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This research reframes explainability for generative AI by treating large diffusion models as malleable creative materials rather than opaque black boxes. By integrating interactive inspection and intervention controls directly into ComfyUI workflows, the study demonstrates how artists can debug model internals to build practical intuition about visual generation mechanics. This approach shifts XAI from theoretical explanation to hands-on experimentation within established node-based pipelines.

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

arXivJuly 28, 20262 min read

arXiv:2607.22428v1 Announce Type: cross Abstract: Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this kind of material engagement We argue that even large models can function as creative materials when their internal structure is made visible and manipulable To support this we propose a handson approach to explainability centred on experimentation and intervention We instantiate this approach with a model bending and an interactive (inspection) interface integrated into ComfyUIs nodebased workflow including interactive layer selection and intervention controls Through qualitative and quantitative analysis of bending interventions in Stable Diffusion 15 we show how manipulating specific components of a diffusion pipeline produces relatively consistent families of visual effects allowing artists to build practical layerlevel intuition about how different parts of the model shape generated images

Under review for "Explainable AI for the Arts" (N. Bryan-Kinns, Ed.), Springer

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)

arXiv:2607.22428 [cs.HC]

(or arXiv:2607.22428v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2607.22428 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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