DiffStyler: Diffusion-based Localized Image Style Transfer [Arxiv Paper]
Create the environment and install the dependencies by running:
conda create -n diffstyler python=3.9
conda activate diffstyler
pip install -r requirements.txt
Train the style LoRAs of the style images.
python lora_train.py --data_path <style_image> --prompt <style_prompt>
Compute the intermediate noisy latents of the structure guidance image.
python preprocess.py --data_path <content_image>
Run the following command for applying Localized Image Style Transfer:
python diffstyler.py --config_path <config_path>
The corresponding masks are calculated using FastSAM(https://github.com/CASIA-IVA-Lab/FastSAM).
We provide two demos for testing:
bash demo1.sh
bash demo2.sh
@misc{li2024diffstyler,
title={DiffStyler: Diffusion-based Localized Image Style Transfer},
author={Shaoxu Li},
year={2024},
eprint={2403.18461},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation. (https://github.com/MichalGeyer/pnp-diffusers)
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