Update README.md

README.md

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-<h1 align="center">This is the modification of LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control for allowing video as a source </h1>
-
-
-<br>
-<div align="center">
-  <!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
-  <a href='https://arxiv.org/pdf/2407.03168'><img src='https://img.shields.io/badge/arXiv-LivePortrait-red'></a>
-  <a href='https://liveportrait.github.io'><img src='https://img.shields.io/badge/Project-LivePortrait-green'></a>
-  <a href ='https://github.com/KwaiVGI/LivePortrait'>Official Liveportrait</a>
-</div>
-<br>
-
-<p align="center">
-  <img src="./assets/docs/showcase2.gif" alt="showcase">
-  <br>
-  🔥 For more results, visit LivePortrait <a href="https://liveportrait.github.io/"><strong>homepage</strong></a> 🔥
-</p>
-
-
-## 🔥 Getting Started
-### 1. Clone the code and prepare the environment
-```bash
-git clone https://github.com/KwaiVGI/LivePortrait
-cd LivePortrait
-
-# create env using conda
-conda create -n LivePortrait python==3.9.18
-conda activate LivePortrait
-# install dependencies with pip
-pip install -r requirements.txt
-```
-
-### 2. Download pretrained weights
-
-Download the pretrained weights from HuggingFace:
-```bash
-# you may need to run `git lfs install` first
-git clone https://huggingface.co/KwaiVGI/liveportrait pretrained_weights
-```
-
-Or, 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). We have packed all weights in one directory 😊. Unzip and place them in `./pretrained_weights` ensuring the directory structure is as follows:
-```text
-pretrained_weights
-├── insightface
-│   └── models
-│       └── buffalo_l
-│           ├── 2d106det.onnx
-│           └── det_10g.onnx
-└── liveportrait
-    ├── base_models
-    │   ├── appearance_feature_extractor.pth
-    │   ├── motion_extractor.pth
-    │   ├── spade_generator.pth
-    │   └── warping_module.pth
-    ├── landmark.onnx
-    └── retargeting_models
-        └── stitching_retargeting_module.pth
-```
-
-### 3. Inference 🚀
-
-#### Fast hands-on
-```bash
-python inference.py
-```
-
-If the script runs successfully, you will get an output mp4 file named `animations/s6--d0_concat.mp4`. This file includes the following results: driving video, input image, and generated result.
-
-<p align="center">
-  <img src="./assets/docs/inference.gif" alt="image">
-</p>
-
-Or, you can change the input by specifying the `-s` and `-d` arguments:
-
-```bash
-python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4
-
-# disable pasting back to run faster
-python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 --no_flag_pasteback
-
-# more options to see
-python inference.py -h
-```
-
-For video: you can change the input by specifying the `-sd` and `-d` arguments:
-
-```bash
-python inference.py -sd assets/examples/driving/d3.mp4 -d assets/examples/driving/d0.mp4 -vd True
-
-# disable pasting back to run faster
-python inference.py -sd assets/examples/driving/d3.mp4 -d assets/examples/driving/d0.mp4 -vd True --no_flag_pasteback
-
-```
-#### Driving video auto-cropping
-
-📕 To use your own driving video, we **recommend**:
- - Crop it to a **1:1** aspect ratio (e.g., 512x512 or 256x256 pixels), or enable auto-cropping by `--flag_crop_driving_video`.
- - Focus on the head area, similar to the example videos.
- - Minimize shoulder movement.
- - Make sure the first frame of driving video is a frontal face with **neutral expression**.
-
-Below is a auto-cropping case by `--flag_crop_driving_video`:
-```bash
-python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d13.mp4 --flag_crop_driving_video
-```
-
-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.
-
-#### Template making
-You can also use the `.pkl` file auto-generated to speed up the inference, and **protect privacy**, such as:
-```bash
-python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d5.pkl
-```
-
-**Discover more interesting results on our [Homepage](https://liveportrait.github.io)** 😊
-
-### 4. Gradio interface 🤗
-
-We also provide a Gradio interface for a better experience, just run by:
-
-```bash
-python app.py
-```
-
-You can specify the `--server_port`, `--share`, `--server_name` arguments to satisfy your needs!
-
-
-
-
-## Acknowledgements
-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), [LivePortrait](https://github.com/KwaiVGI/LivePortrait) repositories, for their open research and contributions.
-
-## Citation 💖
-
-```bibtex
-@article{guo2024liveportrait,
-  title   = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control},
-  author  = {Guo, Jianzhu and Zhang, Dingyun and Liu, Xiaoqiang and Zhong, Zhizhou and Zhang, Yuan and Wan, Pengfei and Zhang, Di},
-  journal = {arXiv preprint arXiv:2407.03168},
-  year    = {2024}
-}
-```
+---
+title: Liveportrait_video
+emoji: 😻
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.38.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+<h1 align="center">This is the modification of LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control for allowing video as a source </h1>
+
+
+<br>
+<div align="center">
+  <!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
+  <a href='https://arxiv.org/pdf/2407.03168'><img src='https://img.shields.io/badge/arXiv-LivePortrait-red'></a>
+  <a href='https://liveportrait.github.io'><img src='https://img.shields.io/badge/Project-LivePortrait-green'></a>
+  <a href ='https://github.com/KwaiVGI/LivePortrait'>Official Liveportrait</a>
+</div>
+<br>
+
+<p align="center">
+  <img src="./assets/docs/showcase2.gif" alt="showcase">
+  <br>
+  🔥 For more results, visit LivePortrait <a href="https://liveportrait.github.io/"><strong>homepage</strong></a> 🔥
+</p>
+
+
+## 🔥 Getting Started
+### 1. Clone the code and prepare the environment
+```bash
+git clone https://github.com/KwaiVGI/LivePortrait
+cd LivePortrait
+
+# create env using conda
+conda create -n LivePortrait python==3.9.18
+conda activate LivePortrait
+# install dependencies with pip
+pip install -r requirements.txt
+```
+
+### 2. Download pretrained weights
+
+Download the pretrained weights from HuggingFace:
+```bash
+# you may need to run `git lfs install` first
+git clone https://huggingface.co/KwaiVGI/liveportrait pretrained_weights
+```
+
+Or, 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). We have packed all weights in one directory 😊. Unzip and place them in `./pretrained_weights` ensuring the directory structure is as follows:
+```text
+pretrained_weights
+├── insightface
+│   └── models
+│       └── buffalo_l
+│           ├── 2d106det.onnx
+│           └── det_10g.onnx
+└── liveportrait
+    ├── base_models
+    │   ├── appearance_feature_extractor.pth
+    │   ├── motion_extractor.pth
+    │   ├── spade_generator.pth
+    │   └── warping_module.pth
+    ├── landmark.onnx
+    └── retargeting_models
+        └── stitching_retargeting_module.pth
+```
+
+### 3. Inference 🚀
+
+#### Fast hands-on
+```bash
+python inference.py
+```
+
+If the script runs successfully, you will get an output mp4 file named `animations/s6--d0_concat.mp4`. This file includes the following results: driving video, input image, and generated result.
+
+<p align="center">
+  <img src="./assets/docs/inference.gif" alt="image">
+</p>
+
+Or, you can change the input by specifying the `-s` and `-d` arguments:
+
+```bash
+python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4
+
+# disable pasting back to run faster
+python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 --no_flag_pasteback
+
+# more options to see
+python inference.py -h
+```
+
+For video: you can change the input by specifying the `-sd` and `-d` arguments:
+
+```bash
+python inference.py -sd assets/examples/driving/d3.mp4 -d assets/examples/driving/d0.mp4 -vd True
+
+# disable pasting back to run faster
+python inference.py -sd assets/examples/driving/d3.mp4 -d assets/examples/driving/d0.mp4 -vd True --no_flag_pasteback
+
+```
+#### Driving video auto-cropping
+
+📕 To use your own driving video, we **recommend**:
+ - Crop it to a **1:1** aspect ratio (e.g., 512x512 or 256x256 pixels), or enable auto-cropping by `--flag_crop_driving_video`.
+ - Focus on the head area, similar to the example videos.
+ - Minimize shoulder movement.
+ - Make sure the first frame of driving video is a frontal face with **neutral expression**.
+
+Below is a auto-cropping case by `--flag_crop_driving_video`:
+```bash
+python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d13.mp4 --flag_crop_driving_video
+```
+
+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.
+
+#### Template making
+You can also use the `.pkl` file auto-generated to speed up the inference, and **protect privacy**, such as:
+```bash
+python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d5.pkl
+```
+
+**Discover more interesting results on our [Homepage](https://liveportrait.github.io)** 😊
+
+### 4. Gradio interface 🤗
+
+We also provide a Gradio interface for a better experience, just run by:
+
+```bash
+python app.py
+```
+
+You can specify the `--server_port`, `--share`, `--server_name` arguments to satisfy your needs!
+
+
+
+
+## Acknowledgements
+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), [LivePortrait](https://github.com/KwaiVGI/LivePortrait) repositories, for their open research and contributions.
+
+## Citation 💖
+
+```bibtex
+@article{guo2024liveportrait,
+  title   = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control},
+  author  = {Guo, Jianzhu and Zhang, Dingyun and Liu, Xiaoqiang and Zhong, Zhizhou and Zhang, Yuan and Wan, Pengfei and Zhang, Di},
+  journal = {arXiv preprint arXiv:2407.03168},
+  year    = {2024}
+}
+```