Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability

Large text-to-image diffusion models are usually opaque black boxes, limiting artists' ability to creatively manipulate them. This work reframes explainable AI for creative practice, proposing hands-on "model bending" through an interactive inspection interface built into ComfyUI, enabling layer selection and intervention. Applied to Stable Diffusion 1.5, the authors show that manipulating specific pipeline components produces consistent, learnable families of visual effects. Applications include generative art tools, creative AI education, artist-facing interpretability interfaces, and broader interaction design research into making large opaque models tangible, inspectable materials for hands-on artistic experimentation and intervention. Authors: Ahmed M. Abuzuraiq, Philippe Pasquier Paper: https://arxiv.org/abs/2607.22428v1