# Post-Training Quantization for Audio Diffusion Transformers

arXiv: https://arxiv.org/abs/2510.00313 · HTML: https://mlsanity.com/paper/2510.00313/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [All are Worth Words: A ViT Backbone for Diffusion Models](https://mlsanity.com/paper/2209.12152/) (2022)
- [DeepCache: Accelerating Diffusion Models for Free](https://mlsanity.com/paper/2312.00858/) (2023)
- [Q-Diffusion: Quantizing Diffusion Models](https://mlsanity.com/paper/2302.04304/) (2023)
- [Post-training Quantization on Diffusion Models](https://mlsanity.com/paper/2211.15736/) (2022)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [Fast Timing-Conditioned Latent Audio Diffusion](https://mlsanity.com/paper/2402.04825/) (2024)
- [Noise2Music: Text-conditioned Music Generation with Diffusion Models](https://mlsanity.com/paper/2302.03917/) (2023)
- [PTQD: Accurate Post-Training Quantization for Diffusion Models](https://mlsanity.com/paper/2305.10657/) (2023)
- [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](https://mlsanity.com/paper/2411.05007/) (2024)
- [PTQ4DiT: Post-training Quantization for Diffusion Transformers](https://mlsanity.com/paper/2405.16005/) (2024)
- [Towards Accurate Post-training Quantization for Diffusion Models](https://mlsanity.com/paper/2305.18723/) (2023)
- [Post-training Quantization for Neural Networks with Provable Guarantees](https://mlsanity.com/paper/2201.11113/) (2022)
- [Token Caching for Diffusion Transformer Acceleration](https://mlsanity.com/paper/2409.18523/) (2024)
- [Controllable Music Production with Diffusion Models and Guidance Gradients](https://mlsanity.com/paper/2311.00613/) (2023)
- [AudioX: A Unified Framework for Anything-to-Audio Generation](https://mlsanity.com/paper/2503.10522/) (2025)
- [Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitations](https://mlsanity.com/paper/2412.08550/) (2024)
- [EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis](https://mlsanity.com/paper/2311.08667/) (2023)
- [DiTAS: Quantizing Diffusion Transformers via Enhanced Activation Smoothing](https://mlsanity.com/paper/2409.07756/) (2024)
- [ImmerseDiffusion: A Generative Spatial Audio Latent Diffusion Model](https://mlsanity.com/paper/2410.14945/) (2024)
- [DEX-TTS: Diffusion-based EXpressive Text-to-Speech with Style Modeling on Time Variability](https://mlsanity.com/paper/2406.19135/) (2024)
- [PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models](https://mlsanity.com/paper/2409.13894/) (2024)
- [Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping](https://mlsanity.com/paper/2503.06930/) (2025)

## Built on by (most cited first, 3 of 3)

- [Quantizing Whisper-small: How design choices affect ASR performance](https://mlsanity.com/paper/2511.08093/) (2025)
- [A Quantized Native Runtime for On-Device Semantic Audio Generation](https://mlsanity.com/paper/2607.08526/) (2026)
- [Same Bit Width, Different Outcomes: Post-Training Quantization of Text-to-Speech Across Architectures](https://mlsanity.com/paper/2609.28974/) (2026)

## Abstract (arXiv)

Diffusion Transformers (DiTs) enable high-quality audio synthesis but are often computationally intensive and require substantial storage, which limits their practical deployment. In this paper, we present a comprehensive evaluation of post-training quantization (PTQ) techniques for audio DiTs, analyzing the trade-offs between static and dynamic quantization schemes. We explore two practical extensions (1) a denoising-timestep-aware smoothing method that adapts quantization scales per-input-channel and timestep to mitigate activation outliers, and (2) a lightweight low-rank adapter (LoRA)-based branch derived from singular value decomposition (SVD) to compensate for residual weight errors. Using Stable Audio Open we benchmark W8A8 and W4A8 configurations across objective metrics and human perceptual ratings. Our results show that dynamic quantization preserves fidelity even at lower precision, while static methods remain competitive with lower latency. Overall, our findings show that low-precision DiTs can retain high-fidelity generation while reducing memory usage by up to 79%.