Music2P:简化专辑封面设计的多模式人工智能驱动工具

Joong Ho Choi, Geonyeong Choi, Ji-Eun Han, Wonjin Yang, Zhi-Qi Cheng
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引用次数: 0

摘要

在当今的音乐产业中,专辑封面设计与音乐本身一样至关重要,它反映了艺术家的理念和品牌。然而,许多人工智能驱动的专辑封面服务都需要订阅或专业技术知识,限制了用户的使用。为了应对这些挑战,我们开发了开源、多模式的人工智能驱动工具 Music2P,通过 Ngrok 简化专辑封面制作,使其高效、易用且经济实惠。Music2P利用引导语言图像预训练(BLIP)、音乐到文本的转换(LP-music-caps)、图像分割(LoRA)以及专辑封面和二维码生成(ControlNet)等技术自动完成设计过程。本文演示了 Music2P 界面,详细介绍了我们对这些技术的应用,并概述了未来的改进措施。我们的最终目标是提供一种工具,让音乐家和制作人,尤其是资源或专业知识有限的音乐家和制作人,能够制作出引人注目的专辑封面。
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Music2P: A Multi-Modal AI-Driven Tool for Simplifying Album Cover Design
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.
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