Tiny-tiny PyTorch implementation of diffusion probabilistic models [1] for self-study, based on the amazing tiny-diffusion repository. All this repo, tiny-tiny-diffusion, can do is reconstruct a 2D dinosaur 🦖 from random noise (but it's cool, right?)
Thanks to the simplicity and limited capabilities, the training can be done in a few minutes with my laptop without GPUs. The inference speed is negligible 🚅.
You need Poetry. After cloning this repo, run the following to complete the setup:
poetry installThe demo is available in examples.ipynb.
You can start Jupyter Lab with the following command:
poetry run jupyter labActually, this repo can also draw a cat 🐈 by learning new data:
poetry run python ddpm/train.py dataset.csv_file=assets/shape/cat.csv
The trained model will be saved in learning_results.
The training script uses Hydra to specify hyperparameters.
A base configuration can be found in ddpm/train_conf/example.yaml.
- Okay, so you are interested in diffusion models. Yay! But I am not an ML guy. Indeed, the original paper [1] was quite difficult to understand without preliminary knowledge.
- After skimming some blog posts, I came across [2]; I recommend this paper as a starting point.
- Okay, I was able to grasp the basics. But it is always good to code it for a better understanding. So I searched through some code repositories and found tiny-diffusion. Quite impressive! I decided to mimic it for leaning.
- Although the code in this repository has been rewritten from scratch for self-study, I have heavily referenced tiny-diffusion. Therefore, I don't strongly claim authorship. Please respect the original repository.
- Please remember that I am NOT an ML person. Maybe I made some mistakes. I am still learning!
- The shape data in
assets/shapewas retrieved from Data Morph. - As a minimalist, the repo omits positional encodings; it works
- Setup auto formatting when committing with pre-commit:
poetry run pre-commit install- Ho, J., Jain, A., & Abbeel, P. Denoising diffusion probabilistic models. NeurIPS. 2020.
- Luo, C. Understanding diffusion models: A unified perspective. arXiv preprint arXiv:2208.11970. 2022.