zoubohao/DenoisingDiffusionProbabilityModel-ddpm-

Python

This may be the simplest implement of DDPM. You can directly run Main.py to train the UNet on CIFAR-10 dataset and see the amazing process of denoising.

ddpm

주요 지표

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스타

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2022년 6월2023년 2월2023년 10월2024년 6월2025년 3월2025년 11월2026년 7월

아티팩트PyPIpip install denoisingdiffusionprobabilitymodel-ddpm-

README

DenoisingDiffusionProbabilityModel

This may be the simplest implement of DDPM. I trained with CIFAR-10 dataset. The links of pretrain weight, which trained on CIFAR-10 are in the Issue 2.

If you really want to know more about the framwork of DDPM, I have listed some papers for reading by order in the closed Issue 1.

Lil' Log is also a very nice blog for understanding the details of DDPM, the reference is "https://lilianweng.github.io/posts/2021-07-11-diffusion-models/#:~:text=Diffusion%20models%20are%20inspired%20by,data%20samples%20from%20the%20noise."

HOW TO RUN

1. You can run Main.py to train the UNet on CIFAR-10 dataset. After training, you can set the parameters in the model config to see the amazing process of DDPM.
1. You can run MainCondition.py to train UNet on CIFAR-10. This is for DDPM + Classifier free guidence.

Some generated images are showed below:

1. DDPM without guidence:

Generated Images without condition

1. DDPM + Classifier free guidence:

Generated Images with condition