https://github.com/alexiajm/score_sde_fast_sampling
Repository for the "Gotta Go Fast When Generating Data with Score-Based Models" paper
https://github.com/alexiajm/score_sde_fast_sampling
Last synced: over 1 year ago
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Repository for the "Gotta Go Fast When Generating Data with Score-Based Models" paper
- Host: GitHub
- URL: https://github.com/alexiajm/score_sde_fast_sampling
- Owner: AlexiaJM
- Created: 2021-06-01T11:22:32.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-11-20T21:59:57.000Z (over 4 years ago)
- Last Synced: 2025-03-24T11:21:20.227Z (over 1 year ago)
- Language: Python
- Size: 1.18 MB
- Stars: 104
- Watchers: 4
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# Gotta Go Fast When Generating Data with Score-Based Models
This repo contains the official implementation for the paper [Gotta Go Fast When Generating Data with Score-Based Models](https://arxiv.org/abs/2105.14080), which shows how to generate data as fast as possible with score-based models using a well-designed SDE solver. See the [blog post](https://ajolicoeur.wordpress.com/gotta-go-fast-with-score-based-models/) for more details.
--------------------
This code is a heavy modification of the [Generative Modeling through Stochastic Differential Equations repository](https://github.com/yang-song/score_sde).
## 2021-09-21: Using the GottaGoFast sampler in Torch
If you want the PyTorch version, please use https://github.com/yang-song/score_sde_pytorch/blob/main/sde_lib.py along with my modified version of https://github.com/yang-song/score_sde_pytorch/blob/main/sampling.py which is here: https://github.com/AlexiaJM/score_sde_fast_sampling/blob/main/sde_sampling_torch.py.
Notes:
- I removed the other methods pc methods in this new file, feel free to add them back based on https://github.com/yang-song/score_sde_pytorch/blob/main/sampling.py
- I removed a lot of options which were unnecessary from the algorithm to simplify things
## To run the experiments in the paper
See the [requirements](https://github.com/AlexiaJM/score_sde_fast_sampling/blob/main/requirements.txt).
Change the settings and folders in https://github.com/AlexiaJM/score_sde_fast_sampling/blob/main/experiments.sh and run parts of the script to run the CIFAR-10, LSUN-Church, and FFHQ experiments.
The SDE solver can be found [here](https://github.com/AlexiaJM/score_sde_fast_sampling/blob/main/sampling.py#L172) and the loop [here](https://github.com/AlexiaJM/score_sde_fast_sampling/blob/main/sampling.py#L574).
## For general usage
Please refer to the [original code](https://github.com/yang-song/score_sde).
## Pretrained checkpoints
https://drive.google.com/drive/folders/10pQygNzF7hOOLwP3q8GiNxSnFRpArUxQ?usp=sharing
## References
If you find the code useful for your research, please consider citing
```bib
@article{jolicoeurmartineau2021gotta,
title={Gotta Go Fast When Generating Data with Score-Based Models},
author={Alexia Jolicoeur-Martineau and Ke Li and R{\'e}mi Pich{\'e}-Taillefer and Tal Kachman and Ioannis Mitliagkas},
journal={arXiv preprint arXiv:2105.14080},
year={2021}
}
```
and
```bib
@inproceedings{
song2021scorebased,
title={Score-Based Generative Modeling through Stochastic Differential Equations},
author={Yang Song and Jascha Sohl-Dickstein and Diederik P Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=PxTIG12RRHS}
}
```
Official theme song can be found here: https://soundcloud.com/emyaze/gotta-go-fast.
## Samples (see the paper for more samples)
