[2604.03249] BLK-Assist: A Methodological Framework for Artist-Led Co-Creation with Generative AI Models

Computer Science > Computers and Society arXiv:2604.03249 (cs) [Submitted on 10 Mar 2026] Title:BLK-Assist: A Methodological Framework for Artist-Led Co-Creation with Generative AI Models Authors:Daniel Grimes, Rachel M. Harrison View a PDF of the paper titled BLK-Assist: A Methodological Framework for Artist-Led Co-Creation with Generative AI Models, by Daniel Grimes and 1 other authors View PDF HTML (experimental) Abstract:This paper presents BLK-Assist, a modular framework for artist-specific fine-tuning of diffusion models using parameter-efficient methods. The system is implemented as a case study with a single professional artist's proprietary corpus and consists of three components: BLK-Conceptor (LoRA-adapted conceptual sketch generation), BLK-Stencil (LayerDiffuse-based transparency-preserving asset generation), and BLK-Upscale (hybrid Real-ESRGAN and texture-conditioned diffusion for high-resolution outputs). We document dataset composition, preprocessing, training configurations, and inference workflows to enable reproducibility with publicly available models to illustrate a privacy-preserving, consent-based approach to human-AI co-creation that maintains stylistic fidelity to the source corpus and can be adapted for other artists under similar constraints. Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC) Cite as: arXiv:2604.03249 [cs.CY]   (or arXiv:2604...