To plug-and-play diffusion features, please follow these steps:
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
We first compute the intermediate noisy latents of the structure guidance image. To do that, run:
python preprocess.py --data_path <path_to_guidance_image> --inversion_prompt <inversion_prompt>
where <inversion_prompt> should describe the content of the guidance image. The intermediate noisy latents will be saved under the path latents_forward/<image_name>, where <image_name> is the filename of the provided guidance image.
Run the following command for applying PnP on the structure guidance image:
python pnp.py --config_path <pnp_config_path>
where <pnp_config_path> 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.
@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}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent