Create README.md

added inference code, works well on colab

README.md

@@ -1,107 +1,21 @@
-# [CVPR2022] Thin-Plate Spline Motion Model for Image Animation
+# Model repo
 
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
-![stars](https://img.shields.io/github/stars/yoyo-nb/Thin-Plate-Spline-Motion-Model.svg?style=flat)
-![GitHub repo size](https://img.shields.io/github/repo-size/yoyo-nb/Thin-Plate-Spline-Motion-Model.svg)
-
-Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"
-
-[**Paper**](https://arxiv.org/abs/2203.14367) **|** [**Supp**](https://cloud.tsinghua.edu.cn/f/f7b8573bb5b04583949f/?dl=1)
-
-### Example animation
-
-![vox](assets/vox.gif)
-![ted](assets/ted.gif)
-
-**PS**: The paper trains the model for 100 epochs for a fair comparison. You can use more data and train for more epochs to get better performance.
-
-
-### Web demo for animation
-- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model)
-- Try the web demo for animation here: [![Replicate](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model/badge)](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model)
-- Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DREfdpnaBhqISg0fuQlAAIwyGVn1loH_?usp=sharing)
-
-### Pre-trained models
-- ~~[Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/30ab8765da364fefa101/)~~
-- [Yandex](https://disk.yandex.com/d/bWopgbGj1ZUV1w)
-- [Google Drive](https://drive.google.com/drive/folders/1pNDo1ODQIb5HVObRtCmubqJikmR7VVLT?usp=sharing)
-- [Baidu Yun](https://pan.baidu.com/s/1hnXmDpIbRC6WqE3tF9c5QA?pwd=1234)
-
-### Installation
-
-We support ```python3```.(Recommended version is Python 3.9).
-To install the dependencies run:
-```bash
-pip install -r requirements.txt
-```
-
-
-### YAML configs
- 
-There are several configuration files one for each `dataset` in the `config` folder named as ```config/dataset_name.yaml```. 
-
-See description of the parameters in the ```config/taichi-256.yaml```.
-
-### Datasets
-
-1) **MGif**. Follow [Monkey-Net](https://github.com/AliaksandrSiarohin/monkey-net).
-
-2) **TaiChiHD** and **VoxCeleb**. Follow instructions from [video-preprocessing](https://github.com/AliaksandrSiarohin/video-preprocessing). 
-
-3) **TED-talks**. Follow instructions from [MRAA](https://github.com/snap-research/articulated-animation).
-
-Here are **VoxCeleb**, **TaiChiHD** and **TED-talks**  pre-processed datasets used in the paper. [Baidu Yun](https://pan.baidu.com/s/1HKJOtXBIiP_tlLiFbzn3oA?pwd=x7xv)
-Download all files under the folder, then merge the files and decompress, for example:
-```bash
-cat vox.tar.* > vox.tar
-tar xvf vox.tar
-```
-
-
-### Training
-To train a model on specific dataset run:
+ following is the original repo:
 ```
-CUDA_VISIBLE_DEVICES=0,1 python run.py --config config/dataset_name.yaml --device_ids 0,1
+https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model.git
 ```
-A log folder named after the timestamp will be created. Checkpoints, loss values, reconstruction results will be saved to this folder.
 
+inference was run using colab and then the following code was used to upload data here:
 
-#### Training AVD network
-To train a model on specific dataset run:
 ```
-CUDA_VISIBLE_DEVICES=0 python run.py --mode train_avd --checkpoint '{checkpoint_folder}/checkpoint.pth.tar' --config config/dataset_name.yaml
-```
-Checkpoints, loss values, reconstruction results will be saved to `{checkpoint_folder}`.
-
-
-
-### Evaluation on video reconstruction
+from huggingface_hub import HfApi
+api = HfApi()
 
-To evaluate the reconstruction performance run:
+api.upload_folder(
+    folder_path="/content/Thin-Plate-Spline-Motion-Model",
+    repo_id="SaffalPoosh/thin-spline",
+    repo_type="model",
+)
 ```
-CUDA_VISIBLE_DEVICES=0 python run.py --mode reconstruction --config config/dataset_name.yaml --checkpoint '{checkpoint_folder}/checkpoint.pth.tar'
-```
-The `reconstruction` subfolder will be created in `{checkpoint_folder}`.
-The generated video will be stored to this folder, also generated videos will be stored in ```png``` subfolder in loss-less '.png' format for evaluation.
-To compute metrics, follow instructions from [pose-evaluation](https://github.com/AliaksandrSiarohin/pose-evaluation).
-
-
-### Image animation demo
-- notebook: `demo.ipynb`, edit the config cell and run for image animation.
-- python:
-```bash
-CUDA_VISIBLE_DEVICES=0 python demo.py --config config/vox-256.yaml --checkpoint checkpoints/vox.pth.tar --source_image ./source.jpg --driving_video ./driving.mp4
-```
-
-# Acknowledgments
-The main code is based upon [FOMM](https://github.com/AliaksandrSiarohin/first-order-model) and [MRAA](https://github.com/snap-research/articulated-animation)
-
-Thanks for the excellent works!
-
-And Thanks to:
-
-- [@chenxwh](https://github.com/chenxwh): Add Web Demo & Docker environment [![Replicate](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model/badge)](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model) 
 
-- [@TalkUHulk](https://github.com/TalkUHulk): The C++/Python demo is provided in [Image-Animation-Turbo-Boost](https://github.com/TalkUHulk/Image-Animation-Turbo-Boost)
 
-- [@AK391](https://github.com/AK391): Add huggingface web demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model)