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Transferscope – Making Multi-Modal Conditioning for Image Diffusion Models Tangible

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dc.contributor.authorPietsch, Christopher
dc.contributor.authorStankowski, Aeneas
dc.date.accessioned2024-08-21T11:08:42Z
dc.date.available2024-08-21T11:08:42Z
dc.date.issued2024
dc.description.abstractThe significance of artificial intelligence (AI) is progressively amplifying for designers, especially within the domain of human-computer interaction. For design students, a foundational comprehension of machine learning (ML) algorithms is indispensable to navigate and utilize this technology in both theoretical and applied contexts - in order to leverage it within design proposals, and also within the design process. Generative AI Tools have rapidly entered creative processes of designers and artists alike, and have been heavily adopted by lay people. They have been praised for democratizing high-quality image creation. However, there are still concerns about the limited artistic control and steerability they provide , especially for professional creatives. This raises questions about how well these tools can be integrated into carefully developed creative workflows, given the constraints on composition and detail. Additionally, text2image algorithms are highly competitive with more manual creation and visualisation techniques in terms of speed and fidelity, while lacking opportunities for deliberation and fine-grained control. As a physical artifact, Transferscope attempts to tangibly introduce professional designers and students to generative AI powered workflows that facilitate creative control, while maintaining the option to leverage serendipity-driven iteration uniquely made possible by the instant-availability provide by image generation models like stable diffusion.Transferscope serves an educational purposes within an experiential teaching approach, and has been designed to work within exhibition and classroom settings alike.en
dc.identifier.doi10.18420/muc2024-mci-demo-248
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44381
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2024 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.rightshttp://purl.org/eprint/accessRights/RestrictedAccess
dc.rights.urihttp://purl.org/eprint/accessRights/RestrictedAccess
dc.subjectGenerative AI
dc.subjectDiffusion Algorithms
dc.subjectImage Diffusion
dc.subjectTangible Interactions
dc.subjectSpeculative Design
dc.subjectCo-Creation
dc.subjectInteraction Design
dc.subjectCo-Ideation
dc.titleTransferscope – Making Multi-Modal Conditioning for Image Diffusion Models Tangibleen
dc.typeText/Conference Paper
gi.conference.date1.-4. September 2024
gi.conference.locationKarlsruhe
gi.conference.sessiontitleMCI: Demos: Interactive Systems or Demonstrators

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