MIX: An Image Generation System Using Image Prompts for Industrial Design

Generative AI is increasingly being applied in the industrial design field. However, the existing AI design tools are still challenging due to complex operations and parameter settings. To solve these problems, we conducted comprehensive interviews with industry experts and proposed the MIX system. The system emphasizes image prompts and is designed to support designers for more efficient task completion. MIX incorporated both the IP-Adapter and the ControlNet control model with multimodal language models, making more natural interaction possible. It enables designers to quickly produce high-quality design renderings from target reference images. Through qualitative and quantitative user experiments, we found that the MIX system performs well in both efficiency and practicality in design development. Finally, we discuss about when it would be more appropriate to use text prompts versus image prompts in these different design scenarios. Available at https://mix.drafff.net.

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Publication Details

Journal
AHFE international
Published
2025-01-01
DOI
https://doi.org/10.54941/ahfe1006167
Primary Topic
Image Retrieval and Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

MIX: An Image Generation System Using Image Prompts for Industrial Design

Longfei Zhou, Lionel M. Ni, Yuxin Ding, Xinda Chen et al.
AHFE international
Image Retrieval and Classification Techniques
article

MIX: An Image Generation System Using Image Prompts for Industrial Design

Longfei Zhou, Lionel M. Ni, Yuxin Ding, Xinda Chen, W. Y. Sun
article en

Abstract

Generative AI is increasingly being applied in the industrial design field. However, the existing AI design tools are still challenging due to complex operations and parameter settings. To solve these problems, we conducted comprehensive interviews with industry experts and proposed the MIX system. The system emphasizes image prompts and is designed to support designers for more efficient task completion. MIX incorporated both the IP-Adapter and the ControlNet control model with multimodal language models, making more natural interaction possible. It enables designers to quickly produce high-quality design renderings from target reference images. Through qualitative and quantitative user experiments, we found that the MIX system performs well in both efficiency and practicality in design development. Finally, we discuss about when it would be more appropriate to use text prompts versus image prompts in these different design scenarios. Available at https://mix.drafff.net.

AHFE internationalVol. 170
Openalex Percentile: Top 87%
Image Retrieval and Classification Techniques
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MIX: An Image Generation System Using Image Prompts for Industrial Design — Longfei Zhou, Lionel M. Ni, et al. · AHFE international (2025) | TGRS Research Map | TGRS