Generative Futures: Creativity with Diffusion and GANs

Creativity has entered a new phase of computation. Generative Futures is a deep learning book devoted to the models reshaping creation—diffusion systems and generative adversarial networks.
The writing explores how machines learn to generate images, audio, video, and design by modeling probability and structure. Readers follow the evolution from adversarial training to diffusion-based refinement, understanding why different approaches excel at different creative tasks.
Rather than treating generation as spectacle, the book builds understanding. It explains how latent spaces form, how conditioning guides output, and how evaluation differs when novelty matters. Each chapter connects algorithms to creative control, quality, and ethics.
The tone is exploratory yet grounded, welcoming artists, engineers, and researchers alike. Language remains intuitive, balancing conceptual clarity with technical substance.
Generative Futures moves through generative principles, GAN dynamics, diffusion processes, control mechanisms, and societal impact—framing creativity as collaboration between human intent and learned models.
Key themes explored include:
• Generative modeling foundations
• Diffusion vs GANs
• Creative control and conditioning
• Evaluation of generative quality
• Ethics and authorship
Generative Futures is for creators and builders—offering insight into how generative models expand what creation can be.