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

TL;DR

BLK-Assist is a modular framework enabling artist-specific fine-tuning of diffusion models for co-creation, preserving style and privacy, demonstrated through a case study with a professional artist.

Contribution

Introduces a novel, modular, privacy-preserving framework for fine-tuning diffusion models tailored to individual artists' styles.

Findings

Successfully adapted diffusion models to a professional artist’s corpus.

Provided detailed workflows and configurations for reproducibility.

Achieved stylistic fidelity and privacy preservation in AI-assisted art creation.

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.