ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models

2026-07-10 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Speech & Audio AI · Depth: Expert, quick

Summary

ReGen is a hierarchical multi-prompt representation generation framework designed to overcome limitations in diffusion Transformers (DiT) where intermediate representation regularization can entangle latents and restrict generative capacity. It jointly estimates multiple vector fields for both representations and data within a single diffusion model, enhancing conditional flow matching (CFM) through generalized flow matching (GFM). Validated on single-stage waveform diffusion models like neural audio codec and Wave-VAE, ReGen significantly improves waveform generation quality from highly compressed latent representations at 12.5 Hz. Furthermore, ReGenVoice, a latent diffusion model (LDM)-based text-to-speech model, demonstrates strong speech intelligibility and speaker similarity using a small dataset. This LDM operates efficiently at 6.25 Hz, requiring only 1 day of training on 4 GPUs and achieving fast inference with an RTF of 0.08.

Key takeaway

For Machine Learning Engineers developing efficient generative models, ReGen offers a promising approach to enhance diffusion model performance. You should consider its hierarchical multi-prompt representation generation and generalized flow matching techniques to improve latent disentanglement and generation quality. This method enables efficient training, demonstrated by ReGenVoice's 1-day training on 4 GPUs and fast 0.08 RTF inference, making it suitable for resource-constrained text-to-speech or waveform synthesis projects.

Key insights

ReGen improves diffusion model efficiency and quality by disentangling latents through hierarchical multi-prompt representation generation and generalized flow matching.

Principles

• Hierarchical multi-prompt generation can disentangle latents.
• Jointly estimate vector fields for representations and data.
• Generalized flow matching enhances conditional flow matching.

Method

ReGen jointly estimates multiple vector fields for representations and data within a single diffusion model, employing hierarchical multi-prompt generation and generalized flow matching to improve conditional flow matching.

In practice

• Improve waveform generation quality.
• Develop efficient text-to-speech models.
• Accelerate training and inference for LDMs.

Topics

• Diffusion Models
• Latent Diffusion Models
• Flow Matching
• Representation Learning
• Text-to-Speech
• Waveform Generation
• Model Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.