BLK-Assist: A Methodological Framework for Artist-Led Co-Creation with Generative AI Models
It addresses the problem of enabling artists to collaborate with AI while maintaining control over their style and data, though it is incremental as it builds on existing parameter-efficient methods.
The paper introduces BLK-Assist, a framework for fine-tuning diffusion models to assist artists in co-creation, using a case study with a single artist's proprietary data to achieve stylistic fidelity while preserving privacy and consent.
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.