fashion images with captions with a really small DALL-E (2 layers) for just 24 hours with the following results
<p/>
<img src="./images/clothing.png" width="420"></img>
- <a href="https://github.com/afiaka87">afiaka87</a> trained for 6 epochs on the same dataset as before thanks to the efficient 16k VQGAN with the following <a href="https://github.com/lucidrains/DALLE-pytorch/discussions/322>discussion">results</a>
<p align='centered'>
<img src="https://user-images.githubusercontent.com/3994972/123564891-b6f18780-d780-11eb-9019-8a1b6178f861.png" width="420" alt-text='a photo of westwood park, san francisco, from the water in the afternoon'></img>
<img src="https://user-images.githubusercontent.com/3994972/123564776-4c404c00-d780-11eb-9c8e-3356df358df3.png" width="420" alt-text='a female mannequin dressed in an olive button-down shirt and gold palazzo pants'> </img>
</p>
Thanks to the amazing "mega b#6696" you can generate from this checkpoint in colab -
<a href="https://colab.research.google.com/drive/11V2xw1eLPfZvzW8UQyTUhqCEU71w6Pr4?usp=sharing">
<img alt="Run inference on the Afiaka checkpoint in Colab" src="https://colab.research.google.com/assets/colab-badge.svg">
</a>
- (5/2/21) First <a href="https://github.Excerpt of 28,096 characters
Read on GitHub
Phil Wang · United States
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Clay Mullis
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Romain Beaumont · @google · United States
20
Mehdi Cherti · Juelich Supercomputing Center (JSC), Forschungszentrum Jülich GmbH, LAION · Germany
8
3
Grant Celley
3
2
Alexander Borzunov
2
2
2
snoop2head · Carnegie Mellon University · United States
1
1
1
Kartheek Akella · India
1
Inchul Shin · Quantit · South Korea
1
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Yuliang Liu · Professor, Huazhong University of Science and Technology
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dba51277a0e799c7, topic:text-to-image, desc:text-to-image, readme:text-to-image
matched fp:dba51277a0e799c7, topic:deep-learning