Update README.md

README.md

@@ -14,15 +14,14 @@ app_file: app.py
 pinned: false
 ---
 
-<!-- ## Get Started
+## Get Started
+1. Install ROSE Dependencies
 
-1. Install ProPainter Dependencies
-
-   You can follow the [Dependencies and Installation](https://github.com/Luo-Yihang/ProPainter-pr/tree/dev_yihang#dependencies-and-installation).
+   You can follow the [Dependencies and Installation](https://github.com/Kunbyte-AI/ROSE?tab=readme-ov-file#dependencies-and-installation).
 
 3. Install Demo Dependencies
 ```shell
-cd web-demos/hugging_face
+cd hugging_face
 
 # install python dependencies 
 pip3 install -r requirements.txt
@@ -33,7 +32,7 @@ python app.py
 
 ## Usage Guidance
 * Step 1: Upload your video and click the `Get video info` button.
-   ![Step 1](./assets/step1.png)
+
 
 * Step 2: 
    1. *[Optional]* Specify the tracking period for the currently added mask by dragging the `Track start frame` or `Track end frame`.
@@ -42,13 +41,12 @@ python app.py
       - *[Optional]* Click `Clear clicks` if you want to reselect the mask area, or
       - *[Optional]* Click `Remove mask` to remove all masks.
    4. *[Optional]* Go back to step 2.1 to add another mask.
-   ![Step 2](./assets/step2.png)
+
    
 * Step 3: 
    1. Click the `Tracking` button to track the masks for the whole video.
-   2. *[Optional]* Select the ProPainter parameters if the `ProPainter Parameters` dropdown.
    2. Then click `Inpainting` to get the inpainting results.
-   ![Step 3](./assets/step3.png)
+
 
 *You can always refer to the `Highlighted Text` box on the page for guidance on the next step!*
 
@@ -61,11 +59,7 @@ If you find our repo useful for your research, please consider citing our paper:
 ```
 
 
-## License
-
-This project is licensed under <a rel="license" href="./LICENSE">NTU S-Lab License 1.0</a>. Redistribution and use should follow this license.
-
 
 ## Acknowledgements
 
-The project harnesses the capabilities from [Track Anything](https://github.com/gaomingqi/Track-Anything), [Segment Anything](https://github.com/facebookresearch/segment-anything) and [Cutie](https://github.com/hkchengrex/Cutie). Thanks for their awesome works. -->
+The project harnesses the capabilities from [Track Anything](https://github.com/gaomingqi/Track-Anything), [Segment Anything](https://github.com/facebookresearch/segment-anything) and [Cutie](https://github.com/hkchengrex/Cutie). Also the gradio demo page is based on [ProPainter's huggingface demo page](https://github.com/sczhou/ProPainter/tree/main/web-demos/hugging_face). Thanks for their awesome works.