EVALUATING THE CREATIVE POTENTIAL OF STABLE DIFFUSION MODELS IN CONCEPT ART PIPELINES

Suhas Bhise; Twinkal Israni; Shailesh Kumar; Himanshu Makhija; Seethaladevi S; Sunitha Devi M

doi:10.29121/shodhkosh.v7.i4s.2026.7486

Authors

Suhas Bhise Assistant Professor, Department of E&TC Engineering, Vishwakarma Institute of Technology, Pune – 411037, Maharashtra, India
Twinkal Israni Assistant Professor, Department of Design, Vivekananda Global University, Jaipur, India
Dr. Shailesh Kumar Associate Professor, Department of Electrical & Electronics, Arka Jain University, Jamshedpur, Jharkhand, India
Himanshu Makhija Centre of Research Impact and Outcome, Chitkara University, Rajpura – 140417, Punjab, India
Seethaladevi S Assistant Professor, Department of Mathematics, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai – 600080, Tamil Nadu, India
Sunitha Devi M Assistant Professor, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai – 600080, Tamil Nadu, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7486

Keywords:

Stable Diffusion, Concept Art Generation, Generative Ai, Prompt Engineering, Digital Creativity

Abstract [English]

Stable Diffusion models have become a groundbreaking technology in the field of generative artificial intelligence that has impacted concept art pipelines in the world of creative industries. This paper will assess the artistic capabilities of Stable Diffusion through the lens of its capability to produce high-quality, heterogeneous, and contextual visual images based on textual prompting. This study examines the main aspects such as dataset selection, prompt engineering strategies, model configuration, and fine-tuning techniques to gain more control over artworks and the faithfulness of output. A hierarchical pipeline that incorporates text conditioning, latent diffusion and post-generative refinement is suggested to streamline the process of concept art generation. The comparative analysis to conventional generative techniques, especially Generative Adversarial Network (GANs) shows that image coherence is improved, it is also stylistically diverse, and it is computationally efficient. The results of the experiment prove that Stable Diffusion outperforms in terms of visual realism, flexibility, and creative adaptability and can be a useful tool in the hands of artists, designers, and game developers. In addition, the paper covers applied implications, such as minimized production time and expanded ideation functionality, and limitation, such as timely sensitivity and ethical issues regarding data utilization. The results would indicate that Stable Diffusion models have a potential of transforming the workflows of digital concept art and enhancing the collaboration of human and AI creativity.

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