Science Score: 54.0%
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✓CITATION.cff file
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○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Repository
Minimal Implementation of a D3PM in pytorch
Basic Info
- Host: GitHub
- Owner: cloneofsimo
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://proceedings.neurips.cc/paper/2021/hash/958c530554f78bcd8e97125b70e6973d-Abstract.html
- Size: 1.89 MB
Statistics
- Stars: 212
- Watchers: 4
- Forks: 16
- Open Issues: 6
- Releases: 0
Metadata Files
readme.md
Minimal Implementation of a D3PM (Structured Denoising Diffusion Models in Discrete State-Spaces), in pytorch
Special thanks to fal.ai for the compute resources for this project.
This is minimal (400 LOC), but fully faithful implementation of a D3PM Structured Denoising Diffusion Models in Discrete State-Spaces. in pytorch.
I have tried to keep the code as simple as possible with much comments and explanation that is somewhat lacking on the original jax implementation, so that it is easy to understand. As far as I know, this is the first, faithful reimplementation of D3PM in pytorch. (Please correct me if I am wrong). Of course, this implementation was heavily based on the official implementation.
Difference between this implementation and the official implementation:
- This one has conditional sampling, so as you can see, generations are class-conditioned.
- This one uses rather different/simple model architecture.
- This one simplfies the official implementation very very much, so it is 400 LOC.
- This one does not use truncated logistic reparameterization, but you can use that if you wish.
- Only has uniform sample with inverse-linear beta scheudule, but you can change that with couple loc as well.
Usage
Following is completely self-contained example.
bash
python d3pm_runner.py
Following uses dit.py, for CIFAR-10 dataset.
bash
python d3pm_runner_cifar.py
Requirements
Install torch, torchvision, pillow, tqdm
bash
pip install torch torchvision pillow tqdm
Citation
This implementation:
bibtex
@misc{d3pm_pytorch,
author={Simo Ryu},
title={Minimal Implementation of a D3PM (Structured Denoising Diffusion Models in Discrete State-Spaces), in pytorch},
year={2024},
howpublished={\url{https://github.com/cloneofsimo/d3pm}}
}
Original Paper:
bibtex
@article{austin2021structured,
title={Structured denoising diffusion models in discrete state-spaces},
author={Austin, Jacob and Johnson, Daniel D and Ho, Jonathan and Tarlow, Daniel and Van Den Berg, Rianne},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={17981--17993},
year={2021}
}
Owner
- Name: Simo Ryu
- Login: cloneofsimo
- Kind: user
- Company: Corca AI
- Website: https://fb.com/MLPaperFetchingCat
- Twitter: cloneofsimo
- Repositories: 10
- Profile: https://github.com/cloneofsimo
Cats are Turing machines cloneofsimo@gmail.com
Citation (CITATION.cff)
cff-version: 1.2.0 message: "Citations would be appreciated if you end up using this tool! I currently go by Simo Ryu" authors: - family-names: "Ryu" given-names: "Simo" orcid: "https://orcid.org/0009-0008-0017-2677" title: "Minimal Implementation of a D3PM (Structured Denoising Diffusion Models in Discrete State-Spaces), in pytorch" version: 0.0.1 date-released: 2024-04 url: "https://github.com/cloneofsimo/d3pm"
GitHub Events
Total
- Issues event: 5
- Watch event: 71
- Issue comment event: 2
- Fork event: 4
Last Year
- Issues event: 5
- Watch event: 71
- Issue comment event: 2
- Fork event: 4
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Simo Ryu | c****o@g****m | 15 |
| simo-ryu | s****u@n****m | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 9
- Total pull requests: 0
- Average time to close issues: about 6 hours
- Average time to close pull requests: N/A
- Total issue authors: 9
- Total pull request authors: 0
- Average comments per issue: 1.33
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 9
- Pull requests: 0
- Average time to close issues: about 6 hours
- Average time to close pull requests: N/A
- Issue authors: 9
- Pull request authors: 0
- Average comments per issue: 1.33
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
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- fiy2W (1)
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- toshi-koike-akino (1)
- SizhuangHe (1)
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