拡散モデルをアートに開く：インタラクティブなモデル操作と実践に基づく説明可能性
Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability
大規模テキストから画像を生成する拡散モデルを、アーティストが素材として扱えるよう内部構造を可視化・操作可能にする手法を提案し、ComfyUI上で層選択や介入操作を実装して効果を検証した。
著者: Ahmed M. Abuzuraiq, Philippe Pasquier
分類: cs.HC, cs.AI, cs.LG, cs.MM
原文アブストラクト
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