File size: 2,845 Bytes
256c9c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # Denoising Diffusion Probabilistic Models (DDPM)
Implementation of [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) (Ho, Jain, Abbeel, 2020).
## What this implements
The DDPM training and sampling procedure — a class of generative models that learn to reverse a gradual noising process. The core contribution is the training objective (simplified variational bound) and the reverse sampling algorithm, NOT the U-Net architecture (which is adapted from prior work). This implementation covers the forward diffusion process, the simplified training objective (L_simple), the noise schedule, and the reverse sampling algorithm from Algorithm 1 and Algorithm 2.
## Quick start
```bash
pip install -r requirements.txt
```
```python
from src.model import UNet, UNetConfig
from src.loss import DDPMLoss
from src.utils import linear_noise_schedule
config = UNetConfig()
model = UNet(config)
noise_schedule = linear_noise_schedule(timesteps=1000)
# Example: predict noise from noisy image at timestep t
import torch
x_t = torch.randn(2, 3, 32, 32) # noisy image
t = torch.randint(0, 1000, (2,)) # timesteps
predicted_noise = model(x_t, t)
print(predicted_noise.shape) # (2, 3, 32, 32)
```
## File structure
```
ddpm/
├── README.md # This file
├── REPRODUCTION_NOTES.md # Ambiguity audit — what's specified vs. assumed
├── requirements.txt # Dependencies
├── src/
│ ├── model.py # U-Net noise prediction network (§3.3, Appendix B)
│ ├── loss.py # DDPM simplified loss L_simple (§3.4, Eq. 14)
│ ├── data.py # Dataset skeleton for image data
│ ├── train.py # Training loop — Algorithm 1
│ ├── evaluate.py # FID score computation
│ └── utils.py # Noise schedule, forward process, sampling (Algorithm 2)
├── configs/
│ └── base.yaml # All hyperparameters from §4 and Appendix B
└── notebooks/
└── walkthrough.ipynb # Paper sections → code → sanity checks
```
## Important: Read REPRODUCTION_NOTES.md
This implementation flags every choice that the paper does not specify.
Before using this code for research, read [REPRODUCTION_NOTES.md](REPRODUCTION_NOTES.md)
to understand which implementation details are from the paper and which are our choices.
## Citation
```bibtex
@article{ho2020denoising,
title={Denoising diffusion probabilistic models},
author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
journal={Advances in neural information processing systems},
volume={33},
pages={6840--6851},
year={2020}
}
```
---
*Generated by [paper2code](https://github.com/PrathamLearnsToCode/paper2code) — citation-anchored paper implementation.*
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