This repository contains the code accompanying the paper Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence. The code implements Diffusion Hyperfeatures, a framework for consolidating multi-scale and multi-timestep feature maps from a diffusion model into per-pixel feature descriptors.

This code was tested with Python 3.8. To install the necessary packages, please run:

conda env create -f environment.yml
conda activate dhf

To extract and save Diffusion Hyperfeatures for your own set of real images, or a set of synthetic images with your own custom prompts, run extract_hyperfeatures.py.

To run on real images, you can provide a folder of images with or without corresponding annotations.

python3 extract_hyperfeatures.py --save_root hyperfeatures --config_path configs/real.yaml --image_root assets/spair/images --images_or_prompts_path annotations/spair_71k_test-6.json 

python3 extract_hyperfeatures.py --save_root hyperfeatures --config_path configs/real.yaml --image_root assets/spair/images --images_or_prompts_path ""

To run on synthetic images, you can provide a json file containing a list of prompts.

python3 extract_hyperfeatures.py --save_root hyperfeatures --config_path configs/synthetic.yaml  --image_root "" --images_or_prompts_path annotations/synthetic-3.json

We also provide demos for the semantic keypoint matching task using Diffusion Hyperfeatures.

For real images, real_demo waks through visualizing correspondences using either nearest neighbors or mutual nearest neighbors.

For synthetic images, synthetic_demo provides an interactive demo for visualizing correspondences given different prompts and different sets of user annotated source points.

@article{luo2023dhf,
  author    = {Luo, Grace and Dunlap, Lisa and Park, Dong Huk and Holynski, Aleksander and Darrell, Trevor},
  title     = {Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence},
  journal   = {arXiv},
  year      = {2023},
}

Our codebase builds on top of a few prior works, including Deep ViT Features as Dense Visual Descriptors, Zero-Shot Category-Level Object Pose Estimation, Shape-Guided Diffusion, and ODISE.