Diffusion models have achieved great success in image generation tasks
through iterative noise estimation. However, the heavy denoising process and
complex neural networks hinder their low-latency applications in real-world
scenarios. Quantization can effectively reduce model complexity, and
post-training quantization (PTQ), which does not require fine-tuning, is highly
promising in accelerating the denoising process. Unfortunately, we find that
due to the highly dynamic distribution of activations in different denoising
steps, existing PTQ methods for diffusion models suffer from distribution
mismatch issues at both calibration sample level and reconstruction output
level, which makes the performance far from satisfactory, especially in low-bit
cases. In this paper, we propose Enhanced Distribution Alignment for
Post-Training Quantization of Diffusion Models (EDA-DM) to address the above
issues. Specifically, at the calibration sample level, we select calibration
samples based on the density and diversity in the latent space, thus
facilitating the alignment of their distribution with the overall samples; and
at the reconstruction output level, we propose Fine-grained Block
Reconstruction, which can align the outputs of the quantized model and the
full-precision model at different network granularity. Extensive experiments
demonstrate that EDA-DM outperforms the existing post-training quantization
frameworks in both unconditional and conditional generation scenarios. At
low-bit precision, the quantized models with our method even outperform the
full-precision models on most datasets.

优化的分布对齐方法可用于增强扩散模型的后训练量化，从而解决现有方法中存在的分布不匹配问题，提高在噪声估计任务中的性能，并在低位精度情况下超过完整精度模型的性能。

增强的分布对齐用于后训练量化扩散模型

Enhanced Distribution Alignment for Post-Training Quantization of  Diffusion Models

Non-autoregressive (NAR) modeling has gained significant interest in speech
processing since these models achieve dramatically lower inference time than
autoregressive (AR) models while also achieving good transcription accuracy.
Since NAR automatic speech recognition (ASR) models must wait for the
completion of the entire utterance before processing, some works explore
streaming NAR models based on blockwise attention for low-latency applications.
However, streaming NAR models significantly lag in accuracy compared to
streaming AR and non-streaming NAR models. To address this, we propose a
streaming "semi-autoregressive" ASR model that incorporates the labels emitted
in previous blocks as additional context using a Language Model (LM)
subnetwork. We also introduce a novel greedy decoding algorithm that addresses
insertion and deletion errors near block boundaries while not significantly
increasing the inference time. Experiments show that our method outperforms the
existing streaming NAR model by 19% relative on Tedlium2, 16%/8% on
Librispeech-100 clean/other test sets, and 19%/8% on the Switchboard(SWB) /
Callhome(CH) test sets. It also reduced the accuracy gap with streaming AR and
non-streaming NAR models while achieving 2.5x lower latency. We also
demonstrate that our approach can effectively utilize external text data to
pre-train the LM subnetwork to further improve streaming ASR accuracy.

提出了一种流式的 “半自回归” ASR 模型，通过在上一个片段中引入先前块中发出的标签作为额外上下文，使用语言模型（LM）子网络，以及引入了一种新的贪婪解码算法来解决块边界附近的插入和删除错误，提高了语音处理的准确性和响应速度。