# Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation (CVPR 2023)
## [Project Page]
[](https://arxiv.org/abs/2211.12572) [](https://huggingface.co/spaces/hysts/PnP-diffusion-features) 
[](https://www.dropbox.com/sh/8giw0uhfekft47h/AAAF1frwakVsQocKczZZSX6La?dl=0)
**To plug-and-play diffusion features, please follow these steps:**
1. [Setup](#setup)
2. [Latent extraction](#latent-extraction)
3. [Running PnP](#running-pnp)
## Setup
Create the environment and install the dependencies by running:
```
conda create -n pnp-diffusers python=3.9
conda activate pnp-diffusers
pip install -r requirements.txt
```
## Latent Extraction
We first compute the intermediate noisy latents of the structure guidance image. To do that, run:
```
python preprocess.py --data_path --inversion_prompt
```
where `` should describe the content of the guidance image. The intermediate noisy latents will be saved under the path `latents_forward/`, where `` is the filename of the provided guidance image.
## Running PnP
Run the following command for applying PnP on the structure guidance image:
```
python pnp.py --config_path
```
where `` is a path to a yaml config file. The config includes fields for providing the guidance image path, the PnP output path, translation prompt, guidance scale, PnP feature and self-attention injection thresholds, and additional hyperparameters. See an example config in `config_pnp.yaml`.
## Citation
```
@InProceedings{Tumanyan_2023_CVPR,
author = {Tumanyan, Narek and Geyer, Michal and Bagon, Shai and Dekel, Tali},
title = {Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {1921-1930}
}
```