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🥚 EGG: Energy Guided Diffusion for optimizing neurally exciting images

This is the official implementation of the paper "Energy Guided Diffusion for Generating Neurally Exciting Images" accepted at NeurIPS 2023.

EGG diffusion generated neurally exciting images

Energy Guided Diffusion for Generating Neurally Exciting Images,
Pawel A. Pierzchlewicz, Konstantin F. Willeke, Arne F. Nix, Pavithra Elumalai, Kelli Restivo, Tori Shinn, Cate Nealley, Gabrielle Rodriguez, Saumil Patel, Katrin Franke, Andreas S. Tolias, and Fabian H. Sinz

[NeurIPS] [bioRxiv]

This repository is based on the guided-diffusion repository.

Installation

Package Requirements

Some packages need to be downloaded manually. Run the following commands to download the required packages:

./download_requirements.sh

You can install the remaining packages by running:

pip install -e ./lib/nnvision
pip install -e ./lib/mei
pip install -e .

Pre-trained model weights

To run EGG you need to download the pre-trained weights of the ADM model. The experiments use a model pretrained by OpenAI on 256x256 ImageNet images.

Model Weights
ImageNet 256x256 uncond 256x256_diffusion_uncond.pt
Attention Readout + Data Driven Core HuggingFace

place the weights in the models folder.

Usage

Here is a minimal example for running the EGG diffusion on a pretrained model.

from functools import partial

import torch


from egg.models import models
from egg.diffusion import EGG

# Setup the parameters
energy_scale = 5
num_samples = 1
num_steps = 50
device = 'cuda' if torch.cuda.is_available() else 'cpu'

def energy_fn(pred_x_0, unit_idx=0, contrast=50):
    """
    Energy function for optimizing MEIs, i.e. images that maximally excite a given unit.

    :param pred_x_0: the predicted "clean" image
    :param unit_idx: the index of the unit to optimize
    :param contrast: the contrast of the image
    :return: the predicted neural response of the predicted image for the given unit
    """
    x = F.interpolate(
        pred_x_0.clone(), size=(100, 100), mode="bilinear", align_corners=False
    ).mean(1, keepdim=True) # resize to 100x100 and convert to grayscale
    
    x = x / torch.norm(x) * contrast
    
    return dict(train=-models['task_driven']['train'](x)[..., unit_idx])


diffusion = EGG(
    diffusion_artefact='./models/256x256_diffusion_uncond.pt',
    num_steps=num_steps
)

samples = diffusion.sample(
    energy_fn=partial(energy_fn, unit_idx=0),
    energy_scale=energy_scale
)

Reproducing the experiments

First the docker image needs to be built:

docker compose build base

This will setup the docker image with all the required packages. This will take a while, but only needs to be done once.

The experiments can then be reproduced by running the scripts in the scripts folder. For example, to reproduce the experiments generating MEIs using EGG run:

docker compose run --rm python scripts/meis_egg.py

Finally, the analysis of the results and plots can be found in the notebooks folder.

Citation

If you find this repository useful for your research, please cite the following paper:

@article{Pierzchlewicz2023,
  title    = "Energy Guided Diffusion for Generating Neurally Exciting Images",
  author   = "Pierzchlewicz, Pawe{\l} A and Willeke, Konstantin F and Nix, Arne
              F and Elumalai, Pavithra and Restivo, Kelli and Shinn, Tori and
              Nealley, Cate and Rodriguez, Gabrielle and Patel, Saumil and
              Franke, Katrin and Tolias, Andreas S and Sinz, Fabian H",
  abstract = ",
  journal  = "bioRxiv",
  pages    = "2023.05.18.541176",
  month    =  may,
  year     =  2023,
  language = "en"
}