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Editing Music with Melody and Text: Using ControlNet for Diffusion Transformer

Siyuan Hou, Shansong Liu, Ruibin Yuan, Wei Xue, Ying Shan, Mangsuo Zhao, Chao Zhang

TL;DR

A novel approach using a Diffusion Transformer augmented with an additional control branch using ControlNet allows for long-form and variable-length music generation and editing controlled by text and melody prompts, and introduces a novel top-k constant-Q Transform representation as the melody prompt.

Abstract

Despite the significant progress in controllable music generation and editing, challenges remain in the quality and length of generated music due to the use of Mel-spectrogram representations and UNet-based model structures. To address these limitations, we propose a novel approach using a Diffusion Transformer (DiT) augmented with an additional control branch using ControlNet. This allows for long-form and variable-length music generation and editing controlled by text and melody prompts. For more precise and fine-grained melody control, we introduce a novel top-$k$ constant-Q Transform representation as the melody prompt, reducing ambiguity compared to previous representations (e.g., chroma), particularly for music with multiple tracks or a wide range of pitch values. To effectively balance the control signals from text and melody prompts, we adopt a curriculum learning strategy that progressively masks the melody prompt, resulting in a more stable training process. Experiments have been performed on text-to-music generation and music-style transfer tasks using open-source instrumental recording data. The results demonstrate that by extending StableAudio, a pre-trained text-controlled DiT model, our approach enables superior melody-controlled editing while retaining good text-to-music generation performance. These results outperform a strong MusicGen baseline in terms of both text-based generation and melody preservation for editing. Audio examples can be found at https://stable-audio-control.github.io.

Editing Music with Melody and Text: Using ControlNet for Diffusion Transformer

TL;DR

A novel approach using a Diffusion Transformer augmented with an additional control branch using ControlNet allows for long-form and variable-length music generation and editing controlled by text and melody prompts, and introduces a novel top-k constant-Q Transform representation as the melody prompt.

Abstract

Despite the significant progress in controllable music generation and editing, challenges remain in the quality and length of generated music due to the use of Mel-spectrogram representations and UNet-based model structures. To address these limitations, we propose a novel approach using a Diffusion Transformer (DiT) augmented with an additional control branch using ControlNet. This allows for long-form and variable-length music generation and editing controlled by text and melody prompts. For more precise and fine-grained melody control, we introduce a novel top- constant-Q Transform representation as the melody prompt, reducing ambiguity compared to previous representations (e.g., chroma), particularly for music with multiple tracks or a wide range of pitch values. To effectively balance the control signals from text and melody prompts, we adopt a curriculum learning strategy that progressively masks the melody prompt, resulting in a more stable training process. Experiments have been performed on text-to-music generation and music-style transfer tasks using open-source instrumental recording data. The results demonstrate that by extending StableAudio, a pre-trained text-controlled DiT model, our approach enables superior melody-controlled editing while retaining good text-to-music generation performance. These results outperform a strong MusicGen baseline in terms of both text-based generation and melody preservation for editing. Audio examples can be found at https://stable-audio-control.github.io.
Paper Structure (17 sections, 3 equations, 1 figure, 2 tables)

This paper contains 17 sections, 3 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Diffusion Model
  4. Diffusion Transformer (DiT)
  5. Proposed Method
  6. Diffusion Transformer with ControlNet
  7. Top-k CQT Representation for Melody Information
  8. Progressive Curriculum Masking Strategy
  9. Experimental Setup
  10. Data
  11. Model
  12. Evaluation
  13. Experimental Results
  14. Objective Results
  15. Subjective Results
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Overview of our Music Editing Model: (a) Melody prompt processing pipeline. The top-$k$ CQT of the stereo audio is computed, and the prominent components are selected to form the melody prompt, which can also be manually composed. The latent melody prompt is then derived through further processing using an embedding layer and 1D convolutions. (b) The architecture of our model. The model primarily consists of DiT and ControlNet. Various conditioning inputs are supplied via two methods: prepending and cross-attention. During training, only the ControlNet and the structure responsible for extracting melody prompts are fine-tuned. In the figure, indicates frozen components, while indicates components that are finetuned.