yu-takagi/StableDiffusionReconstruction

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1127 stars · 70 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Research codebase reproducing Takagi and Nishimoto's CVPR 2023 method for reconstructing images a person viewed from fMRI brain activity using Stable Diffusion. It provides end-to-end pipelines for MRI preprocessing, feature extraction, encoding-model training, and latent-diffusion-based image reconstruction, including extensions from the technical paper such as decoding text prompts from brain signals.

Use cases

• reconstruct images from fmri brain activity
• decode what a person is seeing from brain scans
• reproduce the takagi nishimoto CVPR 2023 brain-to-image paper
• convert brain activity into images using stable diffusion
• train encoding models on the natural scenes dataset
• decode text prompts from neural activity
• visual stimulus reconstruction from fMRI data

When to choose

• you want to reproduce or extend the CVPR 2023 high-resolution brain-decoding method
• you have NSD fMRI data and want to reconstruct viewed images or prompts
• you need a reference implementation of latent-diffusion-based visual decoding for research

When to avoid

• you want a polished, production-ready image generation or inference tool
• you lack fMRI datasets, GPU resources, or MRI preprocessing experience
• you need actively maintained software with extensive documentation and support

Facets

library · maturity maintenance

machine-learning deep-learning stable-diffusion image-processing computer-vision artificial-intelligence machine-learning deep-learning computer-vision image-processing data-science python neuroscience brain-decoding fmri mind-reading visual-reconstruction cvpr-2023 research-code generative-ai natural-scenes-dataset jupyter gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="yu-takagi/StableDiffusionReconstruction")