📚 FastSAE is the packaged successor to PatchSAE (ICLR 2025), refactored for easier adoption. See the paper's reference implementation there, or jump straight into the tutorial below.
Follow these steps to install and verify FastSAE.
# 1) Create and activate a fresh environment
conda create -n fastsae python=3.12 -y
conda activate fastsae
# 2) Install project dependencies
pip install -r requirements.txt
# 3) Install PyTorch matching your system (CUDA/CPU)
# See official instructions at `https://pytorch.org/get-started/locally/`
# (If you already have a working torch install, you can skip this.)
# 4) Install FastSAE in editable mode
pip install -e .
# 5) (Optional) Dev tools
pip install -U pre-commit ruff
pre-commit installCreate a .env file in the repo root to configure paths and runtime behavior.
Or use the provided example to create your .env:
cp env.example .envQuick checks:
# Verify install and version
python -c "import fastsae, torch; print('fastsae', fastsae.__version__, '| cuda:', torch.cuda.is_available())"👉 Follow examples/reproduce_patchsae/tutorial.ipynb.
This shows how to use FastSAE package by reproducing PatchSAE paper.
- Fix ghost grad bug: use pre-activations (before ReLU) for dead neurons instead of post-activations, matching the PatchSAE reference. Previously, all dead neurons received
exp(0)=1, collapsingW_decdirections.
- Initial release.
If you find our code or models useful in your work, please cite our paper:
@inproceedings{
lim2025patchsae,
title={Sparse autoencoders reveal selective remapping of visual concepts during adaptation},
author={Hyesu Lim and Jinho Choi and Jaegul Choo and Steffen Schneider},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=imT03YXlG2}
}
