REINVENTING DIGITAL ILLUSTRATION WITH GENERATIVE AI TOOLS

Abstract

The advancement of digital illustration has led to a revolution stage whereby it entails the application of generative artificial intelligence which integrates the human creativity and the computational creativity. In this paper, the shift towards generative ecosystems via models such as GANs, VAEs, and diffusion networks will be considered in relation to the transformation of the conventional workflows of vectors and raster. It suggests an ambivalent framework based on which the illustration is regarded as a multidimensional contact between human mental will and machine learning inference. In order to estimate the similarity of artwork produced with the help of AI and human-produced artworks in terms of the aesthetic and semantic quality, the paper proposes a Creative Performance Index (CPI) as a critical combination of Fréchet Inception Distance (FID) and CLIP Score and the human-based measurements of originality and emotional resonance. Through a number of case studies of applications like DALLE, Stable Diffusion and Midjourney, it has been demonstrated in the paper that coaching of human feedback based on an iteration approach has a profound impact on artistic containment and richness of ideas. The findings validate that generative AI does not replace the agency of the illustrator but expands it to make the creative process adaptive and symbiotic system of leading to ideas, contemplating on them, and perfecting them.