README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - image-to-image
tags:
  - image-editing
  - artistic-style-transfer
  - few-shot-learning

PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data

Paper | Hugging Face Model

Authors: Huang Shijie, Yiren Song, Yuxuan Zhang, Hailong Guo, Xueyin Wang, Mike Zheng Shou, Liu Jiaming Show Lab, National University of Singapore

Dataset

This dataset provides paired image data for artistic image editing. Each entry contains:

• source: Path to the original image.
• target: Path to the artistically edited image.
• caption: A description of the edits applied.

The dataset is available on Hugging Face Datasets. See the dataset README for further details (add if a separate README exists).

Quick Start (Model Usage)

Configuration

1. Environment setup:

   git clone git@github.com:showlab/PhotoDoodle.git
   cd PhotoDoodle
   conda create -n doodle python=3.11.10
   conda activate doodle
   

2. Requirements installation:

   pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
   pip install --upgrade -r requirements.txt
   

Inference

The provided code integrates the diffusers pipeline with the PhotoDoodle model. You can run inference using the script:

python inference.py

or directly use the pipeline: (Code example from original README remains here)

from src.pipeline_pe_clone import FluxPipeline
import torch
from PIL import Image

# ... (rest of the inference code from original README)

Model Weights

Hugging Face Model contains the following weights:

Model Name	Description	Resolution
pretrain.safetensors	Base PhotoDoodle model	768, 768
sksmonstercalledlulu.safetensors	Style model for Cartoon monster	768, 512
sksmagiceffects.safetensors	Style model for 3D effects	768, 512
skspaintingeffects.safetensors	Style model for Flowing color blocks	768, 512
sksedgeeffect.safetensors	Style model for Hand-drawn outline	768, 512

(Note: You need to load and fuse the pretrained checkpoint to use the style models.)

Results

Citation

@misc{huang2025photodoodlelearningartisticimage,
      title={PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data}, 
      author={Shijie Huang and Yiren Song and Yuxuan Zhang and Hailong Guo and Xueyin Wang and Mike Zheng Shou and Jiaming Liu},
      year={2025},
      eprint={2502.14397},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.14397}, 
}