To run the GESS model, you need to install the required packages. First, we create a conda environment and install the necessary packages for the latent diffusion model:

conda env create -f environment.yaml
conda activate ldm

pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0
pip install git+https://github.com/arogozhnikov/einops.git

Due to time constraints, we provide a few examples of our preprocessed structural features (pre-extracted by CycleGAN [3]) and semantic features for demonstrating the component substitution method used in the paper. We are working on organizing the complete code.

To run this demo, you need to download the pretrained diffusion model[1] for 768x768 resolution from the following link:

https://ommer-lab.com/files/rdm/model.ckpt

Save the downloaded file in the "./all_data/" directory. Then, run the following command:

python god_gen.py -sub 3

The generated results are saved in the "./outputs/" folder. Here is an example of the results for subject 3:

The model runs on a single GPU (NVIDIA GeForce RTX 3090) and approximately 7 ddim sampling steps are performed every second. We would like to thank the authors of the RDM code [2], as our project is based on it.

[1] Blattmann A, Rombach R, Oktay K, et al. Semi-Parametric Neural Image Synthesis[C]//Advances in Neural Information Processing Systems. 2022.

[2] Rombach R, Blattmann A, Lorenz D, et al. High-resolution image synthesis with latent diffusion models[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 10684-10695.

[3] Beliy R, Gaziv G, Hoogi A, et al. From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI[J]. Advances in Neural Information Processing Systems, 2019, 32.