  This paper delves into the fascinating field of AI-generated art and explores
the various deep neural network architectures and models that have been
utilized to create it. From the classic convolutional networks to the
cutting-edge diffusion models, we examine the key players in the field. We
explain the general structures and working principles of these neural networks.
Then, we showcase examples of milestones, starting with the dreamy landscapes
of DeepDream and moving on to the most recent developments, including Stable
Diffusion and DALL-E 3, which produce mesmerizing images. We provide a detailed
comparison of these models, highlighting their strengths and limitations, and
examining the remarkable progress that deep neural networks have made so far in
a short period of time. With a unique blend of technical explanations and
insights into the current state of AI-generated art, this paper exemplifies how
art and computer science interact.
