将扩散模型视为神经经济系统，揭示其如何抽象社会交流为可交易符号。
The Market in the Model: Latent Diffusion as Neural Economy
- 把扩散模型拆解为组件，分析其在计算机视觉中的任务与自动化决策
- 指出模型将社会沟通转化为可度量的向量，服务于平台注意力经济
- 主张批判应聚焦社会交换而非版权，避免强化模型的物化逻辑
生成式图像模型在视觉文化与人文学科中的批判多聚焦于数据集的影响，却忽视了模型机制中嵌入的意识形态。本文通过分析潜空间扩散模型的各组件如何响应计算机视觉工程需求而被设计，结合部件历史与视觉理论，揭示该模型运作如神经经济：一个封闭的符号系统，将社会交流抽象为可比向量，并将其转化为待售商品。追踪训练与生成流程，发现每个操作均转移社会意义，进一步巩固平台与注意力经济的逻辑。论文警示，仅关注版权与商品保护的批判可能重演模型所生产的拜物教，主张应以社会交换为核心重构批判视角。
原文摘要 · Abstract (English)
Valuable critique of generative image models within visual culture and the humanities has emphasized the role of datasets in shaping the images they produce. Yet, close studies of the ideological positions embedded into the mechanism of the models have been neglected, leaving them imagined as "black boxes." In a bid to expand, rather than replace, dataset critique, this paper examines the mechanisms of the latent diffusion model in terms of the problems they were brought in to solve on behalf of computer vision engineers, and the decisions each component was tasked with automating. I interpret that ensemble through the histories of its parts and the theory of vision the system inscribes into every generated image. Drawing on Impett and Offert's notion of neural exchange value, I offer this analysis to argue that the model operates as a neural economy: a contained symbolic system that abstracts social communication into commensurable vectors as it transfers the social sphere into parcels for sale. Tracing the training and generation pipelines component by component reveals what each operation displaces, and how it further entrenches the logics of platform and attention economies over social communication. The paper warns that any critique fixated exclusively on copyright and commodity defenses risks reaffirming the very fetishism the model produces, and argues instead for centering social exchange.
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