Noise Pilot: Enabling Artistic Workflow Composition with Diffusion-Based Image Generation

要旨

Creativity support tools (CSTs) increasingly include image-generation features. The underlying diffusion models enact a particular image diffusing process that AI CSTs tend to obscure within a black-box. Artists’ creative control is limited to indirect manipulation (prompting), chaining these "black-boxes" together, or using ML-engineering skills to build custom black-boxes. Seeking to maintain the low-threshold offered by prompting, while raising the ceiling of expressive interactions, we built Noise Pilot: a multi-layered approach to supporting diffusion-based creative processes at three levels of depth. We used Noise Pilot as a probe to study the artistic processes of 9 artists over a 2-week period. Artists engaged with diffusion at different levels of manipulative depth and crafted reusable artifacts to enact bespoke diffusion processes; some produced results impossible to achieve with prompting alone. We discuss how black-box AIs in CSTs limit creative power, and propose subverting this by favoring visibility over obscurity, and materiality over personification.

受賞
Honorable Mention
著者
James Smith
UC Berkeley, Berkeley, California, United States
Shm Garanganao. Almeda
UC Berkeley, Berkeley, California, United States
Timothy J.. Aveni
University of California, Berkeley, Berkeley, California, United States
Anya Agarwal
University of California, Berkeley, Berkeley, California, United States
Bjoern Hartmann
UC Berkeley, Berkeley, California, United States
動画

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Designing Creative GenAI Tools

P1 - Room 115
7 件の発表
2026-04-15 18:00:00
2026-04-15 19:30:00