Leverages Generative Adversarial Networks (GANs) and Neural Style Transfer to create unique fabric that blend traditional
aesthetics with modern AI technology.
Neural Style Transfer Algorithm:
Merges content images (base fabric design) with style images (textures, color schemes, patterns).
The content image provides structure and layout, while the style image contributes aesthetic features.
The algorithm minimizes the difference between the original images and the generated design, preserving the fabric’s base structure and integrating artistic styles.
User Interface (UI):
Allows image uploads for both content and style images.
Provides sliders to adjust the ratio between “content contribution” (retaining original design) and “style contribution” (influencing the design with style elements).
Designers can preview real-time iterations, experimenting with different settings before finalizing a design.
Creative Flexibility:
Utilizes StyleGAN for high-quality image synthesis.
Uses Cycle GAN for unpaired style transfer, enabling transformations without the need for paired data.
Workflow:
Fabric image collation, pre-processing, image classification, model training, validation, and a design feedback loop.
Enables designers to create high-quality synthetic saree samples that blend tradition with cutting-edge innovation.