让大模型可操控，艺术家能直观理解并改造生成过程。
Explainability-in-Action: Enabling Expressive Manipulation and Tacit Understanding by Bending Diffusion Models in ComfyUI
- 通过节点式界面在ComfyUI中直接操作扩散模型内部结构
- 艺术家能实时看到组件调整对输出的影响，形成直觉理解
- 适合希望深度参与生成艺术创作的创作者和研究者
可解释人工智能（XAI）在创意场景中可超越透明性，支持艺术参与、可修改性和持续实践。尽管经过精心设计的数据集和训练的人类规模模型能赋予艺术家更多自主权，但像文本到图像扩散模型这类大规模生成模型往往掩盖了这些可能性。我们提出，只要揭示并可操作其内部结构，大型模型也能成为创作素材。本文基于长期、动手实践的工艺方法，借鉴施温的“行动中反思”理念，通过集成至ComfyUI节点式界面的模型弯曲与检查插件，展示了该方法的应用。实验证明，通过交互式地调整生成模型的不同部分，艺术家能够发展出对各组件如何影响输出结果的直觉认知。
原文摘要 · Abstract (English)
Explainable AI (XAI) in creative contexts can go beyond transparency to support artistic engagement, modifiability, and sustained practice. While curated datasets and training human-scale models can offer artists greater agency and control, large-scale generative models like text-to-image diffusion systems often obscure these possibilities. We suggest that even large models can be treated as creative materials if their internal structure is exposed and manipulable. We propose a craft-based approach to explainability rooted in long-term, hands-on engagement akin to Schön's "reflection-in-action" and demonstrate its application through a model-bending and inspection plugin integrated into the node-based interface of ComfyUI. We demonstrate that by interactively manipulating different parts of a generative model, artists can develop an intuition about how each component influences the output.
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