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FreeDrag

(★ 418)

[CVPR 2024] Official implementation of FreeDrag: Feature Dragging for Reliable Point-based Image Editing

파일 탐색기

최종 버전 다운로드 (.zip)

- __init__.py
- util.py
- - drag_pipeline.cpython-38.pyc
- - dummy.txt
- - train_dreambooth_lora.py
  - train_lora.sh
- - pytorch_lora_weights.bin
- - attn_utils.py
  - freedrag_utils.py
  - freeu_utils.py
  - lora_utils.py
  - ui_utils.py
- drag_pipeline.py
- drag_ui.py
- environment.yaml
- readme.txt
- comparison_diffusion_1.png
- comparison_diffusion_2.png
- comparison_gan.png
- fig1.png
- logo2.png
- style.css
- Teaser.png
- - __init__.py
  - bias_act.cpp
  - bias_act.cu
  - bias_act.h
  - bias_act.py
  - conv2d_gradfix.py
  - conv2d_resample.py
  - fma.py
  - grid_sample_gradfix.py
  - upfirdn2d.cpp
  - upfirdn2d.cu
  - upfirdn2d.h
  - upfirdn2d.py
- __init__.py
- custom_ops.py
- misc.py
- persistence.py
- training_stats.py
- __init__.py
- augment.py
- dataset.py
- loss.py
- networks.py
.gitignore
arial.ttf
download_models.sh
FreeDrag_gradio.py
functions.py
legacy.py
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/LPengYang/FreeDrag

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd FreeDrag

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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<p align="center">
  <img src="./resources/logo2.png">
</p>

# FreeDrag: Feature Dragging for Reliable Point-based Image Editing
<p align="center">
  <img src="./resources/Teaser.png">
  <img src="./resources/comparison_diffusion_2.png">
</p>

## Visualization 
[![]](https://user-images.githubusercontent.com/58554846/253733958-c97629a0-5928-476b-99f2-79d5f92762e7.mp4)

## Web Demo (online dragging editing in 11 different StyleGAN2 models)
[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/LPengYang/FreeDrag)

Official implementation of **FreeDrag: Feature Dragging for Reliable Point-based Image Editing**.
- *Authors*: Pengyang Ling*, [Lin Chen*](https://lin-chen.site), [Pan Zhang](https://panzhang0212.github.io/), Huaian Chen, Yi Jin, Jinjin Zheng,
- *Institutes*: University of Science and Technology of China; Shanghai AI Laboratory
- [[Paper]](https://arxiv.org/abs/2307.04684) [[Project Page]](https://lin-chen.site/projects/freedrag) [[Web Demo]](https://openxlab.org.cn/apps/detail/LPengYang/FreeDrag)

This repo proposes FreeDrag, a novel interactive point-based image editing framework free of the laborious and unstable point tracking process🔥🔥🔥.

## Abstract
To serve the intricate and varied demands of image editing, precise and flexible manipulation in image content is indispensable. Recently, Drag-based editing methods have gained impressive performance. However, these methods predominantly center on point dragging, resulting in two noteworthy drawbacks, namely "miss tracking", where difficulties arise in accurately tracking the predetermined handle points, and "ambiguous tracking", where tracked points are potentially positioned in wrong regions that closely resemble the handle points. To address the above issues, we propose **FreeDrag**, a feature dragging methodology designed to free the burden on point tracking. The **FreeDrag** incorporates two key designs, i.e., template feature via adaptive updating and line search with backtracking, the former improves the stability against drastic content change by elaborately controls feature updating scale after each dragging, while the latter alleviates the misguidance from similar points by actively restricting the search area in a line. These two technologies together contribute to a more stable semantic dragging with higher efficiency. Comprehensive experimental results substantiate that our approach significantly outperforms pre-existing methodologies, offering reliable point-based editing even in various complex scenarios.

<p align="center">
  <img src="./resources/comparison_gan.png">
  <img src="./resources/comparison_diffusion_1.png">
</p>

## 📜 News
[2024/03/06] [FreeDrag](https://arxiv.org/abs/2307.04684) is accepted by CVPR 2024.

[2023/12/11] The updataed [FreeDrag](https://arxiv.org/abs/2307.04684) containing the implementations in both StyleGAN and Diffusion models is available now.

[2023/12/8]  FreeDrag based on diffusion model is available now, which support dragging editing in both real images and generated images.

[2023/7/31] The web demo (StyleGAN) in [OpenXLab](https://openxlab.org.cn/apps/detail/LPengYang/FreeDrag) is available now.

[2023/7/28] The function of real image editing is available now.

[2023/7/15] Code of local demo is available now!💥

[2023/7/11] The [paper](https://arxiv.org/abs/2307.04684) and [project page](https://lin-chen.site/projects/freedrag) are released!

## 💡 Highlights
- [x] Local demo of FreeDrag
- [x] Web demo of FreeDrag
- [x] Diffusion-based FreeDrag

## 🛠️Usage

First clone our repository
```
git clone --depth=1 https://github.com/LPengYang/FreeDrag
```
To create a new environment, please follow the requirements of [NVlabs/stylegan2-ada](https://github.com/NVlabs/stylegan2-ada-pytorch#requirements).

**Notice:** It is observed that the errors (setting up PyTorch plugin “bias_act_plugin“... Failed or “upfirdn2d_plugin“... Failed) may appear in some devices, we hope these potential solutions ([1](https://blog.csdn.net/qq_15969343/article/details/129190607), [2](https://github.com/NVlabs/stylegan2-ada-pytorch/issues/155), [3](https://github.com/NVlabs/stylegan3/issues/124), [4](https://github.com/XingangPan/DragGAN/issues/106)) could be helpful in this case.

Then install the additional requirements

```
pip install -r requirements.txt
```

Then download the pre-trained models of stylegan2
```
bash download_models.sh
```
**Notice：**  The first model (face model) could be downloaded very slowly in some cases. In this case, it is suggested to restart the download (works sometimes) or directly download it from this [link](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/research/models/stylegan2/files), please download the correct model (ffhq-512×512) and renamed it as "faces.pkl" and manually put it in the created checkpoints file (after all the other models are downloaded).

Finally initialize the gradio platform for interactive point-based manipulation

```
CUDA_LAUNCH_BLOCKING=1 python FreeDrag_gradio.py
```
You can also upload your images and then edit them. For a high-quality image inversion, it is suggested to make sure that the resolution and style (such as layout) of the uploaded images are consistent with the generated images of corresponding model. The resolution of different model is listed as follows:

|Model|face|horse|elephant|lion|dog|bicycle|giraffe|cat|car|church|metface|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|Resolution|512|256|512|512|1024|256|512|512|512|256|1024|

The proposed **FreeDragBench Dataset** is available on the [website](https://drive.google.com/file/d/1p2muR6aW6fqEGW8yTcHl86DCuUkwgtNY/view?usp=sharing).

## ❤️Acknowledgments
- [DragGAN](https://github.com/XingangPan/DragGAN/)
- [DragDiffusion](https://yujun-shi.github.io/projects/dragdiffusion.html)
- [StyleGAN2](https://github.com/NVlabs/stylegan2-ada-pytorch)
  
## License
All codes used or modified from [StyleGAN2](https://github.com/NVlabs/stylegan2-ada-pytorch) are under the [Nvidia Source Code License](https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt). 
The code related to the FreeDrag algorithm is only allowed for personal activity. The diffusion-based FreeDrag is implemented based on [DragDiffusion](https://yujun-shi.github.io/projects/dragdiffusion.html).

## ✒️ Citation
If you find our work helpful for your research, please consider citing the following BibTeX entry.
```bibtex
@inproceedings{ling2024freedrag,
  title={Freedrag: Feature dragging for reliable point-based image editing},
  author={Ling, Pengyang and Chen, Lin and Zhang, Pan and Chen, Huaian and Jin, Yi and Zheng, Jinjin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6860--6870},
  year={2024}
}
```