| --- |
| 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 |
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| [Paper](https://arxiv.org/abs/2502.14397) | [Hugging Face Model](https://huggingface.co/nicolaus-huang/PhotoDoodle) |
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| 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 |
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| <img src='./assets/teaser.png' width='100%' /> |
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| ## Dataset |
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| This dataset provides paired image data for artistic image editing. Each entry contains: |
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| * **source:** Path to the original image. |
| * **target:** Path to the artistically edited image. |
| * **caption:** A description of the edits applied. |
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| 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). |
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| ## Quick Start (Model Usage) |
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| ### 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 |
| ``` |
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| ### Inference |
| The provided code integrates the `diffusers` pipeline with the PhotoDoodle model. You can run inference using the script: |
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| ```bash |
| python inference.py |
| ``` |
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| or directly use the pipeline: (Code example from original README remains here) |
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| ```python |
| from src.pipeline_pe_clone import FluxPipeline |
| import torch |
| from PIL import Image |
| |
| # ... (rest of the inference code from original README) |
| ``` |
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| ### Model Weights |
| [Hugging Face Model](https://huggingface.co/nicolaus-huang/PhotoDoodle) contains the following weights: |
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| | 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 | |
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| **(Note: You need to load and fuse the `pretrained` checkpoint to use the style models.)** |
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| ### Results |
|  |
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| ## 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}, |
| } |
| ``` |
