ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
Representation alignment (REPA) has been investigated to accelerate diffusion training, but we observe that regularizing intermediate representations in diffusion Transformers (DiT) may implicitly entangle latents and limit generative capacity. To address this issue, we propose ReGen, a hierarchical multi-prompt representation generation framework that jointly estimates multiple vector fields for both representations and data within a single diffusion model. We further introduce generalized flow
Record details
Published: 10 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (10 July 2026), “ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models,” evidence record 71, https://ethics.ai/record/71 (originally published by arXiv).
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