Add app

README.md

@@ -1,22 +1,8 @@
-# Line Art Colorization
-This project is a minimalistic implementation of AlacGan and it is based on the paper called User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks (https://arxiv.org/pdf/1808.03240.pdf) as well as its github repository.
-
-## Colab example to play with the model (just run!)
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jInaIELLo-Y1M8MnIgA-7aXSWRYlHnC9?usp=sharing)
-
-## Differences from the original implementation
-1. Less variants of line thickness (as it did not make the model performance significantly worse)
-2. Different images in the dataset
-3. No local features network added
-4. All image pairs are acquired via the xdog algorithm whereas in the paper, real line art images were also used to train the model
-
-Because of these differences, the results are slightly worse but the model was trained significantly faster and the process of collecting data did not take too long.
-
-## Model weights
-https://download938.mediafire.com/nd1xp1xdgitg/aig8n36f4vrne6t/gen_373000.pth
-
-## Colorization examples
-All these images were colorized by the alacgan neural network
-
-![Results of colorization](https://i.imgur.com/qngw4BI.png)
-
+title: Line Art Colorization
+emoji: 🏃
+colorFrom: green
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.17.0
+app_file: app.py
+pinned: false