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https://github.com/vividitytech/diffusion-mcmc
https://github.com/vividitytech/diffusion-mcmc
Last synced: 14 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/vividitytech/diffusion-mcmc
- Owner: vividitytech
- Created: 2022-12-20T02:26:07.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-01-04T21:50:04.000Z (almost 2 years ago)
- Last Synced: 2024-08-01T16:53:55.166Z (3 months ago)
- Language: Python
- Size: 13.7 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# diffusion-mcmc
Code for reproducing some key results of our paper Speed up the inference of diffusion models via shortcut MCMC sampling (https://arxiv.org/abs/2301.01206).## Requirement
jaxlib, tensorflow, etc## run example:
python global_vdm_2d.py
## some important parameters:
joinFlag = False # add the flag whether we need joined learning
freeze_x_decoder = False
shortcut = True
K=10 default, which can change based on experiments## reference
we demonstrate our approach based on a 2D swirl dataset and using MLPs, most code base are from VDM [Link to open in Colab](https://colab.research.google.com/github/google-research/vdm/blob/main/colab/2D_VDM_Example.ipynb).if you think it is helpful please cite:
Gang Chen, Speed up the inference of diffusion models via shortcut MCMC sampling (https://arxiv.org/abs/2301.01206).