@inproceedings{huo-etal-2026-ditreducio,
    title = "{D}i{TR}educio: A Training-Free Acceleration for {D}i{T}-Based {TTS} via Progressive Calibration",
    author = "Huo, Yanru  and
      Jiang, Ziyue  and
      Tang, Zuoli  and
      Hong, Qingyang  and
      Zhao, Zhou",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1157/",
    pages = "23101--23116",
    ISBN = "979-8-89176-395-1",
    abstract = "While Diffusion Transformers (DiT) have advanced non-autoregressive (NAR) speech synthesis, their high computational demands remain an obvious limitation. Existing DiT-based text-to-speech (TTS) model acceleration approaches predominantly focus on reducing sampling steps through distillation techniques, yet they remain constrained by training costs. We introduce DiTReducio, a training-free acceleration framework that compresses computations in DiT-based TTS models through a progressive calibration process. We propose two compression methods, Temporal Skipping and Branch Skipping, to eliminate redundant computations during inference. Moreover, based on two characteristic attention patterns identified within DiT layers, we devise a pattern-guided strategy to selectively apply the compression methods. Our method allows flexible modulation between generation quality and computational efficiency through adjustable compression thresholds. Experimental evaluations conducted on F5-TTS and MegaTTS 3 demonstrate that DiTReducio achieves a 75.4{\%} reduction in FLOPs and improves the Real-Time Factor (RTF) by 37.1{\%}, while preserving generation quality. The code is available at https://github.com/MM-Speech/DiTReducio."
}