# Music2P: A Multi-Modal AI-Driven Tool for Simplifying Album Cover Design

arXiv: https://arxiv.org/abs/2408.01651 · HTML: https://mlsanity.com/paper/2408.01651/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [LP-MusicCaps: LLM-Based Pseudo Music Captioning](https://mlsanity.com/paper/2307.16372/) (2023)
- [Audio-guided Album Cover Art Generation with Genetic Algorithms](https://mlsanity.com/paper/2207.07162/) (2022)

## Abstract (arXiv)

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.