This repository implements DIFT (Diffusion Features), a method that discovers semantic correspondences between images by extracting and comparing features from the intermediate layers of a pretrained Stable Diffusion model. Rather than training a separate model, it exploits the rich semantic representations that emerge in diffusion features. The code includes a Jupyter notebook for interactive correspondence visualization and scripts for extracting diffusion features as torch tensors for custom applications.