FaceFusion-Videos-from-Latent-Interpolations

Grading PDF

Introduction 🚀

This project focuses on creating animations using WGANs by interpolating in the latent space. These animations take the form of smooth transitions between various generated images, resulting in GIFs that showcase the capabilities of the trained model.

---

Table of Contents 📖

1. Introduction
2. Getting Started Locally
3. Prerequisites
4. Setup
5. Installation
6. Running the Application

---

Getting Started Locally 💻

Follow these steps to get the project running on your local machine.

Prerequisites

• Git: Version control system
• Anaconda: Open-source distribution for Python/R
  • Download Anaconda here.

Setup 🛠️

1. Create a Dummy Folder on your desktop.
2. Open Git Bash by right-clicking on the folder and selecting "Git Bash here".
3. Clone the Repository:
   

git clone https://github.com/Lizoug/FaceFusion-Videos-from-Latent-Interpolations.git`

4. Close Git Bash and open Anaconda Prompt.

Installation 🔧

1. Open Anaconda Prompt.
2. Create a New Conda Environment:
   

conda create --name myenv python=3.8

3. Activate the New Environment:
   

conda activate myenv

4. Navigate to the Project Directory:
   

cd path_to_dummy_folder/FaceFusion-Videos-from-Latent-Interpolations

5. Install Requirements:
   

pip install -r requirements.txt

6. Download Pre-Trained Models:
   

cd Backend
python download_model.py

Running the Application 🏃🏽‍♀️

1. Navigate to the Frontend Directory
   

cd ..
cd Frontend

2. Run Streamlit Application
   

streamlit run app.py