Design with Generative AI

Creativity in the Era of Machine Intelligence
Erscheinungsjahr 2026
ISBN: 978-3-032-21309-9
Verlag: Springer

Buch, Englisch, 214 Seiten, Format (B × H): 155 mm x 235 mm

In an age when AI is rapidly reshaping creative practice, this book bridges the worlds of design and technology to reveal how artificial intelligence is transforming both the tools and materials of creativity. The book argues that understanding AI — its inner mechanisms, possibilities, and limitations — is now as essential for designers as understanding color or composition. Drawing parallels between historical shifts in materiality and today’s algorithmic frontier, it shows how machine learning can become a new medium for human expression rather than a substitute for it.

Written by experts fluent in both design thinking and technical innovation, moves beyond surface-level tutorials to offer a systematic, accessible framework for creative professionals seeking to harness generative AI with intention and control. Through clear explanations of foundational theories, case studies of leading techniques such as diffusion models, ControlNet, and LoRA, and practical demonstrations within real design contexts, the book provides the knowledge and confidence needed to engage with AI critically and creatively.

Ultimately,  is a guide to AI literacy for the creative community: a resource that encourages dialogue between designers and technologists, fosters trust in AI as a partner in innovation, and inspires new approaches to visual creation rooted in both technological depth and artistic vision.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Li, Lezhi

Kim, Dongyun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Sozialwissenschaften Medien- und Kommunikationswissenschaften Kommunikationswissenschaften Digitale Medien, Internet, Telekommunikation
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Geisteswissenschaften Design Produktdesign, Industriedesign

Inhaltsverzeichnis

Introduction.- Part 1: Exploring Existing: Generative AI Tools.- Chapter 1: Generative AI tooling landscape.- Chapter 2: Dall-E and Midjourney: online image generation tools.- Chapter 3: Stable Diffusion: open source generative model.- Chapter 4: Other alternatives Introduction to alternative genAI tools in this space, such as Photoshop GenAI plugin, lookX.ai, NewArc.ai, PromeAi etc.- Chapter 5: Beyond out-of-box solutions: This chapter discusses the limitation of out-of-box generative AI solutions, and motivates Part 2 where customization techniques are introduced to solve these issues.- Part 2: Customizing Generative AI Models for Design.- Chapter 6: Model fine-tuning basics using pytorch.- Chapter 7: ControlNet: controlled image editing.- Chapter 8: LoRA for redefining semantics.- Chapter 9: InstructPix2Pix for precisely targeted image editing.- Chapter 10: Other technologies.

Über Autor(innen)

Lezhi Li is a machine learning researcher working on large language models and large image generation models. Her career path spans multiple top-tier companies in Silicon Valley. The unconventional start of her AI career began at Harvard Graduate School of Design, where she trained models to evaluate the aesthetics of cityscapes -- among the earliest efforts at the school to integrate AI into design research. From multidisciplinary design training to advanced work in foundation models, her career reflects a sustained convergence of creativity and scientific analytical thinking. As AI increasingly serves as a computational medium for human thinking, reasoning, and aesthetic expression, her work on large-scale foundation models sits at the intersection where these expressions become efficient and precise, while remaining original and meaningfully human.

Dongyun Kim is a creative technologist and AI engineer working at the intersection of generative AI, design, and technology. His research and professional interests focus on identifying latent structures within complex phenomena and organizing them into systematic frameworks. In the context of AI, his work examines how AI models encode and interpret the world, and how real-world conditions can be represented, abstracted, and reconstructed through computational systems.
He received formal training in design and technology at Harvard Graduate School of Design and the University of Pennsylvania, supported by the Korean Government Scholarship for Overseas Study. His career experience spans architecture, Silicon Valley AI startups, and global finance, reflecting a sustained engagement with both experimental research and applied systems that inform his perspective on generative models as representational and analytical media.