Stable Diffusion-Based Approach for Automated Fashion Editing

Abstract

Abstract AI has improved the efficiency of fashion editing by combining style and garment selection to generate visually appealing outfits. Clothes are digitally altered by AI algorithms, which simplify workflows and reduce resource consumption by changing colours, textures, and silhouettes. Achieving the intended results quickly is the goal of this study. To cut down on training time and computational expenses, efficient editing makes use of pretrained models and parallel processing strategies. In addition to fulfilling deadlines and preserving image quality, this enables fashion experts to concentrate on innovation. AI techniques based on textual descriptions and image-to-image editing are both applied. This study examined the latter, which concentrated on text-based garment altering. There are restrictions even if AI has transformed fashion editing. The goal of this research project is to improve fashion editing efficiency by improving image quality at a lower computing cost.