Brain-Diffuser

Instructions

Requirements

• Create conda environment using environment.yml in the main directory by entering conda env create -f environment.yml . It is an extensive environment and may include redundant libraries. You may also create environment by checking requirements yourself.

Data Acquisition and Processing

1. Download NSD data from NSD AWS Server:

   cd data
   python download_nsddata.py

2. Download "COCO_73k_annots_curated.npy" file from HuggingFace NSD
3. Prepare NSD data for the Reconstruction Task:

   cd data
   python prepare_nsddata.py -sub 1
   python prepare_nsddata.py -sub 2
   python prepare_nsddata.py -sub 5
   python prepare_nsddata.py -sub 7

First Stage Reconstruction with VDVAE

1. Download pretrained VDVAE model files and put them in vdvae/model/ folder

wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-log.jsonl
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-opt.th

1. Extract VDVAE latent features of stimuli images for any subject 'x' using python scripts/vdvae_extract_features.py -sub x
2. Train regression models from fMRI to VDVAE latent features and save test predictions using python scripts/vdvae_regression.py -sub x
3. Reconstruct images from predicted test features using python scripts/vdvae_reconstruct_images.py -sub x

Second Stage Reconstruction with Versatile Diffusion

1. Download pretrained Versatile Diffusion model "vd-four-flow-v1-0-fp16-deprecated.pth", "kl-f8.pth" and "optimus-vae.pth" from HuggingFace and put them in versatile_diffusion/pretrained/ folder

1. Extract CLIP-Vision features of stimuli images for any subject 'x' using python scripts/clipvision_extract_features.py -sub x

1. Train regression models from fMRI to CLIP-Vision features and save test predictions using python scripts/clipvision_regression.py -sub x
2. Reconstruct images from predicted test features using python scripts/versatilediffusion_reconstruct_images.py -sub x . This code is written as you are using two 12GB GPUs but you may edit according to your setup.

References

• Codes in vdvae directory are derived from openai/vdvae
• Codes in versatile_diffusion directory are derived from earlier version of SHI-Labs/Versatile-Diffusion
• Dataset used in the studies are obtained from Natural Scenes Dataset