Update README.md

README.md

@@ -1,171 +1,44 @@
-## [Blind Face Restoration via Deep Multi-scale Component Dictionaries](https://arxiv.org/pdf/2008.00418.pdf)
+title: Deep Multi Scale
+emoji: 🦀
+colorFrom: green
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+---
 
->##### __Note: This branch contains all the restoration results, including 512×512 face region and the final result by putting the enhanced face to the origial input. The former version that can only generate the face result is put in [master branch](https://github.com/csxmli2016/DFDNet/tree/master)__ 
+# Configuration
 
+`title`: _string_
+Display title for the Space
 
-<p>
-Overview of our proposed method. It mainly contains two parts: (a) the off-line generation of multi-scale component dictionaries from large amounts of high-quality images, which have diverse poses and expressions. K-means is adopted to generate K clusters for each component (i.e., left/right eyes, nose and mouth) on different feature scales. (b) The restoration process and dictionary feature transfer (DFT) block that are utilized to provide the reference details in a progressive manner. Here, DFT-i block takes the Scale-i component dictionaries for reference in the same feature level.
-</p>  
-    
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
 
-<img src="./Imgs/pipeline_a.png">
-<p align="center">(a) Offline generation of multi-scale component dictionaries.</p>
-<img src="./Imgs/pipeline_b.png">
-<p align="center">(b) Architecture of our DFDNet for dictionary feature transfer.</p>
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
 
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
 
-## Pre-train Models and dictionaries
-Downloading from the following url and put them into ./.
-- [BaiduNetDisk](https://pan.baidu.com/s/1K4fzjPiezVSMl5NjHoJCGQ) (s9ht)
-- [GoogleDrive](https://drive.google.com/drive/folders/1bayYIUMCSGmoFPyd4Uu2Uwn347RW-vl5?usp=sharing)
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
 
-The folder structure should be:
-    
-    .
-    ├── checkpoints                    
-    │   ├── facefh_dictionary                  
-    │   │   └── latest_net_G.pth   
-    ├── weights
-    │   └── vgg19.pth
-    ├── DictionaryCenter512
-    │   ├── right_eye_256_center.npy
-    │   ├── right_eye_128_center.npy
-    │   ├── right_eye_64_center.npy
-    │   ├── right_eye_32_center.npy
-    │   └── ...
-    └── ...
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
 
-## Prerequisites 
->([Video Installation Tutorial](https://www.youtube.com/watch?v=OTqGYMSKGF4). Thanks for [bycloudump](https://www.youtube.com/channel/UCfg9ux4m8P0YDITTPptrmLg)'s tremendous help.)
-- Pytorch (≥1.1 is recommended)
-- dlib
-- dominate
-- cv2
-- tqdm
-- [face-alignment](https://github.com/1adrianb/face-alignment)
-    ```bash
-    cd ./FaceLandmarkDetection
-    python setup.py install
-    cd ..
-    ```
-    
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
 
-## Testing
-```bash
-python test_FaceDict.py --test_path ./TestData/TestWhole --results_dir ./Results/TestWholeResults --upscale_factor 4 --gpu_ids 0 
-```
-#### __Four parameters can be changed for flexible usage:__
-```
---test_path # test image path
---results_dir # save the results path
---upscale_factor # the upsample factor for the final result
---gpu_ids # gpu id. if use cpu, set gpu_ids=-1
-```
+`models`: _List[string]_
+HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
+Will be parsed automatically from your code if not specified here.
 
->Note: our DFDNet can only generate 512&times;512 face result for any given face image.
-
-#### __Result path contains the following folder:__
-- Step0_Input: ```# Save the input image.```
-- Step1_AffineParam: ```# Save the crop and align parameters for copying the face result to the original input.```
-- Step1_CropImg: ```# Save the cropped face images and resize them to 512×512.```
-- Step2_Landmarks: ```# Save the facial landmarks for RoIAlign.```
-- Step3_RestoreCropFace: ```# Save the face restoration result (512×512).```
-- Step4_FinalResults: ```# Save the final restoration result by putting the enhanced face to the original input.```
-
-## Some plausible restoration results on real low-quality images
-
- <table  style="float:center" width=100%>
- <tr>
-  <th><B>Input</B></th><th><B>Crop and Align</B></th><th><B>Restore Face</B></th><th><B>Final Results (UpScaleWhole=4)</B></th>
- </tr>
-  <tr>
-  <td>
-  <img src='./Imgs/RealLR/n000056_0060_01.png'>
-  </td>
-  <td>
-  <img src='./Imgs/RealLR/n000056_0060_01.png'>
-  </td>
-  <td>
-   <img src='./Imgs/ShowResults/n000056_0060_01.png'>
-  </td>
-  <td>
-   <img src='./Imgs/ShowResults/n000056_0060_01.png'>
-  </td>
- </tr>
-  <tr>
-  <td>
-  <img src='./Imgs/RealLR/n000184_0094_01.png'>
-  </td>
-  <td>
-  <img src='./Imgs/RealLR/n000184_0094_01.png'>
-  </td>
-  <td>
-   <img src='./Imgs/ShowResults/n000184_0094_01.png'>
-  </td>
-  <td>
-   <img src='./Imgs/ShowResults/n000184_0094_01.png'>
-  </td>
- </tr>
- <tr>
-  <td>
-  <img src='./Imgs/Whole/test1_0.jpg'>
-  </td>
-  <td>
-   <img src='./Imgs/Whole/test1_1.jpg'>
-  </td>
-    <td>
-   <img src='./Imgs/Whole/test1_2.jpg'>
-  </td>
-    <td>
-   <img src='./Imgs/Whole/test1_3.jpg'>
-  </td>
- </tr>
- <tr>
-  <td>
-  <img src='./Imgs/Whole/test2_0.jpg'>
-  </td>
-  <td>
-   <img src='./Imgs/Whole/test2_1.jpg'>
-  </td>
-    <td>
-   <img src='./Imgs/Whole/test2_2.jpg'>
-  </td>
-    <td>
-   <img src='./Imgs/Whole/test2_3.jpg'>
-  </td>
- </tr>
- <tr>
-  <td>
-  <img src='./Imgs/Whole/test5_0.jpg'>
-  </td>
-  <td>
-   <img src='./Imgs/Whole/test5_1.jpg'>
-  </td>
-    <td>
-   <img src='./Imgs/Whole/test5_2.jpg'>
-  </td>
-    <td>
-   <img src='./Imgs/Whole/test5_3.jpg'>
-  </td>
- </tr>
-  
- </table>
-
-## TO DO LIST (if possible)
-- [ ] Enhance all the faces in one image.
-- [ ] Enhance the background.
-
-
-## Citation
-
-```
-@InProceedings{Li_2020_ECCV,
-author = {Li, Xiaoming and Chen, Chaofeng and Zhou, Shangchen and Lin, Xianhui and Zuo, Wangmeng and Zhang, Lei},
-title = {Blind Face Restoration via Deep Multi-scale Component Dictionaries},
-booktitle = {ECCV},
-year = {2020}
-}
-```
-
-<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
+`datasets`: _List[string]_
+HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
+Will be parsed automatically from your code if not specified here.
 
+`pinned`: _boolean_
+Whether the Space stays on top of your list.