Badr-MOUFAD / hackathon_starter_kit

Starter code for the Hackathon "Zero-shot Diffusion based image restoration"

Hackathon starter-kit

Zero-shot Diffusion based image restoration.

The goal of the hackathon is to guide the participants to develop robust and efficient algorithms that leverage the advances in DMs to solve inverse problems with no-additional training of the models. The focus will be common tasks encountered in image restoration such as image inpainting and Super Resolution.

Installation

Beforehand, ensure to download the code. You can use git or download it as zip.

1. Run the following the command to create a fresh Python environment.

python3 -m venv venv-hackathon

1. Activate the environment

source venv-hackathon/bin/activate

1. then install the project on editable mode

pip install -e .

1. Finally, download FFHQ model checkpoint and put it on material/checkpoints folder.

Do not forget to put the absolute path of the project in py_source/local_paths.py. Similarly, put the absolute path to FFHQ checkpoint in /py_source/configs/ffhq_model.yaml

About the repository structure

The material folder contains external files such images, and model checkpoints.

Essential functions and classes to load pre-trained Diffusion Models, load images, display them, and initialize inverse problem are located in py_source/ folder. In particular,

• py_source/sampling/ folder contains examples of algorithm for solving inverse problem
• py_source/utils.py contains functions to load model, images, and plot them

There are two notebooks to help you get started

• demo_inverse_problems.ipynb shows how to define an inverse problem, solve it with an algorithm, and visualize the result
• demo_evaluation.ipynb explains and illustrates the evaluation process of an algorithm

Note

• To avoid installation conflicts, the code of the following repositories was moved/modified inside src folder
  • https://github.com/gabrielvc/mcg_diff
  • https://github.com/openai/guided-diffusion
• Link to download FFHQ model checkpoint https://drive.google.com/drive/folders/1jElnRoFv7b31fG0v6pTSQkelbSX3xGZh
• Evaluation script and the inverse problems used will be uploaded later during the week