Music2P: A Multi-Modal AI-Driven Tool for Simplifying Album Cover Design
Joong Ho Choi, Geonyeong Choi, Ji-Eun Han, Wonjin Yang, Zhi-Qi Cheng
TL;DR
Music2P addresses the barrier of accessibility and cost in AI-driven album cover design for independent artists by providing an open-source, multi-modal pipeline. It integrates BLIP for image captioning, LP-music-caps for music-to-text conversion, LoRA for adaptive segmentation, and ControlNet for controllable cover generation, with Ngrok enabling quick deployment. A QR-code generation feature is included to support promotional workflows. The work demonstrates end-to-end generation from multi-modal inputs and outlines practical deployment considerations, while noting future work on scalable infrastructure and expanded LoRA training to handle challenging inputs like faces or patterned objects.
Abstract
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.