Advertisement using Customize LoRA

Abstract

This paper discusses the development of an avenue for personalized, pre-trained image generation models using Low-Rank Adaptation (LoRA) to confront some challenges of the advertising space, especially in clothing brands. Conventional advertising campaigns in the clothing industry often rely on costly, time-consuming photoshoots with professional models. The idea here is to utilize LoRA to finetune a base model and reduce the time spent distract you away from the costly issue by creating quality images of your brand. Our approach allows you to get high-quality images of your brand tailored to your advertising needs, without the need for physical models or large setups. By providing an effective Low-Rank Adapted (LoRA) image generation model using Replicate, the Flux base model, and Hugging Face to train it, our approach allows users to generate reliable and appealing images in the seconds it would take to shoot a physical ad campaign. The research offers an accessible and affordable alternative to traditional photoshoots, saving businesses money and time, while maintaining reliability, appeal and consistency with the brand. The results show that Low-Rank Adapted (LoRA) based customizations have the potential to be a disruptive resource for the production of aesthetic visual content for fashion marketing activations.