Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation

Abstract

Recent advancements in diffusion models and large language models (LLMs) have significantly propelled the field of generation tasks. Text-to-Audio (TTA), a burgeoning generation application designed to generate audio from natural language prompts, is attracting increasing attention. However, existing TTA studies often struggle with generation quality and text-audio alignment, especially for complex textual inputs. Drawing inspiration from state-of-the-art Text-to-Image (T2I) diffusion models, we introduce Auffusion, a TTA system adapting T2I model frameworks to TTA task, by effectively leveraging their inherent generative strengths and precise cross-modal alignment. Our objective and subjective evaluations demonstrate that Auffusion surpasses previous TTA approaches using limited data and computational resources. Furthermore, the text encoder serves as a critical bridge between text and audio, since it acts as an instruction for the diffusion model to generate coherent content. Previous studies in T2I recognize the significant impact of encoder choice on cross-modal alignment, like fine-grained details and object bindings, while similar evaluation is lacking in prior TTA works. Through comprehensive ablation studies and innovative cross-attention map visualizations, we provide insightful assessments, being the first to reveal the internal mechanisms in the TTA field and intuitively explain how different text encoders influence the diffusion process. Our findings reveal Auffusion's superior capability in generating audios that accurately match textual descriptions, which is further demonstrated in several related tasks, such as audio style transfer, inpainting, and other manipulations.