In today's music industry, album cover design is as crucial as the music itself, reflecting the artist's vision and brand. However, many AI-driven album cover services require subscriptions or technical expertise, limiting accessibility. To address these challenges, we developed Music2P, an open-source, multi-modal AI-driven tool that streamlines album cover creation, making it efficient, accessible, and cost-effective through Ngrok. Music2P automates the design process using techniques such as Bootstrapping Language Image Pre-training (BLIP), music-to-text conversion (LP-music-caps), image segmentation (LoRA), and album cover and QR code generation (ControlNet). This paper demonstrates the Music2P interface, details our application of these technologies, and outlines future improvements. Our ultimate goal is to provide a tool that empowers musicians and producers, especially those with limited resources or expertise, to create compelling album covers.

Would seminal Beatles classic Abbey Road have been so memorable if it had been called Everest - and featured George Harrison smoking a cigarette in front of a snow-covered volcano on the cover instead of the Fab Four crossing the street in London? That is one of several questions posed by digital experts today - who have re-imagined how some of the world's most iconic album covers might have looked if they had been released under their original working titles. The study, from digital agency WMG, has used image generation technology instead of the names by which they are now known the world over. An AI bot has predicted what iconic album covers might have looked like if world-famous artists including The Beatles and Queen had plumped for the original record names. Queen's studio album The Miracle was released in 1989 and was named after a song included on the album tracklist Using the working titles of some of music's most legendary albums, SEO and digital marketing experts WMG used AI tool Midjourney to visualise what their covers could have looked like.

Sometimes we stumble upon insight in unexpected places. Late last year, for example, I read perhaps the most precise description ever written about AI-generated art in The New York Times comments section. The article described what happened when a man named Jason Allen submitted an image generated by the AI program Midjourney to an art contest and won. While the story focused on the debate over the ethics of AI image generators, the comment had nothing to do with thorny moral considerations. Instead, it described how the winning work looked.

Generative Adversarial Networks (GANs) were introduced by Goodfellow in 2014, and since then have become popular for constructing generative artificial intelligence models. However, the drawbacks of such networks are numerous, like their longer training times, their sensitivity to hyperparameter tuning, several types of loss and optimization functions and other difficulties like mode collapse. Current applications of GANs include generating photo-realistic human faces, animals and objects. However, I wanted to explore the artistic ability of GANs in more detail, by using existing models and learning from them. This dissertation covers the basics of neural networks and works its way up to the particular aspects of GANs, together with experimentation and modification of existing available models, from least complex to most. The intention is to see if state of the art GANs (specifically StyleGAN2) can generate album art covers and if it is possible to tailor them by genre. This was attempted by first familiarizing myself with 3 existing GANs architectures, including the state of the art StyleGAN2. The StyleGAN2 code was used to train a model with a dataset containing 80K album cover images, then used to style images by picking curated images and mixing their styles.

David Hockney, one of the world's most famous living artists, is also a proponent of digital art. Hockney would argue significant technological advances occurred in the 15th Century with the arrival of optical devices. Centring around the mid 15th Century a radical transformation in the visual quality of painting happened. What we would call photorealistic today replaced the stylised rendering of the likes of Giotto. An understanding of optics and lenses gave artists a new way to capture the reality that the eye could see.

Over 60,000 songs are released on Spotify every day, and the competition for the listener's attention is immense. In that regard, the importance of captivating and inviting cover art cannot be underestimated, because it is deeply entangled with a song's character and the artist's identity, and remains one of the most important gateways to lead people to discover music. However, designing cover art is a highly creative, lengthy and sometimes expensive process that can be daunting, especially for non-professional artists. For this reason, we propose a novel deep-learning framework to generate cover art guided by audio features. Inspired by VQGAN-CLIP, our approach is highly flexible because individual components can easily be replaced without the need for any retraining. This paper outlines the architectural details of our models and discusses the optimization challenges that emerge from them. More specifically, we will exploit genetic algorithms to overcome bad local minima and adversarial examples. We find that our framework can generate suitable cover art for most genres, and that the visual features adapt themselves to audio feature changes. Given these results, we believe that our framework paves the road for extensions and more advanced applications in audio-guided visual generation tasks.

On June 1st, 2022, I received an email: "We're excited to have you as an early tester in the Midjourney Beta!" A week has now passed in what feels like a couple of hours. The image above was generated as a result of me writing a sentence filled with various descriptors about a yellow tent and a nature-filled landscape. As an artist, I've been cautiously curious about the application of AI learning to the creative industry. After finally dipping my toes into this pool by generating some prompts of my own, I was ready to swim.