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license: cc-by-nc-4.0 # Assuming a Creative Commons license, adjust if needed.
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](https://arxiv.org/abs/2502.14397) | [Hugging Face Model](https://huggingface.co/nicolaus-huang/PhotoDoodle)
Authors: [Huang Shijie](https://scholar.google.com/citations?user=HmqYYosAAAAJ), [Yiren Song](https://scholar.google.com.hk/citations?user=L2YS0jgAAAAJ), [Yuxuan Zhang](https://xiaojiu-z.github.io/YuxuanZhang.github.io/), [Hailong Guo](https://github.com/logn-2024), Xueyin Wang, [Mike Zheng Shou](https://sites.google.com/view/showlab), [Liu Jiaming](https://scholar.google.com/citations?user=SmL7oMQAAAAJ&hl=en)
[Show Lab](https://sites.google.com/view/showlab), National University of Singapore
<img src='./assets/teaser.png' width='100%' />
## 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](https://huggingface.co/datasets/nicolaus-huang/PhotoDoodle). See the [dataset README](./data/README.md) for further details (add if a separate README exists).
## Quick Start (Model Usage)
### Configuration
1. **Environment setup:**
```bash
git clone git@github.com:showlab/PhotoDoodle.git
cd PhotoDoodle
conda create -n doodle python=3.11.10
conda activate doodle
```
2. **Requirements installation:**
```bash
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:
```bash
python inference.py
```
or directly use the pipeline: (Code example from original README remains here)
```python
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](https://huggingface.co/nicolaus-huang/PhotoDoodle) 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},
}
``` |