chore: update model card
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README.md
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<a href='https://github.com/cleardusk' target='_blank'><strong>Jianzhu Guo</strong></a><sup> 1β </sup> 
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<a href='https://github.com/KwaiVGI' target='_blank'><strong>Dingyun Zhang</strong></a><sup> 1,2</sup> 
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<a href='https://github.com/KwaiVGI' target='_blank'><strong>Xiaoqiang Liu</strong></a><sup> 1</sup> 
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<a href='https://
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<a href='https://scholar.google.com.hk/citations?user=_8k1ubAAAAAJ' target='_blank'><strong>Yuan Zhang</strong></a><sup> 1</sup> 
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</div>
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</div>
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<br>
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<div align="center"
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<!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
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<a href='https://arxiv.org/pdf/2407.03168'><img src='https://img.shields.io/badge/arXiv-LivePortrait-red'></a>
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<a href='https://liveportrait.github.io'><img src='https://img.shields.io/badge/Project-LivePortrait-green'></a>
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## π₯ Updates
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- **`2024/07/
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- **`2024/07/
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## Introduction
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This repo, named **LivePortrait**, contains the official PyTorch implementation of our paper [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://arxiv.org/pdf/2407.03168).
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pip install -r requirements.txt
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```
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### 2. Download pretrained weights
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```text
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pretrained_weights
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βββ insightface
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### 3. Inference π
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```bash
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python inference.py
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```
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```bash
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4
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#
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 --no_flag_pasteback
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# more options to see
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python inference.py -h
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```
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```bash
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python app.py
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```
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### 5. Inference speed evaluation πππ
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We have also provided a script to evaluate the inference speed of each module:
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| Motion Extractor | 28.12 | 108 | 0.84 |
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| Spade Generator | 55.37 | 212 | 7.59 |
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| Warping Module | 45.53 | 174 | 5.21 |
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| Stitching and Retargeting Modules| 0.23 | 2.3 | 0.31 |
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## Acknowledgements
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We would like to thank the contributors of [FOMM](https://github.com/AliaksandrSiarohin/first-order-model), [Open Facevid2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis), [SPADE](https://github.com/NVlabs/SPADE), [InsightFace](https://github.com/deepinsight/insightface) repositories, for their open research and contributions.
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## Citation π
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If you find LivePortrait useful for your research, welcome to π this repo and cite our work using the following BibTeX:
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```bibtex
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@article{
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title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control},
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author = {Jianzhu
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}
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```
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<a href='https://github.com/cleardusk' target='_blank'><strong>Jianzhu Guo</strong></a><sup> 1β </sup> 
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<a href='https://github.com/KwaiVGI' target='_blank'><strong>Dingyun Zhang</strong></a><sup> 1,2</sup> 
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<a href='https://github.com/KwaiVGI' target='_blank'><strong>Xiaoqiang Liu</strong></a><sup> 1</sup> 
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<a href='https://scholar.google.com/citations?user=t88nyvsAAAAJ&hl' target='_blank'><strong>Zhizhou Zhong</strong></a><sup> 1,3</sup> 
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<a href='https://scholar.google.com.hk/citations?user=_8k1ubAAAAAJ' target='_blank'><strong>Yuan Zhang</strong></a><sup> 1</sup> 
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</div>
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</div>
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<br>
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<div align="center">
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<!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
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<a href='https://arxiv.org/pdf/2407.03168'><img src='https://img.shields.io/badge/arXiv-LivePortrait-red'></a>
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<a href='https://liveportrait.github.io'><img src='https://img.shields.io/badge/Project-LivePortrait-green'></a>
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## π₯ Updates
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- **`2024/07/10`**: πͺ We support audio and video concatenating, driving video auto-cropping, and template making to protect privacy. More to see [here](docs/changelog/2024-07-10.md).
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- **`2024/07/09`**: π€ We released the [HuggingFace Space](https://huggingface.co/spaces/KwaiVGI/liveportrait), thanks to the HF team and [Gradio](https://github.com/gradio-app/gradio)!
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- **`2024/07/04`**: π We released the initial version of the inference code and models. Continuous updates, stay tuned!
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- **`2024/07/04`**: π₯ We released the [homepage](https://liveportrait.github.io) and technical report on [arXiv](https://arxiv.org/pdf/2407.03168).
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## Introduction
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This repo, named **LivePortrait**, contains the official PyTorch implementation of our paper [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://arxiv.org/pdf/2407.03168).
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pip install -r requirements.txt
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```
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**Note:** make sure your system has [FFmpeg](https://ffmpeg.org/) installed!
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### 2. Download pretrained weights
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The easiest way to download the pretrained weights is from HuggingFace:
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```bash
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# you may need to run `git lfs install` first
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git clone https://huggingface.co/KwaiVGI/liveportrait pretrained_weights
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```
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Alternatively, you can download all pretrained weights from [Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib) or [Baidu Yun](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn). Unzip and place them in `./pretrained_weights`.
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Ensuring the directory structure is as follows, or contains:
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```text
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pretrained_weights
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βββ insightface
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### 3. Inference π
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#### Fast hands-on
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```bash
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python inference.py
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```
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```bash
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4
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# disable pasting back to run faster
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 --no_flag_pasteback
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# more options to see
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python inference.py -h
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```
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#### Driving video auto-cropping
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π To use your own driving video, we **recommend**:
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- Crop it to a **1:1** aspect ratio (e.g., 512x512 or 256x256 pixels), or enable auto-cropping by `--flag_crop_driving_video`.
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- Focus on the head area, similar to the example videos.
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- Minimize shoulder movement.
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- Make sure the first frame of driving video is a frontal face with **neutral expression**.
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Below is a auto-cropping case by `--flag_crop_driving_video`:
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```bash
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d13.mp4 --flag_crop_driving_video
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```
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If you find the results of auto-cropping is not well, you can modify the `--scale_crop_video`, `--vy_ratio_crop_video` options to adjust the scale and offset, or do it manually.
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#### Motion template making
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You can also use the auto-generated motion template files ending with `.pkl` to speed up inference, and **protect privacy**, such as:
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```bash
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d5.pkl
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```
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**Discover more interesting results on our [Homepage](https://liveportrait.github.io)** π
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### 4. Gradio interface π€
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We also provide a Gradio <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a> interface for a better experience, just run by:
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```bash
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python app.py
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```
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You can specify the `--server_port`, `--share`, `--server_name` arguments to satisfy your needs!
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π We also provide an acceleration option `--flag_do_torch_compile`. The first-time inference triggers an optimization process (about one minute), making subsequent inferences 20-30% faster. Performance gains may vary with different CUDA versions.
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```bash
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# enable torch.compile for faster inference
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python app.py --flag_do_torch_compile
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```
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**Note**: This method has not been fully tested. e.g., on Windows.
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**Or, try it out effortlessly on [HuggingFace](https://huggingface.co/spaces/KwaiVGI/LivePortrait) π€**
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### 5. Inference speed evaluation πππ
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We have also provided a script to evaluate the inference speed of each module:
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| Motion Extractor | 28.12 | 108 | 0.84 |
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| Spade Generator | 55.37 | 212 | 7.59 |
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| Warping Module | 45.53 | 174 | 5.21 |
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| Stitching and Retargeting Modules | 0.23 | 2.3 | 0.31 |
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*Note: The values for the Stitching and Retargeting Modules represent the combined parameter counts and total inference time of three sequential MLP networks.*
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## Community Resources π€
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Discover the invaluable resources contributed by our community to enhance your LivePortrait experience:
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- [ComfyUI-LivePortraitKJ](https://github.com/kijai/ComfyUI-LivePortraitKJ) by [@kijai](https://github.com/kijai)
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- [comfyui-liveportrait](https://github.com/shadowcz007/comfyui-liveportrait) by [@shadowcz007](https://github.com/shadowcz007)
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- [LivePortrait hands-on tutorial](https://www.youtube.com/watch?v=uyjSTAOY7yI) by [@AI Search](https://www.youtube.com/@theAIsearch)
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- [ComfyUI tutorial](https://www.youtube.com/watch?v=8-IcDDmiUMM) by [@Sebastian Kamph](https://www.youtube.com/@sebastiankamph)
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- [LivePortrait In ComfyUI](https://www.youtube.com/watch?v=aFcS31OWMjE) by [@Benji](https://www.youtube.com/@TheFutureThinker)
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- [Replicate Playground](https://replicate.com/fofr/live-portrait) and [cog-comfyui](https://github.com/fofr/cog-comfyui) by [@fofr](https://github.com/fofr)
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And many more amazing contributions from our community!
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## Acknowledgements
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We would like to thank the contributors of [FOMM](https://github.com/AliaksandrSiarohin/first-order-model), [Open Facevid2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis), [SPADE](https://github.com/NVlabs/SPADE), [InsightFace](https://github.com/deepinsight/insightface) repositories, for their open research and contributions.
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## Citation π
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If you find LivePortrait useful for your research, welcome to π this repo and cite our work using the following BibTeX:
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```bibtex
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@article{guo2024liveportrait,
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title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control},
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author = {Guo, Jianzhu and Zhang, Dingyun and Liu, Xiaoqiang and Zhong, Zhizhou and Zhang, Yuan and Wan, Pengfei and Zhang, Di},
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journal = {arXiv preprint arXiv:2407.03168},
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year = {2024}
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}
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```
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