High-resolution image reconstruction with latent diffusion models from human brain activity

Takagi and Nishimoto, CVPR 2023

[Paper] [Technical Paper] [Project Page] [FAQ] [FAQ(Japanese)]

General Information

This is a repository for reproducing the method we presented (Takagi and Nishimoto, CVPR 2023) for visual experience reconstruction from brain activity using Stable Diffusion.

Based on our earlier work (Takagi and Nishimoto, CVPR 2023), we further examined the extent to which various additional decoding techniques affect the performance of reconstructing visual experience in a following Technical Paper, including a method of decoding text prompt from the brain (see figure below). These methods are also available in this repository.

We confirmed that adding several techniques contribute to improving the accuracy from Takagi and Nishimoto CVPR 2023. In the figure below, for each method, three generated images from different stochastic noise were randomly chosen.

Environment setup

Download nsddata, nsddata_betas, and nsddata_stimuli from NSD and place them under the nsd directory.
pip install -r requirements.txt
Install Stable Diffusion v1.4 (under the diffusion_sd1/ directory), download checkpoint (sd-v1-4.ckpt), and place it under the codes/diffusion_sd1/stable-diffusion/models/ldm/stable-diffusion-v1/ directory.
For incorporating GAN, install bdpy (under the gan/ directory), download VGG_ILSVRC_19_layers and bvlc_reference_caffenet_generator_ILSVRC2012_Training from https://figshare.com/articles/dataset/brain-decoding-cookbook/21564384, and place them under the codes/gan/models/pytorch/ directory.
For incorporating decoded depth, install Stable Diffusion v2.0 (under the diffusion_sd2/ directory), download checkpoint (512-depth-ema.ckpt), and place it under the codes/diffusion_sd2/stablediffusion/models/ directory.

MRI Preprocessing

cd codes/utils/
python make_subjmri.py --subject subj01

Reconstruction based on CVPR method

cd codes/utils/
python img2feat_sd1.py  --imgidx 0 73000 --gpu 0
python make_subjstim.py --featname init_latent --use_stim each --subject subj01
python make_subjstim.py --featname init_latent --use_stim ave --subject subj01
python make_subjstim.py --featname c --use_stim each --subject subj01
python make_subjstim.py --featname c --use_stim ave --subject subj01
python ridge.py --target c --roi ventral --subject subj01
python ridge.py --target init_latent --roi early --subject subj01

cd codes/diffusion_sd1/
python diffusion_decoding.py --imgidx 0 10 --gpu 1 --subject subj01 --method cvpr

Reconstruction with Decoded Text Prompt

cd codes/caption/BLIP/
python img2feat_blip.py --gpu 0

cd codes/utils/
python make_subjstim.py --featname blip --use_stim ave --subject subj01
python make_subjstim.py --featname blip --use_stim each --subject subj01
python ridge.py --target blip --roi early ventral midventral midlateral lateral parietal  --subject subj01

cd codes/caption/BLIP/
python decode_captions.py --subject subj01

cd codes/diffusion_sd1/
python diffusion_decoding.py --imgidx 0 --gpu 1 --subject subj01 --method text

Reconstruction with Decoded Text Prompt + GAN

cd codes/gan/bdpy/
python setup.py install

cd codes/gan/
python make_vgg19bdpy.py --imgidx 0 73000

cd codes/utils/
(run the following code from conv1_1 to fc8.)
python make_subjstim_vgg19.py --layer conv1_1 --subject subj01
python ridge.py --target conv1_1 --roi early ventral midventral midlateral lateral parietal --subject subj01

cd codes/gan/
python make_vgg19fromdecode.py --subject subj01
python recon_icnn_image_vgg19_dgn_relu7gen_gd.py

cd codes/diffusion_sd1/
python diffusion_decoding.py --imgidx 0 --gpu 0 --subject subj01 --method gan

Reconstruction with Decoded Text Prompt + GAN + Decoded Depth

Need to pip install -U transformers. Note that this update may cause BLIP to stop working. It is recommended to do this in a different environment.

cd codes/depth/
python img2feat_dpt.py --imgidx 0 73000 --gpu 0

cd codes/utils/
(run the following code from dpt_emb0 to dpt_emb3.)
python make_subjstim.py --featname dpt_emb0 --use_stim ave --subject subj01
python make_subjstim.py --featname dpt_emb0 --use_stim each --subject subj01
python ridge.py --target dpt_emb0 --roi early ventral midventral midlateral lateral parietal --subject subj01
python ridge.py --target dpt_emb0 --roi early ventral midventral midlateral lateral parietal --subject subj01

cd codes/depth/
python dptemb2dpt.py --gpu 0 --subject subj01

cd codes/diffusion_sd2/
python diffusion_decoding.py --imgidxs 0 1 --gpu 0 --subject subj01

Evaluation

cd codes/utils/
python img2feat_decoded.py --gpu 0 --subject subj01 --method cvpr
python identification.py --usefeat inception --subject subj01 --method cvpr

Citation

Original paper.

@inproceedings{takagi2023high,
  title={High-resolution image reconstruction with latent diffusion models from human brain activity},
  author={Takagi, Yu and Nishimoto, Shinji},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14453--14463},
  year={2023}
}

Following technical report.

@misc{takagi2023improving,
      title={Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs}, 
      author={Takagi, Yu and Nishimoto, Shinji},
      year={2023},
      eprint={2306.11536},
      archivePrefix={arXiv},
      primaryClass={q-bio.NC}
}

Acknowledgement

Our codebase builds on these repositories. We would like to thank the authors.

https://github.com/CompVis/stable-diffusion

https://github.com/Stability-AI/stablediffusion

https://github.com/gallantlab/himalaya

https://github.com/tknapen/nsd_access

https://github.com/salesforce/BLIP

https://github.com/KamitaniLab/bdpy

https://github.com/KamitaniLab/brain-decoding-cookbook-public

https://github.com/isl-org/DPT

Contact

Plase email if you have any questions.

yu-takagi/StableDiffusionReconstruction

yu-takagi

Reviews

Repository Details