metadata

title: Neural Style Transfer
emoji: 🦀
colorFrom: red
colorTo: gray
sdk: streamlit
sdk_version: 1.15.2
app_file: app.py
pinned: false
license: mit

Neural image style transfer

Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.

This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image. These statistics are extracted from the images using a convolutional network.

Goal

In this project we are buidling streamlit demo for Fast arbitrary image style transfer using a pretrained Image Stylization model from TensorFlow Hub. To use it, simply upload a content image and style image.

Deployed app

The app is deployed on Huggingface Spaces: Click here for live demo

Project Structure

Neural Style Transfer Project

├── app.py
├── requirements.txt
└── examples

Project Requirements

Python3
git

Project Steps

Step 1: Cloning the repo

git clone https://github.com/DigitalProductschool/AI-Makerspace.git

Step 2: Changing working directory to TextAutocomplete-Streamlit

cd AI-Makerspace/HuggingFace/StyleTransfer

Step 3: Installing dependencies using pip3

pip3 install -r requirements.txt

Step 4: Running the streamlit web app

streamlit run app.py

Now go to http://localhost:8501/ to test out this streamlit web-app

References:

1. Exploring the structure of a real-time, arbitrary neural artistic stylization network

2. Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub

3. The idea to build a neural style transfer application was inspired from this Hugging Face Space