Edge-to-Image Translation Using Conditional GAN

Strategic Deployment

Our Conditional GAN model, now live, transforms shoe outline sketches into photorealistic images using a Flask-based web app. This application allows designers, customers, and retailers to quickly generate realistic shoe visuals from simple sketches, supporting applications like virtual try-ons, product customization, and rapid prototyping. It’s a game-changer for fashion and retail innovation!

️ Advanced Data Processing for Accuracy

By normalizing data within the range [-1,1], we optimized our model for consistent and high-quality image generation, ensuring accurate translation from edge maps to fully realized shoe images.

Valuable Business Insights

Our platform provides actionable insights for e-commerce platforms to enhance customer engagement through virtual try-ons and stylized social media content. Businesses can also leverage the tool for concept testing, gathering customer preferences early to optimize inventory and production strategies.

? Innovative Modeling with Stable Diffusion

We leveraged Stable Diffusion as a pretrained model to enhance the image quality further. Coupled with our Conditional GAN architecture—featuring a PatchGAN Discriminator and U-Net-based Generator—this model delivers high-resolution, lifelike image outputs, revolutionizing digital fashion and concept testing.