Awesome

Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation (CVPR 2023)

[<a href="https://pnp-diffusion.github.io/" target="_blank">Project Page</a>]

<a href="https://replicate.com/arielreplicate/plug_and_play_image_translation"><img src="https://replicate.com/arielreplicate/plug_and_play_image_translation/badge"></a>

To plug-and-play diffusion features, please follow these steps:

1. Setup
2. Latent extraction
3. 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 <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.

Running PnP

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.

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}
}