Instructions to use csbowei/ART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use csbowei/ART with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("csbowei/ART") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Update README.md
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README.md
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---
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license: apache-2.0
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---
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---
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base_model: black-forest-labs/FLUX.1-Kontext-dev
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base_model_relation: adapter
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library_name: diffusers
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license: apache-2.0
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tags:
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- lora
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- flux
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- flux-kontext
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- image-to-image
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- makeup-transfer
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- makeup-removal
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- safetensors
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---
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<div align='center'>
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<h1 align="center">[ECCV 2026] Anchoring on Reality: Breaking the Pseudo-Target Ceiling in Makeup Transfer</h1>
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Bo Wei<sup> 1*</sup> 
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<a href='https://scholar.google.com/citations?user=wLTXeNwAAAAJ&hl=en&oi=ao' target='_blank'>Xianhui Lin</a><sup> 2†</sup> 
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Yi Dong<sup> 2</sup> 
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Zhongzhong Li<sup> 2</sup> 
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Zonghui Li<sup> 2</sup> 
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<a href='https://scholar.google.com/citations?hl=en&user=BhmLztgAAAAJ' target='_blank'>Zirui Wang</a><sup> 2</sup> 
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</div>
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<div align='center'>
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<a href='https://scholar.google.com/citations?hl=en&user=12MzNVkAAAAJ' target='_blank'>Jiachen Yang</a><sup> 2</sup> 
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Xing Liu<sup> 2</sup> 
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Hong Gu<sup> 2</sup> 
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<a href='https://scholar.google.com/citations?hl=en&user=tmT_voUAAAAJ' target='_blank'>Xiaoming Li</a><sup> 3✉</sup> 
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<a href='https://scholar.google.com/citations?hl=en&user=rUOpCEYAAAAJ' target='_blank'>Wangmeng Zuo</a><sup> 1✉</sup>
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</div>
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<div align='center'>
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<sup>1 </sup>Harbin Institute of Technology 
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<sup>2 </sup>vivo BlueImage Lab 
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<sup>3 </sup>Nanjing University
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</div>
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<div align='center'>
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<small><sup>*</sup> Work done during an internship at vivo.</small> 
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<small><sup>†</sup> Project lead</small>
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</div>
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<div align="center">
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<p>
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<a href="https://arxiv.org/abs/2606.31089" target="_blank"><img src="https://img.shields.io/badge/arXiv-ART-red" alt="arXiv link"></a>
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<a href="https://csbowei.github.io/ART/" target="_blank"><img src="https://img.shields.io/badge/Project-Homepage-green" alt="project homepage"></a>
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<a href="https://github.com/csbowei/ART" target="_blank"><img src="https://img.shields.io/badge/GitHub-Code-blue?logo=github" alt="GitHub code"></a>
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</p>
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</div>
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This repository provides the model weights for **ART: Anchoring on Reality: Breaking the Pseudo-Target Ceiling in Makeup Transfer**.
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ART is a makeup transfer framework that applies a reference cosmetic style to a source face while preserving identity, geometry, and fine-grained makeup details.
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## Files
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| File | Task |
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| ----------------------------------- | --------------- |
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| `art_transfer_lora_512.safetensors` | Makeup transfer |
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| `art_demakeup_lora_512.safetensors` | Makeup removal |
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Higher-resolution checkpoints will be released in the future. Stay tuned.
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## Usage
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These LoRA adapters are for [`black-forest-labs/FLUX.1-Kontext-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
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Refer to the project repository: https://github.com/csbowei/ART
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Example:
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```bash
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# Makeup transfer: apply the reference's makeup onto the input face
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python infer.py \
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--task mt \
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--input ./examples/source/src_001.jpg \
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--ref ./examples/ref/ref_001.jpg \
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--lora_path /path/to/art_transfer_lora_512.safetensors \
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--resolution 512
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```
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```bash
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# Makeup removal: strip cosmetics from the input face
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python infer.py \
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--task demakeup \
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--input ./examples/ref/ref_002.jpg \
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--lora_path /path/to/art_demakeup_lora_512.safetensors \
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--resolution 512
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```
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## Citation
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```bibtex
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@article{wei2026art,
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title={Anchoring on Reality: Breaking the Pseudo-Target Ceiling in Makeup Transfer},
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author={Wei, Bo and Lin, Xianhui and Dong, Yi and Li, Zhongzhong and Li, Zonghui and Wang, Zirui and Yang, Jiachen and Liu, Xing and Gu, Hong and Li, Xiaoming and Zuo, Wangmeng},
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journal={arXiv preprint arXiv:2606.31089},
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year={2026}
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}
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```
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