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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.
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 of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
| Date | Stars |
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| 2026-07-24 | 11306 |
| 2026-07-25 | 11307 |
| 2026-07-28 | 11307 |
| 2026-07-30 | 11307 |
| 2026-07-31 | 11308 |
| 2026-08-05 | 11307 |
| 2026-08-06 | 11307 |
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<img src="./dalle2.png" width="450px"></img> ## DALL-E 2 - Pytorch Implementation of <a href="https://openai.com/dall-e-2/">DALL-E 2</a>, OpenAI's updated text-to-image synthesis neural network, in Pytorch. <a href="https://youtu.be/RJwPN4qNi_Y?t=555">Yannic Kilcher summary</a> | <a href="https://www.youtube.com/watch?v=F1X4fHzF4mQ">AssemblyAI explainer</a> The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network 😂) This model is SOTA for text-to-image for now. Please join <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> if you are interested in helping out with the replication with the <a href="https://laion.ai/">LAION</a> community | <a href="https://www.youtube.com/watch?v=AIOE1l1W0Tw">Yannic Interview</a> As of 5/23/22, it is no longer SOTA. SOTA will be <a href="https://github.com/lucidrains/imagen-pytorch">here</a>. Jax versions as well as text-to-video project will be shifted towards the Imagen architecture, as it is way simpler. ## Status - A research group has used the code in this repository to train a functional diffusion prior for their CLIP generations. Will share their work once they release their preprint. This, and <a href="https://github.com/crowsonkb">Katherine's</a> own experiments, validate OpenAI's finding that the extra prior increases variety of generations. - Decoder is now verified working for unconditional generation on my experimental setup for Oxford flowers. 2 researchers have also confirmed Decoder is working for them. <img src="./samples/oxford.png" width="450px" /> *ongoing at 21k steps* - <a href="https://twitter.com/Buntworthy/status/1529475416775434240?t=0GEge3Kr9I36cjcUVCQUTg">Justin Pinkney</a> successfully trained the diffusion prior in the repository for his CLIP to Stylegan2 text-to-image application - <a href="https://github.com/rom1504">Romain</a> has scaled up training to 800 GPUs with the available scripts without any issues ## Pre-Trained Models - LAION is training prior models. Checkpoints are available on <a href="https://huggingface.co/zenglishuci/conditioned-prior">🤗huggingface</a> and the training statistics are available on <a href="https://wandb.ai/nousr_laion/conditioned-prior/reports/LAION-DALLE2-PyTorch-Prior--VmlldzoyMDI2OTIx">🐝WANDB</a>. - Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/jkrtg0so?workspace=user-veldrovive">In-progress test run</a> 🚧 - Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/3d5rytsa?workspace=">Another test run with sparse attention</a> - DALL-E 2 🚧 - <a href="https://github.com/LAION-AI/dalle2-laion">DALL-E 2 Laion repository</a> ## Appreciation This library would not have gotten to this working state without the help of - <a href="https://github.com/nousr">Zion</a> for the distributed training code for the diffusion prior - <a href="https://github.com/Veldrovive">Aidan</a> for the distributed training code for the decoder as well as the dataloaders - <a href="https://github.com/krish240574">Kumar</a> for working on the initial diffusion training script - <a href="https://github.com/rom1504">Romain</a> for the pull request reviews and project management - <a href="https://github.com/Ciaohe">He Cao</a> and <a href="https://github.com/xiankgx">xiankgx</a> for the Q&A and for identifying of critical bugs - <a href="https://github.com/marunine">Marunine</a> for identifying issues with resizing of the low resolution conditioner, when training the upsampler, in addition to various other bug fixes - <a href=
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1da3a36383aae219, topic:text-to-image, desc:text-to-image, readme:text-to-image
matched fp:1da3a36383aae219, topic:deep-learning, readme:distributed training