让艺术家动手调试扩散模型，直观理解各组件如何影响生成图像。
Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability
- 通过可交互界面操控扩散模型各层，实现可视化干预
- 在Stable Diffusion 1.5上验证，调整特定组件产生稳定视觉效果族
- 适合艺术创作中想深入理解模型机制的实践者
创意实践中，可解释AI不应局限于技术性说明，而应支持艺术家对模型进行检查、修改与调试。然而，当前大规模文生图扩散模型通常作为黑箱端到端工具，限制了这种物质性参与。本文主张，即使大模型也可作为创作材料，只要其内部结构可见且可操作。为此，我们提出一种以实验与干预为核心的可解释性方法，集成于ComfyUI节点式工作流中，包含交互式层选择与干预控制。通过对Stable Diffusion 1.5的定性和定量分析，发现操纵扩散管道中的特定组件能产生相对一致的视觉效果族，使艺术家建立对模型各部分如何塑造图像的实用层级直觉。
原文摘要 · Abstract (English)
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
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