ssilenzi / iadb

Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model

Project page | Paper

This repository is the official implementation of IADB (Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model), published at Siggraph 2023.

For additional information and explanations, please visit the project page.

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 iadb
python iadb.py

Setup

Python 3 dependencies:

• Pytorch
• torchvision
• Diffusers

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 IADB model and how to generate results (using the provided sample_iadb function).