MiniDiffuser
MiniDiffuser is a mini diffusion model based on IADB (See paper, blog post and, 2D tutorial).
Quick start
If you want to setup a new conda environment, download a dataset (celeba) and launch a training, you can follow this:
conda env create -f environment.yml
conda activate minid
python3 minid.py
Replace python3 minid.py with python3 minid.py --e 10 for a quicker training process (this set epoch size to 10 instead the default 100).
Exit
Press crt+c to exit training.
To exit your environment run:
conda deactivate
Help
To see all features flags use:
python3 minid.py -h
Mini Diffuser currently support celeba and cifar10 (default) as dataset, modify minid.py to add yours.
Update you environment
To update your environment run:
conda env update -f environment.yml
Cuda
By default, our environment configuration for PyTorch supports Cuda version 12.1. Run nvidia-smi to see your Cuda version. And consult the officical PyTorch website for optimize version. And configure environment.yml or install dependencies manually.
Setup
Python 3 dependencies:
This code has been tested with Python 3.8 on Ubuntu 22.04. We recommend setting up a dedicated Conda environment using Python 3.8 and Pytorch 2.0.1.
Code description
The iadb.py contains a simple training loop.
It demonstrates how to train a new MiniDiffuser model and how to generate results.