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https://github.com/kuleshov-group/awesome-discrete-diffusion-models
A curated list for awesome discrete diffusion models resources.
https://github.com/kuleshov-group/awesome-discrete-diffusion-models
List: awesome-discrete-diffusion-models
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A curated list for awesome discrete diffusion models resources.
- Host: GitHub
- URL: https://github.com/kuleshov-group/awesome-discrete-diffusion-models
- Owner: kuleshov-group
- Created: 2023-03-10T03:31:09.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-31T17:28:42.000Z (about 2 months ago)
- Last Synced: 2024-10-31T18:25:40.538Z (about 2 months ago)
- Homepage:
- Size: 17.6 KB
- Stars: 41
- Watchers: 4
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-discrete-diffusion-models - A curated list for awesome discrete diffusion models resources. (Other Lists / Monkey C Lists)
README
# awesome-discrete-diffusion-models
[](https://awesome.re)  
A curated list of awesome discrete diffusion models resources.
## Contribution
This repo is maintained by [Subham Sahoo](https://s-sahoo.com/), [Yingheng Wang](https://isjakewong.github.io), and [Yair Schiff](https://yair-schiff.github.io/). Feel free to send [pull requests](https://github.com/isjakewong/awesome-discrete-diffusion-models/pulls) to add more papers! Papers must be added in a chronological sequence, with the most recent accepted papers taking precedence over unaccepted papers. Please use the following format:
```
{paper-name}, {conference} {year} [[link-to-the-abstract-page], [code-if-available]]
```
## Table of Contents
* [Introductory Materials](#introduction)
* Topic areas
* [Discrete Diffusion with Discrete Noise](#discrete)
* [Discrete Diffusion with Gaussian Noise](#gaussian)
* [Samplers](#samplers)
* [Guidance Mechanisms](#guidance)
* [Custom Noise Processes](#custom)
* [Theory](#theory)
* [Applications](#applications)
* [Surveys](#surveys)
## Introductory Materials
* Getting started with Diffusion Language Models, 2024.
* Diffusion Language Models, 2023 [[URL](https://benanne.github.io/2023/01/09/diffusion-language.html)]
* My notes on discrete denoising diffusion models (D3PMs), 2022 [[URL](https://beckham.nz/2022/07/11/d3pms.html)]
### Discrete Diffusion with Discrete Noise
* Simple and Effective Masked Diffusion Language Models, NeurIPS 2024 [[arXiv](https://arxiv.org/abs/2406.07524), [code](https://github.com/kuleshov-group/mdlm)]
* Simplified and Generalized Masked Diffusion for Discrete Data, NeurIPS 2024 [[arXiv](https://arxiv.org/abs/2406.04329)]
* Discrete Flow Matching, NeurIPS 2024 [[arXiv](https://arxiv.org/abs/2407.15595)]
* Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution, ICML 2024 [[arXiv](https://arxiv.org/abs/2310.16834), [code](https://github.com/louaaron/Score-Entropy-Discrete-Diffusion)]
* Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design, ICML 2024 [[arXiv](https://arxiv.org/abs/2402.04997)]
* Think While You Generate: Discrete Diffusion with Planned Denoising, arXiv 2024 [[arXiv](https://arxiv.org/pdf/2410.06264), [code](https://github.com/liusulin/DDPD)]
* Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data, arXiv 2024 [[arXiv](https://arxiv.org/abs/2406.03736), [code](https://github.com/ML-GSAI/RADD)]
* DiffusER: Discrete Diffusion via Edit-based Reconstruction, ICLR 2023 [[arXiv](https://arxiv.org/abs/2210.16886), [code](https://github.com/machelreid/diffuser)]
* Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning, ICLR 2023 [[arXiv](https://arxiv.org/abs/2208.04202), [code](https://github.com/google-research/pix2seq)]
* DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models, ICLR 2023 [[arXiv](https://arxiv.org/abs/2210.08933), [code](https://github.com/Shark-NLP/DiffuSeq)]
* FiLM: Fill-in Language Models for Any-Order Generation, arXiv 2023 [[arXiv](https://arxiv.org/abs/2310.09930), [code](https://github.com/shentianxiao/FiLM)]
* A Continuous Time Framework for Discrete Denoising Models, NeurIPS 2022 [[arXiv](https://arxiv.org/abs/2205.14987), [code](https://github.com/andrew-cr/tauLDR)]
* Autoregressive Diffusion Models, ICLR 2022 [[arXiv](https://arxiv.org/abs/2110.02037)]
* EdiT5: Semi-Autoregressive Text Editing with T5 Warm-Start, arXiv 2022 [[arXiv](https://arxiv.org/abs/2205.12209), [code](https://edit5.page.link/code)]
* Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions, NeurIPS 2021 [[arXiv](https://arxiv.org/abs/2102.05379), [code](https://github.com/didriknielsen/argmax_flows)]
* Structured Denoising Diffusion Models in Discrete State-Spaces, NeurIPS 2021 [[arXiv](https://arxiv.org/abs/2107.03006), [code](https://github.com/google-research/google-research/tree/master/d3pm)]
### Discrete Diffusion with Gaussian Noise
* SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control, ACL 2023 [[arXiv](https://arxiv.org/abs/2210.17432), [code](https://github.com/xhan77/ssd-lm)]
* Diffusion-LM Improves Controllable Text Generation, NeurIPS 2022 [[arXiv](https://arxiv.org/abs/2205.14217), [code](https://github.com/XiangLi1999/Diffusion-LM.git)]
* Self-conditioned Embedding Diffusion for Text Generation, NeurIPS 2022 [[arXiv](https://arxiv.org/abs/2211.04236)]
* Continuous Diffusion for Categorical Data, arXiv 2022 [[arXiv](https://arxiv.org/abs/2211.15089)]
### Samplers
* Beyond Autoregression: Fast LLMs via Self-Distillation Through Time, arXiv 2024 [[arXiv](https://arxiv.org/abs/2410.21035)]
* Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling, arXiv 2024 [[arXiv](https://arxiv.org/abs/2409.02908)]
* Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models, arXiv 2024 [[arXiv](https://arxiv.org/abs/2410.07761)]
### Guidance Mechanisms
* Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction, arXiv 2024 [[arXiv](https://arxiv.org/abs/2410.08134)]
* Unlocking Guidance for Discrete State-Space Diffusion and Flow Models, arXiv 2024 [[arXiv](https://arxiv.org/abs/2406.01572)]
* Protein Design with Guided Discrete Diffusion, NeurIPS 2023 [[arXiv](https://arxiv.org/abs/2305.20009), [code](https://github.com/ngruver/NOS)]
* DINOISER: Diffused Conditional Sequence Learning By Manipulating Noises, TACL 2024 [[arXiv](https://arxiv.org/abs/2302.10025), [code](https://github.com/yegcjs/DINOISER)]
* DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models, ACL 2023 [[arXiv](https://arxiv.org/abs/2211.15029), [code](https://github.com/Hzfinfdu/Diffusion-BERT)]
### Theory
* Discrete Copula Diffusion, arXiv 2024 [[arXiv](https://arxiv.org/abs/2410.01949)]
* Formulating Discrete Probability Flow Through Optimal Transport, NeurIPS 2023 [[arXiv](https://arxiv.org/abs/2311.03886), [code](https://github.com/PangzeCheung/Discrete-Probability-Flow)]
* Categorical SDEs with Simplex Diffusion, arXiv 2022 [[arXiv](https://arxiv.org/abs/2210.14784)]
### Applications
* Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design, arXiv 2024 [[arXiv](https://arxiv.org/pdf/2410.13643), [code](https://github.com/ChenyuWang-Monica/DRAKES)]
* Scaling Diffusion Language Models via Adaptation from Autoregressive Models, arXiv 2024 [[arXiv](https://arxiv.org/abs/2410.17891)]
* Scaling up Masked Diffusion Models on Text, arXiv 2024 [[arXiv](https://arxiv.org/abs/2410.18514)]
* Likelihood-Based Diffusion Language Models, NeurIPS 2023 [[arXiv](https://arxiv.org/abs/2305.18619), [code](https://github.com/igul222/plaid)]
* Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning, arXiv 2023 [[arXiv](https://arxiv.org/abs/2308.12219), [code](https://github.com/yegcjs/DiffusionLLM)]
* Diffusion Models for Non-autoregressive Text Generation: A Survey, IJCAI 2023 Survey Track [[arXiv](https://arxiv.org/abs/2303.06574)]
* A Survey of Diffusion Models in Natural Language Processing, arXiv 2023 [[arXiv](https://arxiv.org/abs/2305.14671)]