Abstract
This paper explores a simple extension of diffusion-based rectified flow Transformers for text-to-music generation, termed as FluxMusic. Generally, along with design in advanced Flux\footnote{https://github.com/black-forest-labs/flux} model, we transfers it into a latent VAE space of mel-spectrum. It involves first applying a sequence of independent attention to the double text-music stream, followed by a stacked single music stream for denoised patch prediction. We employ multiple pre-trained text encoders to sufficiently capture caption semantic information as well as inference flexibility. In between, coarse textual information, in conjunction with time step embeddings, is utilized in a modulation mechanism, while fine-grained textual details are concatenated with the music patch sequence as inputs. Through an in-depth study, we demonstrate that rectified flow training with an optimized architecture significantly outperforms established diffusion methods for the text-to-music task, as evidenced by various automatic metrics and human preference evaluations. Our experimental data, code, and model weights are made publicly available at: \url{https://github.com/feizc/FluxMusic}.
Overview
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FluxMusic integrates rectified flow Transformers within noise-predictive diffusion models to generate music from textual descriptions, leveraging the latent VAE space of mel-spectrograms for enhanced audio fidelity.
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The architecture employs Transformer models with rectified flow training, using a sequence of attention layers and independent text and music streams to predict denoised musical patches, improving semantic understanding and generation quality.
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Extensive evaluations demonstrate FluxMusic's superior performance compared to existing models, highlighting the efficiency and effectiveness of rectified flow pathways and confirming scalability across various model sizes.
FluxMusic: An Exploration in Text-to-Music Generation
Overview
FluxMusic represents an integration of rectified flow Transformers within noise-predictive diffusion models for text-to-music generation. The core framework builds upon the design principles of the FLUX model, leveraging the latent VAE space of mel-spectrograms to produce higher fidelity audio outputs from textual descriptions. The methodological expansions emphasized in the paper illustrate substantial performance enhancements compared to conventional diffusion techniques, particularly in terms of efficiency and generation quality.
Methodological Approach
The architecture of FluxMusic capitalizes on the strengths of Transformer models and rectified flow training. The broader framework can be dissected into several methodological components:
- Latent VAE Space: Music clips are initially converted into mel-spectrograms and subsequently compressed into a latent representation using a VAE. This preprocessing stage ensures the model operates within a manageable latent space, optimizing performance.
- Model Architecture: The primary innovation lies in cascading independent double streams of text and music information through a sequence of attention layers and then relying solely on a stacked music stream for denoised patch prediction. By integrating coarse and fine-grained textual details with musical inputs, the architecture significantly enhances semantic understanding and generation accuracy.
- Rectified Flow Training: The training regimen employs a rectified flow approach, connecting data and noise via a linear trajectory. This accelerates the training process and reduces the computational burdens typically associated with ODE solvers or diffusion-based methods.
Experimental Findings
Extensive evaluations were conducted to benchmark FluxMusic against existing models such as AudioLDM and MusicGen. The key findings from these experiments are notable:
- Performance Metrics: FluxMusic achieved superior performance across various objective metrics, including Fréchet Audio Distance (FAD) and Inception Score (IS). These metrics underscore the model's enhanced generative capabilities.
- Impact of Rectified Flow: The model's application of rectified flow trajectories notably outperformed traditional DDIM methods, showcasing the efficiency and effectiveness of this approach in high-dimensional data generation tasks.
- Scalability: The study explored different configurations of the model, scaling from small to giant versions. FluxMusic exhibited consistent improvements in generative performance with increasing model parameters and depth, which confirms its scalability and robustness.
Implications and Future Directions
The implications of this research extend across both practical and theoretical domains. On the practical side, FluxMusic promises a more efficient pathway for high-fidelity music generation from text descriptions, opening new avenues in multimedia content creation. Theoretically, it reinforces the application of rectified flows within diffusion frameworks, suggesting potential adaptations in other high-dimensional generative tasks.
Future research could focus on further scalability using mixture-of-experts models or distillation techniques aimed at improving inference efficiency. Additionally, exploring other forms of conditional generation with rectified flows could yield deeper insights into the versatility and limitations of these methods.
Conclusion
FluxMusic introduces a compelling approach to text-to-music generation, embellishing the rich latent space with Transformer-based rectified flow training. The strong experimental results position FluxMusic as a competitive framework in the landscape of generative models, setting a probable path for subsequent innovations in the field.
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